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ICLR.cc/2019/Conference | Pooling Is Neither Necessary nor Sufficient for Appropriate Deformation Stability in CNNs | Many of our core assumptions about how neural networks operate remain empirically untested. One common assumption is that convolutional neural networks need to be stable to small translations and deformations to solve image recognition tasks. For many years, this stability was baked into CNN architectures by incorporating interleaved pooling layers. Recently, however, interleaved pooling has largely been abandoned. This raises a number of questions: Are our intuitions about deformation stability right at all? Is it important? Is pooling necessary for deformation invariance? If not, how is deformation invariance achieved in its absence? In this work, we rigorously test these questions, and find that deformation stability in convolutional networks is more nuanced than it first appears: (1) Deformation invariance is not a binary property, but rather that different tasks require different degrees of deformation stability at different layers. (2) Deformation stability is not a fixed property of a network and is heavily adjusted over the course of training, largely through the smoothness of the convolutional filters. (3) Interleaved pooling layers are neither necessary nor sufficient for achieving the optimal form of deformation stability for natural image classification. (4) Pooling confers \emph{too much} deformation stability for image classification at initialization, and during training, networks have to learn to \emph{counteract} this inductive bias. Together, these findings provide new insights into the role of interleaved pooling and deformation invariance in CNNs, and demonstrate the importance of rigorous empirical testing of even our most basic assumptions about the working of neural networks. | Reject |
ICLR.cc/2022/Conference | Online Hyperparameter Meta-Learning with Hypergradient Distillation | Many gradient-based meta-learning methods assume a set of parameters that do not participate in inner-optimization, which can be considered as hyperparameters. Although such hyperparameters can be optimized using the existing gradient-based hyperparameter optimization (HO) methods, they suffer from the following issues. Unrolled differentiation methods do not scale well to high-dimensional hyperparameters or horizon length, Implicit Function Theorem (IFT) based methods are restrictive for online optimization, and short horizon approximations suffer from short horizon bias. In this work, we propose a novel HO method that can overcome these limitations, by approximating the second-order term with knowledge distillation. Specifically, we parameterize a single Jacobian-vector product (JVP) for each HO step and minimize the distance from the true second-order term. Our method allows online optimization and also is scalable to the hyperparameter dimension and the horizon length. We demonstrate the effectiveness of our method on three different meta-learning methods and two benchmark datasets. | Accept (Spotlight) |
ICLR.cc/2023/Conference | Improving the Estimation of Instance-dependent Transition Matrix by using Self-supervised Learning | The transition matrix reveals the transition relationship between clean labels and noisy labels. It plays an important role in building statistically consistent classifiers. In real-world applications, the transition matrix is usually unknown and has to be estimated. It is a challenging task to accurately estimate the transition matrix, especially when it depends on the instance. Given that both instances and noisy labels are available, the major difficulty of learning the transition matrix comes from the missing of clean information. A lot of methods have been proposed to infer clean information. The self-supervised learning has demonstrated great success. These methods could even achieve comparable performance with supervised learning on some datasets but without requiring any labels during the training. It implies that these methods can efficiently infer clean labels.
Motivated by this, in this paper, we have proposed a practical method that leverages self-supervised learning to help learn the instance-dependent transition matrix. Empirically, the proposed method has achieved state-of-the-art performance on different datasets. | Reject |
ICLR.cc/2021/Conference | Variational Information Bottleneck for Effective Low-Resource Fine-Tuning | While large-scale pretrained language models have obtained impressive results when fine-tuned on a wide variety of tasks, they still often suffer from overfitting in low-resource scenarios. Since such models are general-purpose feature extractors, many of these features are inevitably irrelevant for a given target task. We propose to use Variational Information Bottleneck (VIB) to suppress irrelevant features when fine-tuning on low-resource target tasks, and show that our method successfully reduces overfitting. Moreover, we show that our VIB model finds sentence representations that are more robust to biases in natural language inference datasets, and thereby obtains better generalization to out-of-domain datasets. Evaluation on seven low-resource datasets in different tasks shows that our method significantly improves transfer learning in low-resource scenarios, surpassing prior work. Moreover, it improves generalization on 13 out of 15 out-of-domain natural language inference benchmarks. Our code is publicly available in https://github.com/rabeehk/vibert. | Accept (Poster) |
ICLR.cc/2018/Conference | A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks | We present a generalization bound for feedforward neural networks in terms of the product of the spectral norm of the layers and the Frobenius norm of the weights. The generalization bound is derived using a PAC-Bayes analysis. | Accept (Poster) |
ICLR.cc/2018/Conference | The Manifold Assumption and Defenses Against Adversarial Perturbations | In the adversarial-perturbation problem of neural networks, an adversary starts with a neural network model $F$ and a point $\bfx$ that $F$ classifies correctly, and applies a \emph{small perturbation} to $\bfx$ to produce another point $\bfx'$ that $F$ classifies \emph{incorrectly}. In this paper, we propose taking into account \emph{the inherent confidence information} produced by models when studying adversarial perturbations, where a natural measure of ``confidence'' is \|F(\bfx)\|_\infty$ (i.e. how confident $F$ is about its prediction?). Motivated by a thought experiment based on the manifold assumption, we propose a ``goodness property'' of models which states that \emph{confident regions of a good model should be well separated}. We give formalizations of this property and examine existing robust training objectives in view of them. Interestingly, we find that a recent objective by Madry et al. encourages training a model that satisfies well our formal version of the goodness property, but has a weak control of points that are wrong but with low confidence. However, if Madry et al.'s model is indeed a good solution to their objective, then good and bad points are now distinguishable and we can try to embed uncertain points back to the closest confident region to get (hopefully) correct predictions. We thus propose embedding objectives and algorithms, and perform an empirical study using this method. Our experimental results are encouraging: Madry et al.'s model wrapped with our embedding procedure achieves almost perfect success rate in defending against attacks that the base model fails on, while retaining good generalization behavior.
| Reject |
ICLR.cc/2022/Conference | Communicate Then Adapt: An Effective Decentralized Adaptive Method for Deep Training | Decentralized adaptive gradient methods, in which each node averages only with its neighbors, are critical to save communication and wall-clock training time in deep learning tasks. While different in concrete recursions, existing decentralized adaptive methods share the same algorithm structure: each node scales its gradient with information of the past squared gradients (which is referred to as the adaptive step) before or while it communicates with neighbors. In this paper, we identify the limitation of such adapt-then/while-communicate structure: it will make the developed algorithms highly sensitive to heterogeneous data distributions, and hence deviate their limiting points from the stationary solution. To overcome this limitation, we propose an effective decentralized adaptive method with a communicate-then-adapt structure, in which each node conducts the adaptive step after finishing the neighborhood communications. The new method is theoretically guaranteed to approach to the stationary solution in the non-convex scenario. Experimental results on a variety of CV/NLP tasks show that our method has a clear superiority to other existing decentralized adaptive methods. | Reject |
ICLR.cc/2022/Conference | Did I do that? Blame as a means to identify controlled effects in reinforcement learning | Identifying controllable aspects of the environment has proven to be an extraordinary intrinsic motivator to reinforcement learning agents. Despite repeatedly achieving State-of-the-Art results, this approach has only been studied as a proxy to a reward-based task and has not yet been evaluated on its own. We show that solutions relying on action-prediction fail to model critical controlled events. Humans, on the other hand, assign blame to their actions to decide what they controlled. This work proposes Controlled Effect Network (CEN), an unsupervised method based on counterfactual measures of blame to identify effects on the environment controlled by the agent. CEN is evaluated in a wide range of environments showing that it can accurately identify controlled effects. Moreover, we demonstrate CEN's capabilities as intrinsic motivator by integrating it in the state-of-the-art exploration method, achieving substantially better performance than action-prediction models. | Reject |
ICLR.cc/2021/Conference | Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time | We study the problem of learning Bayesian networks where an $\epsilon$-fraction of the samples are adversarially corrupted. We focus on the fully-observable case where the underlying graph structure is known. In this work, we present the first nearly-linear time algorithm for this problem with a dimension-independent error guarantee. Previous robust algorithms with comparable error guarantees are slower by at least a factor of $(d/\epsilon)$, where $d$ is the number of variables in the Bayesian network and $\epsilon$ is the fraction of corrupted samples.
Our algorithm and analysis are considerably simpler than those in previous work. We achieve this by establishing a direct connection between robust learning of Bayesian networks and robust mean estimation. As a subroutine in our algorithm, we develop a robust mean estimation algorithm whose runtime is nearly-linear in the number of nonzeros in the input samples, which may be of independent interest. | Accept (Poster) |
ICLR.cc/2022/Conference | Mistake-driven Image Classification with FastGAN and SpinalNet | Image classification with classes of varying difficulty can cause performance disparity in deep learning models and reduce the overall performance and reliability of the predictions. In this paper, we address the problem of imbalanced performance in image classification, where the trained model has performance deficits in some of the dataset's classes. By employing Generative Adversarial Networks (GANs) to augment these deficit classes, we finetune the model towards a balanced performance among the different classes and an overall better performance on the whole dataset. Specifically, we combine a light-weight GAN method, FastGAN (Liu et al., 2021), for class-wise data augmentation with Progressive SpinalNet (Chopra, 2021) and Sharpness-Aware Minimization (SAM) (Foret et al., 2020) for training. Unlike earlier works, during training, our method focuses on those classes with lowest accuracy after the initial training phase, which leads to better performance. Only these classes are augmented to boost the accuracy. Due to the use of a light-weight GAN method, the GAN-based augmentation is viable and effective for mistake-driven training even for datasets with only few images per class, while simultaneously requiring less computation than other, more complex GAN methods. Our extensive experiments, including ablation studies on all key components, show competitive or better accuracy than the previous state-of-the-art on five datasets with different sizes and image resolutions. | Reject |
ICLR.cc/2022/Conference | Pseudo Numerical Methods for Diffusion Models on Manifolds | Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quality samples such as image and audio samples. However, DDPMs require hundreds to thousands of iterations to produce a sample. Several prior works have successfully accelerated DDPMs through adjusting the variance schedule (e.g., Improved Denoising Diffusion Probabilistic Models) or the denoising equation (e.g., Denoising Diffusion Implicit Models (DDIMs)). However, these acceleration methods cannot maintain the quality of samples and even introduce new noise at high speedup rate, which limit their practicability. To accelerate the inference process while keeping the sample quality, we provide a new perspective that DDPMs should be treated as solving differential equations on manifolds. Under such a perspective, we propose pseudo numerical methods for diffusion models (PNDMs). Specifically, we figure out how to solve differential equations on manifolds and show that DDIMs are simple cases of pseudo numerical methods. We change several classical numerical methods to corresponding pseudo numerical methods and find that pseudo linear multi-step method is the best method in most situations. According to our experiments, by directly using pre-trained models on Cifar10, CelebA and LSUN, PNDMs can generate higher quality synthetic images with only 50 steps compared with 1000-step DDIMs (20x speedup), significantly outperform DDIMs with 250 steps (by around 0.4 in FID) and have good generalization on different variance schedules. | Accept (Poster) |
ICLR.cc/2023/Conference | VIMA: General Robot Manipulation with Multimodal Prompts | Prompt-based learning has emerged as a successful paradigm in natural language processing, where a single general-purpose language model can be instructed to perform any task specified by input prompts. Yet task specification in robotics comes in various forms, such as imitating one-shot demonstrations, following language instructions, and reaching visual goals. They are often considered different tasks and tackled by specialized models. This work shows that we can express a wide spectrum of robot manipulation tasks with *multimodal prompts*, interleaving textual and visual tokens. We design a transformer-based generalist robot agent, VIMA, that processes these prompts and outputs motor actions autoregressively. To train and evaluate VIMA, we develop a new simulation benchmark with thousands of procedurally-generated tabletop tasks with multimodal prompts, 600K+ expert trajectories for imitation learning, and four levels of evaluation protocol for systematic generalization. VIMA achieves strong scalability in both model capacity and data size. It outperforms prior SOTA methods in the hardest zero-shot generalization setting by up to 2.9x task success rate given the same training data. With 10x less training data, VIMA still performs 2.7x better than the top competing approach. Video demos are available at https://iclr3081.github.io/. | Reject |
ICLR.cc/2023/Conference | HiViT: A Simpler and More Efficient Design of Hierarchical Vision Transformer | There has been a debate on the choice of plain vs. hierarchical vision transformers, where researchers often believe that the former (e.g., ViT) has a simpler design but the latter (e.g., Swin) enjoys higher recognition accuracy. Recently, the emerge of masked image modeling (MIM), a self-supervised visual pre-training method, raised a new challenge to vision transformers in terms of flexibility, i.e., part of image patches or tokens are to be discarded, which seems to claim the advantages of plain vision transformers. In this paper, we delve deep into the comparison between ViT and Swin, revealing that (i) the performance gain of Swin is mainly brought by a deepened backbone and relative positional encoding, (ii) the hierarchical design of Swin can be simplified into hierarchical patch embedding (proposed in this work), and (iii) other designs such as shifted-window attentions can be removed. By removing the unnecessary operations, we come up with a new architecture named HiViT (short for hierarchical ViT), which is simpler and more efficient than Swin yet further improves its performance on fully-supervised and self-supervised visual representation learning. In particular, after pre-trained using masked autoencoder (MAE) on ImageNet-1K, HiViT-B reports a 84.6% accuracy on ImageNet-1K classification, a 53.3% box AP on COCO detection, and a 52.8% mIoU on ADE20K segmentation, significantly surpassing the baseline. Code is available at https://github.com/zhangxiaosong18/hivit. | Accept: notable-top-25% |
ICLR.cc/2022/Conference | NAFS: A Simple yet Tough-to-Beat Baseline for Graph Representation Learning | Recently, graph neural networks (GNNs) have shown prominent performance in graph representation learning by leveraging knowledge from both graph structure and node features. However, most of them have two major limitations. First, GNNs can learn higher-order structural information by stacking more layers but can not deal with large depth due to the over-smoothing issue. Second, it is not easy to apply these methods on large graphs due to the expensive computation cost and high memory usage. In this paper, we present node-adaptive feature smoothing (NAFS), a simple non-parametric method that constructs node representations without parameter learning. NAFS first extracts the features of each node with its neighbors of different hops by feature smoothing, and then adaptively combines the smoothed features. Besides, the constructed node representation can further be enhanced by the ensemble of smoothed features extracted via different smoothing strategies. We conduct experiments on four benchmark datasets on two different application scenarios: node clustering and link prediction. Remarkably, NAFS with feature ensemble outperforms the state-of-the-art GNNs on these tasks and mitigates the aforementioned two limitations of most learning-based GNN counterparts. | Reject |
ICLR.cc/2023/Conference | Improved Convergence of Differential Private SGD with Gradient Clipping | Differential private stochastic gradient descent (DP-SGD) with gradient clipping (DP-SGD-GC) is an effective optimization algorithm that can train machine learning models with a privacy guarantee. Despite the popularity of DP-SGD-GC, its convergence in unbounded domain without the Lipschitz continuous assumption is less-understood; existing analysis of DP-SGD-GC either impose additional assumptions or end up with an utility bound that involves an non-vanishing bias term. In this work, for smooth and unconstrained problems, we improve the current analysis and show that DP-SGD-GC can achieve a vanishing utility bound without any bias term. Furthermore, when the noise generated from subsampled gradients is light-tailed, we prove that DP-SGD-GC can achieve nearly the same utility bound as DP-SGD applies to the Lipschitz continuous objectives. As a by-product, we propose a new clipping technique, called value clipping, to mitigate the computational overhead caused by the classic gradient clipping. Experiments on standard benchmark datasets are conducted to support our analysis. | Accept: poster |
ICLR.cc/2022/Conference | Evaluating the Robustness of Time Series Anomaly and Intrusion Detection Methods against Adversarial Attacks | Time series anomaly and intrusion detection are extensively studied in statistics, economics, and computer science. Over the years, numerous methods have been proposed for time series anomaly and intrusion detection using deep learning-based methods. Many of these methods demonstrate state-of-the-art performance on benchmark datasets, giving the false impression that these systems are robust and deployable in practical and industrial scenarios. In this paper, we demonstrate that state-of-the-art anomaly and intrusion detection methods can be easily fooled by adding adversarial perturbations to the sensor data. We use different scoring metrics such as prediction errors, anomaly, and classification scores over several public and private datasets belong to aerospace applications, automobiles, server machines, and cyber-physical systems. We evaluate state-of-the-art deep neural networks (DNNs) and graph neural networks (GNNs) methods, which claim to be robust against anomalies and intrusions, and find their performance can drop to as low as 0\% under adversarial attacks from Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) methods. To the best of our knowledge, we are the first to demonstrate the vulnerabilities of anomaly and intrusion detection systems against adversarial attacks. Our code is available here: https://anonymous.4open.science/r/ICLR298 | Reject |
ICLR.cc/2023/Conference | The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation | Crossmodal knowledge distillation (KD) extends traditional knowledge distillation to the area of multimodal learning and demonstrates great success in various applications. To achieve knowledge transfer across modalities, a pretrained network from one modality is adopted as the teacher to provide supervision signals to a student network learning from the other modality. In contrast to the empirical success reported in prior works, the working mechanism of crossmodal KD remains a mystery. In this paper, we present a thorough understanding of crossmodal KD. We begin by providing two failure cases and demonstrate that KD is not a universal cure in crossmodal knowledge transfer. We then present the modality Venn diagram to understand modality relationships and the modality focusing hypothesis revealing the decisive factor in the efficacy of crossmodal KD. Experimental results on 6 multimodal datasets help justify our hypothesis, diagnose failure cases, and point directions to improve crossmodal knowledge transfer in the future. | Accept: notable-top-5% |
ICLR.cc/2020/Conference | Action Semantics Network: Considering the Effects of Actions in Multiagent Systems | In multiagent systems (MASs), each agent makes individual decisions but all of them contribute globally to the system evolution. Learning in MASs is difficult since each agent's selection of actions must take place in the presence of other co-learning agents. Moreover, the environmental stochasticity and uncertainties increase exponentially with the increase in the number of agents. Previous works borrow various multiagent coordination mechanisms into deep learning architecture to facilitate multiagent coordination. However, none of them explicitly consider action semantics between agents that different actions have different influences on other agents. In this paper, we propose a novel network architecture, named Action Semantics Network (ASN), that explicitly represents such action semantics between agents. ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them. ASN can be easily combined with existing deep reinforcement learning (DRL) algorithms to boost their performance. Experimental results on StarCraft II micromanagement and Neural MMO show ASN significantly improves the performance of state-of-the-art DRL approaches compared with several network architectures. | Accept (Poster) |
ICLR.cc/2022/Conference | SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations | Guided image synthesis enables everyday users to create and edit photo-realistic images with minimum effort. The key challenge is balancing faithfulness to the user inputs (e.g., hand-drawn colored strokes) and realism of the synthesized images. Existing GAN-based methods attempt to achieve such balance using either conditional GANs or GAN inversions, which are challenging and often require additional training data or loss functions for individual applications. To address these issues, we introduce a new image synthesis and editing method, Stochastic Differential Editing (SDEdit), based on a diffusion model generative prior, which synthesizes realistic images by iteratively denoising through a stochastic differential equation (SDE). Given an input image with user guide in a form of manipulating RGB pixels, SDEdit first adds noise to the input, then subsequently denoises the resulting image through the SDE prior to increase its realism. SDEdit does not require task-specific training or inversions and can naturally achieve the balance between realism and faithfulness. SDEdit outperforms state-of-the-art GAN-based methods by up to 98.09% on realism and 91.72% on overall satisfaction scores, according to a human perception study, on multiple tasks, including stroke-based image synthesis and editing as well as image compositing. | Accept (Poster) |
ICLR.cc/2022/Conference | There are free lunches | No-Free-Lunch Theorems state that the performance of all algorithms is the same when averaged over all possible tasks. It has been argued that the necessary conditions for NFL are too restrictive to be found in practice. There must be some information for a set of tasks that ensures some algorithms perform better than others. In this paper we propose a novel idea, "There are free lunches" (TAFL) Theorem, which states that some algorithms can achieve the best performance in all possible tasks, in the condition that tasks are given in a specific order. Furthermore, we point out that with the number of solved tasks increasing, the difficulty of solving a new task decreases. We also present an example to explain how to combine the proposed theorem and the existing supervised learning algorithms. | Reject |
ICLR.cc/2023/Conference | Explicitly Minimizing the Blur Error of Variational Autoencoders | Variational autoencoders (VAEs) are powerful generative modelling methods, however they suffer from blurry generated samples and reconstructions compared to the images they have been trained on. Significant research effort has been spent to increase the generative capabilities by creating more flexible models but often flexibility comes at the cost of higher complexity and computational cost. Several works have focused on altering the reconstruction term of the evidence lower bound (ELBO), however, often at the expense of losing the mathematical link to maximizing the likelihood of the samples under the modeled distribution. Here we propose a new formulation of the reconstruction term for the VAE that specifically penalizes the generation of blurry images while at the same time still maximizing the ELBO under the modeled distribution.
We show the potential of the proposed loss on three different data sets, where it outperforms several recently proposed reconstruction losses for VAEs. | Accept: poster |
ICLR.cc/2021/Conference | PDE-regularized Neural Networks for Image Classification | Neural ordinary differential equations (neural ODEs) introduced an approach to approximate a neural network as a system of ODEs after considering its layer as a continuous variable and discretizing its hidden dimension. While having several good characteristics, neural ODEs are known to be numerically unstable and slow in solving their integral problems, resulting in errors and/or much computation of the forward-pass inference. In this work, we present a novel partial differential equation (PDE)-based approach that removes the necessity of solving integral problems and considers both the layer and the hidden dimension as continuous variables. Owing to the recent advancement of learning PDEs, the presented novel concept, called PR-Net, can be implemented. Our method shows comparable (or better) accuracy and robustness in much shorter forward-pass inference time for various datasets and tasks in comparison with neural ODEs and Isometric MobileNet V3. For the efficient nature of PR-Net, it is suitable to be deployed in resource-scarce environments, e.g., deploying instead of MobileNet. | Reject |
ICLR.cc/2021/Conference | BBRefinement: an universal scheme to improve precision of box object detectors | We present a conceptually simple yet powerful and flexible scheme for refining predictions of bounding boxes. Our approach is trained standalone on GT boxes and can then be combined with an object detector to improve its predictions. The method, called BBRefinement, uses mixture data of image information and the object's class and center. Due to the transformation of the problem into a domain where BBRefinement does not care about multiscale detection, recognition of the object's class, computing confidence, or multiple detections, the training is much more effective. It results in the ability to refine even COCO's ground truth labels into a more precise form. BBRefinement improves the performance of SOTA architectures up to 2mAP points on the COCO dataset in the benchmark. The refinement process is fast; it adds 50-80ms overhead to a standard detector using RTX2080, so it can run in real-time on standard hardware. The code is available at https://gitlab.com/irafm-ai/bb-refinement. | Reject |
ICLR.cc/2022/Conference | BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models | Pre-trained Natural Language Processing (NLP) models, which can be adapted to a variety of downstream language tasks via fine-tuning, highly accelerate the learning progress of NLP models. However, NLP models have been shown to be vulnerable to backdoor attacks. Previous NLP backdoor attacks mainly focus on one specific task. This limitation makes existing solutions less applicable to different NLP models which have been widely used in various tasks.
In this work, we propose BadPre, the first backdoor attack against various downstream models built based on pre-trained NLP models. BadPre can launch trojan attacks against different language tasks with the same trigger.
The key insight of our approach is that downstream models can inherit the security characteristics from the pre-trained models. Specifically, we leverage data posing to the pre-trained NLP models and then inference the downstream models with sentences embedded triggers. Furthermore, to fool backdoor detectors, we design a novel adversarial attack method to generate a more robust trigger.
Experimental results indicate that our approach can effectively attack a wide range of downstream NLP tasks and exhibit significant robustness against backdoor detectors. | Accept (Poster) |
ICLR.cc/2021/Conference | Human Perception-based Evaluation Criterion for Ultra-high Resolution Cell Membrane Segmentation | Computer vision technology is widely used in biological and medical data analysis and understanding. However, there are still two major bottlenecks in the field of cell membrane segmentation, which seriously hinder further research: lack of sufficient high-quality data and lack of suitable evaluation criteria. In order to solve these two problems, this paper first introduces an Ultra-high Resolution Image Segmentation dataset for the Cell membrane, called U-RISC, the largest annotated EM dataset for the Cell membrane with multiple iterative annotations and uncompressed high-resolution raw data. During the analysis process of the U-RISC, we found that the current popular segmentation evaluation criteria are inconsistent with human perception. This interesting phenomenon is confirmed by a subjective experiment involving twenty people. Furthermore, to resolve this inconsistency, we propose a Perceptual Hausdorff Distance (PHD) evaluation criterion to measure the quality of cell membrane segmentation results. Detailed performance comparison and discussion of classic segmentation methods along with two iterative manual annotation results under existing criteria and PHD is given. | Reject |
ICLR.cc/2021/Conference | On The Adversarial Robustness of 3D Point Cloud Classification | 3D point clouds play pivotal roles in various safety-critical fields, such as autonomous driving, which desires the corresponding deep neural networks to be robust to adversarial perturbations. Though a few defenses against adversarial point cloud classification have been proposed, it remains unknown whether they can provide real robustness. To this end, we perform the first security analysis of state-of-the-art defenses and design adaptive attacks on them. Our 100% adaptive attack success rates demonstrate that current defense designs are still vulnerable. Since adversarial training (AT) is believed to be the most effective defense, we present the first in-depth study showing how AT behaves in point cloud classification and identify that the required symmetric function (pooling operation) is paramount to the model's robustness under AT. Through our systematic analysis, we find that the default used fixed pooling operations (e.g., MAX pooling) generally weaken AT's performance in point cloud classification. Still, sorting-based parametric pooling operations can significantly improve the models' robustness. Based on the above insights, we further propose DeepSym, a deep symmetric pooling operation, to architecturally advance the adversarial robustness under AT to 47.01% without sacrificing nominal accuracy, outperforming the original design and a strong baseline by 28.5% ($\sim 2.6 \times$) and 6.5%, respectively, in PointNet. | Reject |
ICLR.cc/2018/Conference | Learning Deep Mean Field Games for Modeling Large Population Behavior | We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space. A discrete time mean field game (MFG) is motivated as an interpretable model founded on game theory for understanding the aggregate effect of individual actions and predicting the temporal evolution of population distributions. We achieve a synthesis of MFG and Markov decision processes (MDP) by showing that a special MFG is reducible to an MDP. This enables us to broaden the scope of mean field game theory and infer MFG models of large real-world systems via deep inverse reinforcement learning. Our method learns both the reward function and forward dynamics of an MFG from real data, and we report the first empirical test of a mean field game model of a real-world social media population. | Accept (Oral) |
ICLR.cc/2023/Conference | Posterior Sampling Model-based Policy Optimization under Approximate Inference | Model-based reinforcement learning algorithms (MBRL) hold tremendous promise for improving the sample efficiency in online RL. However, many existing popular MBRL algorithms cannot deal with exploration and exploitation properly. Posterior sampling reinforcement learning (PSRL) serves as a promising approach for automatically trading off the exploration and exploitation, but the theoretical guarantees only hold under exact inference. In this paper, we show that adopting the same methodology as in exact PSRL can be fairly suboptimal under approximate inference. Motivated by the analysis, we propose an improved factorization for the posterior distribution of polices by removing the conditional independence between the policy and data given the model. By adopting such a posterior factorization, we further propose a general algorithmic framework for PSRL under approximate inference and a practical instantiation of it. Empirically, our algorithm can surpass the baseline methods by a significant margin on both dense rewards and sparse rewards tasks from DM control suite, OpenAI Gym and Metaworld benchmarks. | Reject |
ICLR.cc/2023/Conference | Pruning by Active Attention Manipulation | Structured pruning of a CNN is typically achieved by applying discrete masks on the CNN's filter weights or activation maps, post-training. Here, we present a new filter-importance-scoring concept named pruning by active attention manipulation (PAAM), that sparsifies the CNN's set of filters through a particular attention mechanism, during-training. PAAM learns continuous filter scores from the filter weights by optimizing a cost function regularized by an additive term in the scores. As the filters are not independent, we use attention to dynamically learn their correlations. Moreover, by training the pruning scores of all layers simultaneously, PAAM can account for layer inter-dependencies, which is essential to finding a performant sparse sub-network. PAAM can also train and generate a pruned network from scratch in a straightforward, one-stage training process without requiring a pre-trained network. Finally, PAAM does not need layer-specific hyperparameters and pre-defined layer budgets, since it can implicitly determine the appropriate number of filters in each layer. Our experimental results on different network architectures suggest that PAAM outperforms state-of-the-art structured-pruning methods (SOTA). On CIFAR-10 dataset, without requiring a pre-trained baseline network, we obtain 1.02% and 1.19% accuracy gain and 52.3% and 54% parameters reduction, on ResNet56 and ResNet110, respectively. Similarly, on the ImageNet dataset, PAAM achieves 1.06% accuracy gain while pruning 51.1% of the parameters on ResNet50. For Cifar-10, this is better than the SOTA with a margin of 9.5% and 6.6%, respectively, and on ImageNet with a margin of 11%. | Reject |
ICLR.cc/2020/Conference | Hamiltonian Generative Networks | The Hamiltonian formalism plays a central role in classical and quantum physics. Hamiltonians are the main tool for modelling the continuous time evolution of systems with conserved quantities, and they come equipped with many useful properties, like time reversibility and smooth interpolation in time. These properties are important for many machine learning problems - from sequence prediction to reinforcement learning and density modelling - but are not typically provided out of the box by standard tools such as recurrent neural networks. In this paper, we introduce the Hamiltonian Generative Network (HGN), the first approach capable of consistently learning Hamiltonian dynamics from high-dimensional observations (such as images) without restrictive domain assumptions. Once trained, we can use HGN to sample new trajectories, perform rollouts both forward and backward in time, and even speed up or slow down the learned dynamics. We demonstrate how a simple modification of the network architecture turns HGN into a powerful normalising flow model, called Neural Hamiltonian Flow (NHF), that uses Hamiltonian dynamics to model expressive densities. Hence, we hope that our work serves as a first practical demonstration of the value that the Hamiltonian formalism can bring to machine learning. More results and video evaluations are available at: http://tiny.cc/hgn | Accept (Spotlight) |
ICLR.cc/2022/Conference | A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes | Recently the LARS and LAMB optimizers have been proposed for training neural networks faster using large batch sizes. LARS and LAMB add layer-wise normalization to the update rules of Heavy-ball momentum and Adam, respectively, and have become popular in prominent benchmarks and deep learning libraries. However, without fair comparisons to standard optimizers, it remains an open question whether LARS and LAMB have any benefit over traditional, generic algorithms. In this work we demonstrate that standard optimization algorithms such as Nesterov momentum and Adam can match or exceed the results of LARS and LAMB at large batch sizes. Our results establish new, stronger baselines for future comparisons at these batch sizes and shed light on the difficulties of comparing optimizers for neural network training more generally. | Reject |
ICLR.cc/2021/Conference | PGPS : Coupling Policy Gradient with Population-based Search | Gradient-based policy search algorithms (such as PPO, SAC or TD3) in deep reinforcement learning (DRL) have shown successful results on a range of challenging control tasks. However, they often suffer from flat or deceptive gradient problems. As an alternative to policy gradient methods, population-based evolutionary approaches have been applied to DRL. While population-based search algorithms show more robust learning in a broader range of tasks, they are usually inefficient in the use of samples. Recently, reported are a few attempts (such as CEMRL) to combine gradient with a population in searching optimal policy. This kind of hybrid algorithm takes advantage of both camps. In this paper, we propose yet another hybrid algorithm, which more tightly couples policy gradient with the population-based search. More specifically, we use the Cross-Entropy Method (CEM) for population-based search and Twin Delayed Deep Deterministic Policy Gradient (TD3) for policy gradient. In the proposed algorithm called Coupling Policy Gradient with Population-based Search (PGPS), a single TD3 agent, which learns by a gradient from all experiences generated by population, leads a population by providing its critic function Q as a surrogate to select better performing next-generation population from candidates. On the other hand, if the TD3 agent falls behind the CEM population, then the TD3 agent is updated toward the elite member of the CEM population using loss function augmented with the distance between the TD3 and the CEM elite. Experiments in a MuJoCo environment show that PGPS is robust to deceptive gradient and also outperforms the state-of-the-art algorithms.
| Reject |
ICLR.cc/2018/Conference | DNN Representations as Codewords: Manipulating Statistical Properties via Penalty Regularization | Performance of Deep Neural Network (DNN) heavily depends on the characteristics of hidden layer representations. Unlike the codewords of channel coding, however, the representations of learning cannot be directly designed or controlled. Therefore, we develop a family of penalty regularizers where each one aims to affect one of representation's statistical properties such as sparsity, variance, or covariance. The regularizers are extended to perform class-wise regularization, and the extension is found to provide an outstanding shaping capability. A variety of statistical properties are investigated for 10 different regularization strategies including dropout and batch normalization, and several interesting findings are reported. Using the family of regularizers, performance improvements are confirmed for MNIST, CIFAR-100, and CIFAR-10 classification problems. But more importantly, our results suggest that understanding how to manipulate statistical properties of representations can be an important step toward understanding DNN and that the role and effect of DNN regularizers need to be reconsidered. | Reject |
ICLR.cc/2018/Conference | DENSELY CONNECTED RECURRENT NEURAL NETWORK FOR SEQUENCE-TO-SEQUENCE LEARNING | Deep neural networks based sequence-to-sequence learning has achieved remarkable progress in applications like machine translation and text summarization. However, sequence-to-sequence models suffer from severe inefficiency in training process, requiring huge amount of training time as well as memory usage. In this work, inspired by densely connected layers in modern convolutional neural network, we introduce densely connected sequence-to-sequence learning mechanism to tackle this challenge. In this mechanism, multiple layers of representations from stacked recurrent neural networks are concatenated to enhance feature reuse. Furthermore, a densely connected attention model is elaborately leveraged to improve information flow with more efficient parameter usage via multi-branch structure and local sparsity. We show that such a densely connected mechanism significantly reduces training time and memory usage for sequence-to-sequence learning. In particular, in WMT-14 English-French translation task with a subset of 12M training data, it takes half of training time and model parameters to achieve similar BLEU as typical stacked LSTM models. | Reject |
ICLR.cc/2022/Conference | LatentKeypointGAN: Controlling GANs via Latent Keypoints | Generative adversarial networks (GANs) have attained photo-realistic quality in image generation. However, how to best control the image content remains an open challenge. We introduce LatentKeypointGAN, a two-stage GAN which is trained end-to-end on the classical GAN objective with internal conditioning on a set of space keypoints. These keypoints have associated appearance embeddings that respectively control the position and style of the generated objects and their parts. A major difficulty that we address with suitable network architectures and training schemes is disentangling the image into spatial and appearance factors without domain knowledge and supervision signals. We demonstrate that LatentKeypointGAN provides an interpretable latent space that can be used to re-arrange the generated images by re-positioning and exchanging keypoint embeddings, such as generating portraits by combining the eyes, nose, and mouth from different images. In addition, the explicit generation of keypoints and matching images enables a new, GAN-based method for unsupervised keypoint detection. | Reject |
ICLR.cc/2020/Conference | TPO: TREE SEARCH POLICY OPTIMIZATION FOR CONTINUOUS ACTION SPACES | Monte Carlo Tree Search (MCTS) has achieved impressive results on a range of discrete environments, such as Go, Mario and Arcade games, but it has not yet fulfilled its true potential in continuous domains.In this work, we introduceTPO, a tree search based policy optimization method for continuous environments. TPO takes a hybrid approach to policy optimization. Building the MCTS tree in a continuous action space and updating the policy gradient using off-policy MCTS trajectories are non-trivial. To overcome these challenges, we propose limiting tree search branching factor by drawing only few action samples from the policy distribution and define a new loss function based on the trajectories’ mean and standard deviations. Our approach led to some non-intuitive findings. MCTS training generally requires a large number of samples and simulations. However, we observed that bootstrappingtree search with a pre-trained policy allows us to achieve high quality results with a low MCTS branching factor and few number of simulations. Without the proposed policy bootstrapping, continuous MCTS would require a much larger branching factor and simulation count, rendering it computationally and prohibitively expensive. In our experiments, we use PPO as our baseline policy optimization algorithm. TPO significantly improves the policy on nearly all of our benchmarks. For example, in complex environments such as Humanoid, we achieve a 2.5×improvement over the baseline algorithm. | Reject |
ICLR.cc/2023/Conference | Optimal Transport for Offline Imitation Learning | With the advent of large datasets, offline reinforcement learning is a promising framework for learning good decision-making policies without the need to interact with the real environment.
However, offline RL requires the dataset to be reward-annotated, which presents practical challenges when reward engineering is difficult or when obtaining reward annotations is labor-intensive.
In this paper, we introduce Optimal Transport Relabeling (OTR), an imitation learning algorithm that can automatically relabel offline data of mixed and unknown quality with rewards from a few good demonstrations. OTR's key idea is to use optimal transport to compute an optimal alignment between an unlabeled trajectory in the dataset and an expert demonstration to obtain a similarity measure that can be interpreted as a reward, which can then be used by an offline RL algorithm to learn the policy. OTR is easy to implement and computationally efficient. On D4RL benchmarks, we demonstrate that OTR with a single demonstration can consistently match the performance of offline RL with ground-truth rewards.
| Accept: notable-top-25% |
ICLR.cc/2020/Conference | Implementation Matters in Deep RL: A Case Study on PPO and TRPO | We study the roots of algorithmic progress in deep policy gradient algorithms through a case study on two popular algorithms: Proximal Policy Optimization (PPO) and Trust Region Policy Optimization (TRPO). Specifically, we investigate the consequences of "code-level optimizations:" algorithm augmentations found only in implementations or described as auxiliary details to the core algorithm. Seemingly of secondary importance, such optimizations turn out to have a major impact on agent behavior. Our results show that they (a) are responsible for most of PPO's gain in cumulative reward over TRPO, and (b) fundamentally change how RL methods function. These insights show the difficulty, and importance, of attributing performance gains in deep reinforcement learning.
| Accept (Talk) |
ICLR.cc/2021/Conference | Self-Activating Neural Ensembles for Continual Reinforcement Learning | The ability for an agent to continuously learn new skills without catastrophically forgetting existing knowledge is of critical importance for the development of generally intelligent agents. Most methods devised to address this problem depend heavily on well-defined task boundaries which simplify the problem considerably. Our task-agnostic method, Self-Activating Neural Ensembles (SANE), uses a hierarchical modular architecture designed to avoid catastrophic forgetting without making any such assumptions. At each timestep a path through the SANE tree is activated; during training only activated nodes are updated, ensuring that unused nodes do not undergo catastrophic forgetting. Additionally, new nodes are created as needed, allowing the system to leverage and retain old skills while growing and learning new ones. We demonstrate our approach on MNIST and a set of grid world environments, demonstrating that SANE does not undergo catastrophic forgetting where existing methods do. | Reject |
ICLR.cc/2022/Conference | Learning to Give Checkable Answers with Prover-Verifier Games | Our ability to know when to trust the decisions made by machine learning systems has not kept up with the staggering improvements in their performance, limiting their applicability in high-stakes applications. We propose Prover-Verifier Games (PVGs), a game-theoretic framework to encourage neural networks to solve decision problems in a verifiable manner. The PVG consists of two learners with competing objectives: a trusted verifier network tries to choose the correct answer, and a more powerful but untrusted prover network attempts to persuade the verifier of a particular answer, regardless of its correctness. The goal is for a reliable justification protocol to emerge from this game. We analyze several variants of the basic framework, including both simultaneous and sequential games, and narrow the space down to a subset of games which provably have the desired equilibria. We then develop practical instantiations of the PVG for several algorithmic tasks, and show that in practice, the verifier is able to receive useful and reliable information from an untrusted prover. Importantly, the protocol still works even when the verifier is frozen and the prover's message is directly optimized to convince the verifier. | Reject |
ICLR.cc/2018/Conference | Learning Parsimonious Deep Feed-forward Networks | Convolutional neural networks and recurrent neural networks are designed with network structures well suited to the nature of spacial and sequential data respectively. However, the structure of standard feed-forward neural networks (FNNs) is simply a stack of fully connected layers, regardless of the feature correlations in data. In addition, the number of layers and the number of neurons are manually tuned on validation data, which is time-consuming and may lead to suboptimal networks. In this paper, we propose an unsupervised structure learning method for learning parsimonious deep FNNs. Our method determines the number of layers, the number of neurons at each layer, and the sparse connectivity between adjacent layers automatically from data. The resulting models are called Backbone-Skippath Neural Networks (BSNNs). Experiments on 17 tasks show that, in comparison with FNNs, BSNNs can achieve better or comparable classification performance with much fewer parameters. The interpretability of BSNNs is also shown to be better than that of FNNs. | Reject |
ICLR.cc/2023/Conference | Scalable Multi-Modal Continual Meta-Learning | This paper focuses on continual meta-learning, where few-shot tasks are sequentially available and sampled from a non-stationary distribution. Motivated by this challenging setting, many works have been developed with a mixture of meta-knowledge to cope with the heterogeneity and a dynamically changing number of components to capture incremental information. However, the underlying assumption of mutual exclusiveness among mixture components prevents sharing meta-knowledge across different clusters of tasks. Moreover, the existing incremental methods only rely on the prior to determine whether to increase meta-knowledge, where the unlimited increase would lead to parameter inefficiency. In our work, we propose a Scalable Multi-Modal Continual Meta-Learning (SMM-CML) algorithm. It employs a multi-modal premise that not only encourages different clusters of tasks to share meta-knowledge but also maintains their diversity. Moreover, to capture the incremental information, our algorithm uses Indian Buffet Process (IBP) as a prior number of components and proposes a sparsity method based on evidential theory to filter out the components without receiving support information directly from tasks. Thus we can learn the posterior number of components to avoid parameter inefficiency and reduce computational consumption. Experiments show SMM-CML outperforms SOTA baselines, which illustrates the effectiveness of our multi-modal meta-knowledge, and confirms that our algorithm can learn the really need meta-knowledge from tasks. | Reject |
ICLR.cc/2021/Conference | IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot Learning | The need of collecting large quantities of labeled training data for each new task has limited the usefulness of deep neural networks. Given data from a set of source tasks, this limitation can be overcome using two transfer learning approaches: few-shot learning (FSL) and self-supervised learning (SSL). The former aims to learn `how to learn' by designing learning episodes using source tasks to simulate the challenge of solving the target new task with few labeled samples. In contrast, the latter exploits an annotation-free pretext task across all source tasks in order to learn generalizable feature representations. In this work, we propose a novel Instance-level and Episode-level Pretext Task (IEPT) framework that seamlessly integrates SSL into FSL. Specifically, given an FSL episode, we first apply geometric transformations to each instance to generate extended episodes. At the instance-level, transformation recognition is performed as per standard SSL. Importantly, at the episode-level, two SSL-FSL hybrid learning objectives are devised: (1) The consistency across the predictions of an FSL classifier from different extended episodes is maximized as an episode-level pretext task. (2) The features extracted from each instance across different episodes are integrated to construct a single FSL classifier for meta-learning. Extensive experiments show that our proposed model (i.e., FSL with IEPT) achieves the new state-of-the-art. | Accept (Poster) |
ICLR.cc/2023/Conference | Consistent Data Distribution Sampling for Large-scale Retrieval | Retrieving candidate items with low latency and computational cost is important for large-scale advertising systems. Negative sampling is a general approach to model million-scale items with rich features in the retrieval. The training-inference inconsistency of data distribution brought from sampling negatives is a key challenge. In this work, we propose a novel negative sampling strategy Consistent Data Distribution Sampling (CDDS) to solve such an issue. Specifically, we employ a relative large-scale of uniform training negatives and batch negatives to adequately train long-tail and hot items respectively, and employ high divergence negatives to improve the learning convergence. To make the above training samples approximate the serving item data distribution, we introduce an auxiliary loss based on an asynchronous item embedding matrix over the entire item pool. Offline experiments on real datasets achieve SOTA performance. Online experiments with multiple advertising scenarios show that our method has achieved significant increases in GMV. The source code will be released in the future. | Reject |
ICLR.cc/2023/Conference | Sampling-based inference for large linear models, with application to linearised Laplace | Large-scale linear models are ubiquitous throughout machine learning, with contemporary application as surrogate models for neural network uncertainty quantification; that is, the linearised Laplace method. Alas, the computational cost associated with Bayesian linear models constrains this method's application to small networks, small output spaces and small datasets. We address this limitation by introducing a scalable sample-based Bayesian inference method for conjugate Gaussian multi-output linear models, together with a matching method for hyperparameter (regularisation) selection. Furthermore, we use a classic feature normalisation method (the g-prior) to resolve a previously highlighted pathology of the linearised Laplace method. Together, these contributions allow us to perform linearised neural network inference with ResNet-18 on CIFAR100 (11M parameters, 100 output dimensions × 50k datapoints) and with a U-Net on a high-resolution tomographic reconstruction task (2M parameters, 251k output dimensions). | Accept: poster |
ICLR.cc/2023/Conference | Perturbation Analysis of Neural Collapse | Training deep neural networks for classification often includes minimizing the training loss beyond the zero training error point. In this phase of training, a “neural collapse” behavior has been observed: the variability of features (outputs of the penultimate layer) of within-class samples decreases and the mean features of different classes approach a certain tight frame structure. Recent works analyze this behavior via idealized unconstrained features models where all the minimizers exhibit exact collapse. However, with practical networks and datasets, the features typically do not reach exact collapse, e.g., because deep layers cannot arbitrarily modify intermediate features that are far from being collapsed. In this paper, we propose a richer model that can capture this phenomenon by forcing the features to stay in the vicinity of a predefined features matrix (e.g., intermediate features). We explore the model in the small vicinity case via perturbation analysis and establish results that cannot be obtained by the previously studied models. For example, we prove reduction in the within-class variability of the optimized features compared to the predefined input features (via analyzing gradient flow on the “central-path” with minimal assumptions), analyze the minimizers in the near-collapse regime, and provide insights on the effect of regularization hyperparameters on the closeness to collapse. We support our theory with experiments in practical deep learning settings. | Reject |
ICLR.cc/2021/Conference | Noise or Signal: The Role of Image Backgrounds in Object Recognition | We assess the tendency of state-of-the-art object recognition models to depend on signals from image backgrounds. We create a toolkit for disentangling foreground and background signal on ImageNet images, and find that (a) models can achieve non-trivial accuracy by relying on the background alone, (b) models often misclassify images even in the presence of correctly classified foregrounds--up to 88% of the time with adversarially chosen backgrounds, and (c) more accurate models tend to depend on backgrounds less. Our analysis of backgrounds brings us closer to understanding which correlations machine learning models use, and how they determine models' out of distribution performance.
| Accept (Poster) |
ICLR.cc/2020/Conference | Quantum algorithm for finding the negative curvature direction | We present an efficient quantum algorithm aiming to find the negative curvature direction for escaping the saddle point, which is a critical subroutine for many second-order non-convex optimization algorithms. We prove that our algorithm could produce the target state corresponding to the negative curvature direction with query complexity O(polylog(d)ε^(-1)), where d is the dimension of the optimization function. The quantum negative curvature finding algorithm is exponentially faster than any known classical method which takes time at least O(dε^(−1/2)). Moreover, we propose an efficient algorithm to achieve the classical read-out of the target state. Our classical read-out algorithm runs exponentially faster on the degree of d than existing counterparts. | Reject |
ICLR.cc/2020/Conference | Constant Curvature Graph Convolutional Networks | Interest has been rising lately towards methods representing data in non-Euclidean spaces, e.g. hyperbolic or spherical. These geometries provide specific inductive biases useful for certain real-world data properties, e.g. scale-free or hierarchical graphs are best embedded in a hyperbolic space. However, the very popular class of graph neural networks is currently limited to model data only via Euclidean node embeddings and associated vector space operations. In this work, we bridge this gap by proposing mathematically grounded generalizations of graph convolutional networks (GCN) to (products of) constant curvature spaces. We do this by i) extending the gyro-vector space theory from hyperbolic to spherical spaces, providing a unified and smooth view of the two geometries, ii) leveraging gyro-barycentric coordinates that generalize the classic Euclidean concept of the center of mass. Our class of models gives strict generalizations in the sense that they recover their Euclidean counterparts when the curvature goes to zero from either side. Empirically, our methods outperform different types of classic Euclidean GCNs in the tasks of node classification and minimizing distortion for symbolic data exhibiting non-Euclidean behavior, according to their discrete curvature. | Reject |
ICLR.cc/2022/Conference | Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop | This paper investigates unsupervised learning of Full-Waveform Inversion (FWI), which has been widely used in geophysics to estimate subsurface velocity maps from seismic data. This problem is mathematically formulated by a second order partial differential equation (PDE), but is hard to solve. Moreover, acquiring velocity map is extremely expensive, making it impractical to scale up a supervised approach to train the mapping from seismic data to velocity maps with convolutional neural networks (CNN).We address these difficulties by $\textit{integrating PDE and CNN in a loop}$, thus shifting the paradigm to unsupervised learning that only requires seismic data. In particular, we use finite difference to approximate the forward modeling of PDE as a differentiable operator (from velocity map to seismic data) and model its inversion by CNN (from seismic data to velocity map). Hence, we transform the supervised inversion task into an unsupervised seismic data reconstruction task. We also introduce a new large-scale dataset $\textit{OpenFWI}$, to establish a more challenging benchmark for the community. Experiment results show that our model (using seismic data alone) yields comparable accuracy to the supervised counterpart (using both seismic data and velocity map). Furthermore, it outperforms the supervised model when involving more seismic data. | Accept (Poster) |
ICLR.cc/2022/Conference | Deep Inverse Reinforcement Learning via Adversarial One-Class Classification | Traditional inverse reinforcement learning (IRL) methods require a loop to find the optimal policy for each reward update (called an inner loop), resulting in very time-consuming reward estimation. In contrast, classification-based IRL methods, which have been studied recently, do not require an inner loop and estimate rewards quickly, although it is difficult to prepare an appropriate baseline corresponding to the expert trajectory. In this study, we introduced adversarial one-class classification into the classification-based IRL framework, and consequently developed a novel IRL method that requires only expert trajectories. We experimentally verified that the developed method can achieve the same performance as existing methods. | Reject |
ICLR.cc/2020/Conference | Implicit λ-Jeffreys Autoencoders: Taking the Best of Both Worlds | We propose a new form of an autoencoding model which incorporates the best properties of variational autoencoders (VAE) and generative adversarial networks (GAN). It is known that GAN can produce very realistic samples while VAE does not suffer from mode collapsing problem. Our model optimizes λ-Jeffreys divergence between the model distribution and the true data distribution. We show that it takes the best properties of VAE and GAN objectives. It consists of two parts. One of these parts can be optimized by using the standard adversarial training, and the second one is the very objective of the VAE model. However, the straightforward way of substituting the VAE loss does not work well if we use an explicit likelihood such as Gaussian or Laplace which have limited flexibility in high dimensions and are unnatural for modelling images in the space of pixels. To tackle this problem we propose a novel approach to train the VAE model with an implicit likelihood by an adversarially trained discriminator. In an extensive set of experiments on CIFAR-10 and TinyImagent datasets, we show that our model achieves the state-of-the-art generation and reconstruction quality and demonstrate how we can balance between mode-seeking and mode-covering behaviour of our model by adjusting the weight λ in our objective. | Reject |
ICLR.cc/2020/Conference | Graph Neural Networks for Reasoning 2-Quantified Boolean Formulas | It is valuable yet remains challenging to apply neural networks in logical reasoning tasks. Despite some successes witnessed in learning SAT (Boolean Satisfiability) solvers for propositional logic via Graph Neural Networks (GNN), there haven't been any successes in learning solvers for more complex predicate logic. In this paper, we target the QBF (Quantified Boolean Formula) satisfiability problem, the complexity of which is in-between propositional logic and predicate logic, and investigate the feasibility of learning GNN-based solvers and GNN-based heuristics for the cases with a universal-existential quantifier alternation (so-called 2QBF problems).
We conjecture, with empirical support, that GNNs have certain limitations in learning 2QBF solvers, primarily due to the inability to reason about a set of assignments. Then we show the potential of GNN-based heuristics in CEGAR-based solvers and explore the interesting challenges to generalize them to larger problem instances. In summary, this paper provides a comprehensive surveying view of applying GNN-based embeddings to 2QBF problems and aims to offer insights in applying machine learning tools to more complicated symbolic reasoning problems.
| Reject |
ICLR.cc/2021/Conference | Pre-Training by Completing Point Clouds | There has recently been a flurry of exciting advances in deep learning models on point clouds. However, these advances have been hampered by the difficulty of creating labelled point cloud datasets: sparse point clouds often have unclear label identities for certain points, while dense point clouds are time-consuming to annotate. Inspired by mask-based pre-training in the natural language processing community, we propose a pre-training mechanism based point clouds completion. It works by masking occluded points that result from observations at different camera views. It then optimizes a completion model that learns how to reconstruct the occluded points, given the partial point cloud. In this way, our method learns a pre-trained representation that can identify the visual constraints inherently embedded in real-world point clouds. We call our method Occlusion Completion (OcCo). We demonstrate that OcCo learns representations that improve the semantic understandings as well as generalization on downstream tasks over prior methods, transfer to different datasets, reduce training time and improve label efficiency. | Reject |
ICLR.cc/2020/Conference | Learn Interpretable Word Embeddings Efficiently with von Mises-Fisher Distribution | Word embedding plays a key role in various tasks of natural language processing. However, the dominant word embedding models don't explain what information is carried with the resulting embeddings. To generate interpretable word embeddings we intend to replace the word vector with a probability density distribution. The insight here is that if we regularize the mixture distribution of all words to be uniform, then we can prove that the inner product between word embeddings represent the point-wise mutual information between words. Moreover, our model can also handle polysemy. Each word's probability density distribution will generate different vectors for its various meanings. We have evaluated our model in several word similarity tasks. Results show that our model can outperform the dominant models consistently in these tasks. | Reject |
ICLR.cc/2023/Conference | Towards Inferential Reproducibility of Machine Learning Research | Reliability of machine learning evaluation --- the consistency of observed evaluation scores across replicated model training runs --- is affected by several sources of nondeterminism which can be regarded as measurement noise. Current tendencies to remove noise in order to enforce reproducibility of research results neglect inherent nondeterminism at the implementation level and disregard crucial interaction effects between algorithmic noise factors and data properties. This limits the scope of conclusions that can be drawn from such experiments. Instead of removing noise, we propose to incorporate several sources of variance, including their interaction with data properties, into an analysis of significance and reliability of machine learning evaluation, with the aim to draw inferences beyond particular instances of trained models. We show how to use linear mixed effects models (LMEMs) to analyze performance evaluation scores, and to conduct statistical inference with a generalized likelihood ratio test (GLRT). This allows us to incorporate arbitrary sources of noise like meta-parameter variations into statistical significance testing, and to assess performance differences conditional on data properties. Furthermore, a variance component analysis (VCA) enables the analysis of the contribution of noise sources to overall variance and the computation of a reliability coefficient by the ratio of substantial to total variance. | Accept: poster |
ICLR.cc/2022/Conference | AARL: Automated Auxiliary Loss for Reinforcement Learning | A good state representation is crucial to reinforcement learning (RL) while an ideal representation is hard to learn only with signals from the RL objective. Thus, many recent works manually design auxiliary losses to improve sample efficiency and decision performance. However, handcrafted auxiliary losses rely heavily on expert knowledge, and therefore lack scalability and can be suboptimal for boosting RL performance. In this work, we introduce Automated Auxiliary loss for Reinforcement Learning (AARL), a principled approach that automatically searches the optimal auxiliary loss function for RL. Specifically, based on the collected trajectory data, we define a general auxiliary loss space of size $4.6\times10^{19}$ and explore the space with an efficient evolutionary search strategy. We evaluate AARL on the DeepMind Control Suite and show that the searched auxiliary losses have significantly improved RL performance in both pixel-based and state-based settings, with the largest performance gain observed in the most challenging tasks. AARL greatly outperforms state-of-the-art methods and demonstrates strong generalization ability in unseen domains and tasks. We further conduct extensive studies to shed light on the effectiveness of auxiliary losses in RL. | Reject |
ICLR.cc/2023/Conference | Cramming: Training a language model on a single GPU in one day | Recent trends in language modeling have focused on increasing performance through scaling, and have resulted in an environment where training language models is out of reach for most researchers and practitioners. While most in the community are asking how to push the limits of extreme computation, we ask the opposite question:
How far can we get with a single GPU in just one day?
We investigate the downstream performance achievable with a transformer-based language model trained completely from scratch with masked language modeling for a single day on a single consumer GPU.
Aside from re-analyzing nearly all components of the pretraining pipeline for this scenario and providing a modified pipeline with performance close to BERT, we investigate why scaling down is hard, and which modifications actually improve performance in this scenario. We provide evidence that even in this constrained setting, performance closely follows scaling laws observed in large-compute settings. Through the lens of scaling laws, we categorize a range of recent improvements to training and architecture and discuss their merit and practical applicability (or lack thereof) for the limited compute setting. | Reject |
ICLR.cc/2020/Conference | Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning | Meta-learning methods, most notably Model-Agnostic Meta-Learning (Finn et al, 2017) or MAML, have achieved great success in adapting to new tasks quickly, after having been trained on similar tasks.
The mechanism behind their success, however, is poorly understood.
We begin this work with an experimental analysis of MAML, finding that deep models are crucial for its success, even given sets of simple tasks where a linear model would suffice on any individual task.
Furthermore, on image-recognition tasks, we find that the early layers of MAML-trained models learn task-invariant features, while later layers are used for adaptation, providing further evidence that these models require greater capacity than is strictly necessary for their individual tasks.
Following our findings, we propose a method which enables better use of model capacity at inference time by separating the adaptation aspect of meta-learning into parameters that are only used for adaptation but are not part of the forward model.
We find that our approach enables more effective meta-learning in smaller models, which are suitably sized for the individual tasks.
| Reject |
ICLR.cc/2020/Conference | Transfer Alignment Network for Double Blind Unsupervised Domain Adaptation | How can we transfer knowledge from a source domain to a target domain when each side cannot observe the data in the other side? The recent state-of-the-art deep architectures show significant performance in classification tasks which highly depend on a large number of training data. In order to resolve the dearth of abundant target labeled data, transfer learning and unsupervised learning leverage data from different sources and unlabeled data as training data, respectively. However, in some practical settings, transferring source data to target domain is restricted due to a privacy policy.
In this paper, we define the problem of unsupervised domain adaptation under double blind constraint, where either the source or the target domain cannot observe the data in the other domain, but data from both domains are used for training. We propose TAN (Transfer Alignment Network for Double Blind Domain Adaptation), an effective method for the problem by aligning source and target domain features. TAN maps the target feature into source feature space so that the classifier learned from the labeled data in the source domain is readily used in the target domain. Extensive experiments show that TAN 1) provides the state-of-the-art accuracy for double blind domain adaptation, and 2) outperforms baselines regardless of the proportion of target domain data in the training data.
| Reject |
ICLR.cc/2019/Conference | Incremental Few-Shot Learning with Attention Attractor Networks | Machine learning classifiers are often trained to recognize a set of pre-defined classes. However,
in many real applications, it is often desirable to have the flexibility of learning additional
concepts, without re-training on the full training set. This paper addresses this problem,
incremental few-shot learning, where a regular classification network has already been trained to
recognize a set of base classes; and several extra novel classes are being considered, each with
only a few labeled examples. After learning the novel classes, the model is then evaluated on the
overall performance of both base and novel classes. To this end, we propose a meta-learning model,
the Attention Attractor Network, which regularizes the learning of novel classes. In each episode,
we train a set of new weights to recognize novel classes until they converge, and we show that the
technique of recurrent back-propagation can back-propagate through the optimization process and
facilitate the learning of the attractor network regularizer. We demonstrate that the learned
attractor network can recognize novel classes while remembering old classes without the need to
review the original training set, outperforming baselines that do not rely on an iterative
optimization process. | Reject |
ICLR.cc/2023/Conference | Out-of-distribution Representation Learning for Time Series Classification | Time series classification is an important problem in the real world. Due to its non-stationary property that the distribution changes over time, it remains challenging to build models for generalization to unseen distributions. In this paper, we propose to view time series classification from the distribution perspective. We argue that the temporal complexity of a time series dataset could attribute to unknown latent distributions that need characterize. To this end, we propose DIVERSIFY for out-of-distribution (OOD) representation learning on dynamic distributions of times series. DIVERSIFY takes an iterative process: it first obtains the ‘worst-case’ latent distribution scenario via adversarial training, then reduces the gap between these latent distributions. We then show that such an algorithm is theoretically supported. Extensive experiments are conducted on seven datasets with different OOD settings across gesture recognition, speech commands recognition, wearable stress and affect detection, and sensor-based human activity recognition. Qualitative and quantitative results demonstrate that DIVERSIFY significantly outperforms other baselines and effectively characterizes the latent distributions. Code is available at https://github.com/microsoft/robustlearn. | Accept: poster |
ICLR.cc/2020/Conference | OPTIMAL TRANSPORT, CYCLEGAN, AND PENALIZED LS FOR UNSUPERVISED LEARNING IN INVERSE PROBLEMS | The penalized least squares (PLS) is a classic approach to inverse problems, where a regularization term is added to stabilize the solution. Optimal transport (OT) is another mathematical framework for computer vision tasks by providing means to transport one measure to another at minimal cost. Cycle-consistent generative adversarial network (cycleGAN) is a recent extension of GAN to learn target distributions with less mode collapsing behavior. Although similar in that no supervised training is required, the algorithms look different, so the mathematical relationship between these approaches is not clear. In this article, we provide an important advance to unveil the missing link. Specifically, we reveal that a cycleGAN architecture can be derived as a dual formulation of the optimal transport problem, if the PLS with a deep learning penalty is used as a transport cost between the two probability measures from measurements and unknown images. This suggests that cycleGAN can be considered as stochastic generalization of classical PLS approaches.
Our derivation is so general that various types of cycleGAN architecture can be easily derived by merely changing the transport cost. As proofs of concept, this paper provides novel cycleGAN architecture for unsupervised learning in accelerated MRI and deconvolution microscopy problems, which confirm the efficacy and the flexibility of the theory. | Reject |
ICLR.cc/2022/Conference | Pretraining for Language Conditioned Imitation with Transformers | We study reinforcement learning (RL) agents which can utilize language inputs. To investigate this, we propose a new multimodal benchmark -- Text-Conditioned Frostbite -- in which an agent must complete tasks specified by text instructions in the Atari Frostbite environment. We curate and release a dataset of 5M text-labelled transitions for training and to encourage further research in this direction. On this benchmark, we evaluate Text Decision Transformer (TDT), a transformer directly operating on text, state, and action tokens, and find it improves upon other baseline architectures. Furthermore, we evaluate the effect of pretraining, finding unsupervised pretraining can yield improved results in low-data settings. | Reject |
ICLR.cc/2020/Conference | Neural Operator Search | Existing neural architecture search (NAS) methods explore a limited feature-transformation-only search space while ignoring other advanced feature operations such as feature self-calibration by attention and dynamic convolutions. This disables the NAS algorithms to discover more advanced network architectures. We address this limitation by additionally exploiting feature self-calibration operations, resulting in a heterogeneous search space. To solve the challenges of operation heterogeneity and significantly larger search space, we formulate a neural operator search (NOS) method. NOS presents a novel heterogeneous residual block for integrating the heterogeneous operations in a unified structure, and an attention guided search strategy for facilitating the search process over a vast space. Extensive experiments show that NOS can search novel cell architectures with highly competitive performance on the CIFAR and ImageNet benchmarks.
| Reject |
ICLR.cc/2022/Conference | Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future | For real-time forecasting in domains like public health and macroeconomics, data collection is a non-trivial and demanding task. Often after being initially released, it undergoes several revisions later (maybe due to human or technical constraints) - as a result, it may take weeks until the data reaches a stable value. This so-called ‘backfill’ phenomenon and its effect on model performance have been barely addressed in the prior literature. In this paper, we introduce the multi-variate backfill problem using COVID-19 as the motivating example.
We construct a detailed dataset composed of relevant signals over the past year of the pandemic.
We then systematically characterize several patterns in backfill dynamics and leverage our observations for formulating a novel problem and neural framework, Back2Future, that aims to refines a given model's predictions in real-time. Our extensive experiments demonstrate that our method refines the performance of the diverse set of top models for COVID-19 forecasting and GDP growth forecasting. Specifically, we show that Back2Future refined top COVID-19 models by 6.65% to 11.24% and yield an 18% improvement over non-trivial baselines. In addition, we show that our model improves model evaluation too; hence policy-makers can better understand the true accuracy of forecasting models in real-time. | Accept (Poster) |
ICLR.cc/2019/Conference | NICE: noise injection and clamping estimation for neural network quantization | Convolutional Neural Networks (CNN) are very popular in many fields including computer vision, speech recognition, natural language processing, to name a few. Though deep learning leads to groundbreaking performance in these domains, the networks used are very demanding computationally and are far from real-time even on a GPU, which is not power efficient and therefore does not suit low power systems such as mobile devices. To overcome this challenge, some solutions have been proposed for quantizing the weights and activations of these networks, which accelerate the runtime significantly. Yet, this acceleration comes at the cost of a larger error. The NICE method proposed in this work trains quantized neural networks by noise injection and a learned clamping, which improve the accuracy. This leads to state-of-the-art results on various regression and classification tasks, e.g., ImageNet classification with architectures such as ResNet-18/34/50 with low as 3-bit weights and 3 -bit activations. We implement the proposed solution on an FPGA to demonstrate its applicability for low power real-time applications. | Reject |
ICLR.cc/2020/Conference | Rethinking deep active learning: Using unlabeled data at model training | Active learning typically focuses on training a model on few labeled examples alone, while unlabeled ones are only used for acquisition. In this work we depart from this setting by using both labeled and unlabeled data during model training across active learning cycles. We do so by using unsupervised feature learning at the beginning of the active learning pipeline and semi-supervised learning at every active learning cycle, on all available data. The former has not been investigated before in active learning, while the study of latter in the context of deep learning is scarce and recent findings are not conclusive with respect to its benefit. Our idea is orthogonal to acquisition strategies by using more data, much like ensemble methods use more models. By systematically evaluating on a number of popular acquisition strategies and datasets, we find that the use of unlabeled data during model training brings a spectacular accuracy improvement in image classification, compared to the differences between acquisition strategies. We thus explore smaller label budgets, even one label per class. | Reject |
ICLR.cc/2021/Conference | You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling | Transformer-based models have come to dominate the landscape in a wide range of natural language processing (NLP) applications. The heart of the transformer model is the self-attention mechanism, which captures the interactions of token pairs in the input sequences and consequently, depends quadratically on the input sequence length. It is known that training such models on longer sequences is quite expensive, and often, prohibitively so. We show that a Bernoulli sampling attention mechanism based on Locality Sensitive Hashing (LSH), decreases the quadratic complexity to linear. We bypass the quadratic cost by considering self-attention as a sum of individual tokens associated with Bernoulli random variables that can, in principle, be sampled at once by a single hash (although in practice, this number may be a small constant). This leads to an efficient sampling scheme to estimate self-attention which relies on specific modifications of LSH (based on feasibility of deployment on GPU architectures). We evaluate our proposed algorithm on the GLUE benchmark with standard 512 sequence length and our method achieves comparable or even slightly better performance than a standard pretrained Transformer. To evaluate whether our method can indeed handle longer sequences, we conduct experiments on long sequence (4096) language model pretraining and achieve consistent results as standard self-attention, while observing sizable inference speed-ups and memory savings. | Reject |
ICLR.cc/2021/Conference | Hellinger Distance Constrained Regression | This paper introduces an off-policy reinforcement learning method that uses Hellinger distance between sampling policy (from what samples were collected) and current policy (policy being optimized) as a constraint.
Hellinger distance squared multiplied by two is greater than or equal to total variation distance squared and less than or equal to Kullback-Leibler divergence, therefore a lower bound for expected discounted return for the new policy is improved compared to the lower bound for training with KL.
Also, Hellinger distance is less than or equal to 1, so there is a policy-independent lower bound for expected discounted return.
HDCR is capable of training with Experience Replay, a common setting for distributed RL when collecting trajectories using different policies and learning from this data centralized.
HDCR shows results comparable to or better than Advantage-weighted Behavior Model and Advantage-Weighted Regression on MuJoCo tasks using tiny offline datasets collected by random agents. On bigger datasets (100k timesteps) obtained by pretrained behavioral policy, HDCR outperforms ABM and AWR methods on 3 out of 4 tasks. | Reject |
ICLR.cc/2022/Conference | Dense Gaussian Processes for Few-Shot Segmentation | Few-shot segmentation is a challenging dense prediction task, which entails segmenting a novel query image given only a small annotated support set. The key problem is thus to design a method that aggregates detailed information from the support set, while being robust to large variations in appearance and context. To this end, we propose a few-shot segmentation method based on dense Gaussian process (GP) regression. Given the support set, our dense GP learns the mapping from local deep image features to mask values, capable of capturing complex appearance distributions. Furthermore, it provides a principled means of capturing uncertainty, which serves as another powerful cue for the final segmentation, obtained by a CNN decoder. Instead of a one-dimensional mask output, we further exploit the end-to-end learning capabilities of our approach to learn a high-dimensional output space for the GP. Our approach sets a new state-of-the-art for both 1-shot and 5-shot FSS on the PASCAL-5$^i$ and COCO-20$^i$ benchmarks, achieving an absolute gain of $+14.9$ mIoU in the COCO-20$^i$ 5-shot setting. Furthermore, the segmentation quality of our approach scales gracefully when increasing the support set size, while achieving robust cross-dataset transfer. | Reject |
ICLR.cc/2023/Conference | Planning With Uncertainty: Deep Exploration in Model-Based Reinforcement Learning | Deep model-based reinforcement learning has shown super-human performance in many challenging domains. Low sample efficiency and limited exploration remain however as leading obstacles in the field. In this paper, we demonstrate deep exploration in model-based RL by incorporating epistemic uncertainty into planning trees, circumventing the standard approach of propagating uncertainty through value learning. We evaluate this approach with the state of the art model-based RL algorithm MuZero, and extend its training process to stabilize learning from explicitly-exploratory decisions. Our results demonstrate that planning with uncertainty is able to achieve effective deep exploration with standard uncertainty estimation mechanisms, and with it significant gains in sample efficiency. | Reject |
ICLR.cc/2021/Conference | VECO: Variable Encoder-decoder Pre-training for Cross-lingual Understanding and Generation | Recent studies about learning multilingual representations have achieved significant performance gains across a wide range of downstream cross-lingual tasks. They train either an encoder-only Transformer mainly for understanding tasks, or an encoder-decoder Transformer specifically for generation tasks, ignoring the correlation between the two tasks and frameworks. In contrast, this paper presents a variable encoder-decoder (VECO) pre-training approach to unify the two mainstreams in both model architectures and pre-training tasks. VECO splits the standard Transformer block into several sub-modules trained with both inner-sequence and cross-sequence masked language modeling, and correspondingly reorganizes certain sub-modules for understanding and generation tasks during inference. Such a workflow not only ensures to train the most streamlined parameters necessary for two kinds of tasks, but also enables them to boost each other via sharing common sub-modules. As a result, VECO delivers new state-of-the-art results on various cross-lingual understanding tasks of the XTREME benchmark covering text classification, sequence labeling, question answering, and sentence retrieval. For generation tasks, VECO also outperforms all existing cross-lingual models and state-of-the-art Transformer variants on WMT14 English-to-German and English-to-French translation datasets, with gains of up to 1~2 BLEU. | Reject |
ICLR.cc/2023/Conference | ContraGen: Effective Contrastive Learning For Causal Language Model | Despite exciting progress in large-scale language generation, the expressiveness of its representations is severely limited by the \textit{anisotropy} issue where the hidden representations are distributed into a narrow cone in the vector space. To address this issue, we present ContraGen, a novel contrastive learning framework to improve the representation with better uniformity and discrimination at both sequence-level and token-level. We assess ContraGen on a wide range of downstream tasks in natural and programming languages. We show that ContraGen can effectively enhance both uniformity and discrimination of the representations and lead to the desired improvement on various language understanding tasks where discriminative representations are crucial for attaining good performance. Specifically, we attain $45.9\%$ relative improvement on the Semantic Textual Similarity tasks and $33.5\%$ on Code-to-Code Search tasks. Furthermore, by improving the expressiveness of the representations, ContraGen also boosts the source code generation capability with $9\%$ relative improvement on execution accuracy on HumanEval benchmark. | Reject |
ICLR.cc/2020/Conference | A Stochastic Trust Region Method for Non-convex Minimization | We target the problem of finding a local minimum in non-convex finite-sum minimization. Towards this goal, we first prove that the trust region method with inexact gradient and Hessian estimation can achieve a convergence rate of order $\mathcal{O}({1}/{k^{2/3}})$ as long as those differential estimations are sufficiently accurate.
Combining such result with a novel Hessian estimator, we propose a sample-efficient stochastic trust region (STR) algorithm which finds an $(\epsilon, \sqrt{\epsilon})$-approximate local minimum within $\tilde{\mathcal{O}}({\sqrt{n}}/{\epsilon^{1.5}})$ stochastic Hessian oracle queries.
This improves the state-of-the-art result by a factor of $\mathcal{O}(n^{1/6})$. Finally, we also develop Hessian-free STR algorithms which achieve the lowest runtime complexity.
Experiments verify theoretical conclusions and the efficiency of the proposed algorithms. | Reject |
ICLR.cc/2019/Conference | A rotation-equivariant convolutional neural network model of primary visual cortex | Classical models describe primary visual cortex (V1) as a filter bank of orientation-selective linear-nonlinear (LN) or energy models, but these models fail to predict neural responses to natural stimuli accurately. Recent work shows that convolutional neural networks (CNNs) can be trained to predict V1 activity more accurately, but it remains unclear which features are extracted by V1 neurons beyond orientation selectivity and phase invariance. Here we work towards systematically studying V1 computations by categorizing neurons into groups that perform similar computations. We present a framework for identifying common features independent of individual neurons' orientation selectivity by using a rotation-equivariant convolutional neural network, which automatically extracts every feature at multiple different orientations. We fit this rotation-equivariant CNN to responses of a population of 6000 neurons to natural images recorded in mouse primary visual cortex using two-photon imaging. We show that our rotation-equivariant network outperforms a regular CNN with the same number of feature maps and reveals a number of common features, which are shared by many V1 neurons and are pooled sparsely to predict neural activity. Our findings are a first step towards a powerful new tool to study the nonlinear functional organization of visual cortex. | Accept (Poster) |
ICLR.cc/2023/Conference | Improve distance metric learning by learning positions of class centers | Deep metric learning aims at learning a deep neural network by letting similar samples have small distances while dissimilar samples have large distances. To achieve this goal, the current DML algorithms mainly focus on pulling similar samples in each class as closely as possible. However, pulling similar samples only considers the local distribution of the data samples and ignores the global distribution of the data set, i.e., the center positions of different classes. The global distribution helps the distance metric learning. For example, expanding the distance between centers can increase the discriminant ability of the extracted features. However, how to increase the distance between centers is a challenging task. In this paper, we design a genius function named the skewed mean function, which only considers the most considerable distances of a set of samples. So maximizing the value of the skewed mean function can make the largest distance larger. We also prove that the current energy functions used for uniformity regularization on centers are special cases of our skewed mean function. At last, we conduct extensive experiments to illustrate the superiority of our methods. | Reject |
ICLR.cc/2021/Conference | Reinforcement Learning with Random Delays | Action and observation delays commonly occur in many Reinforcement Learning applications, such as remote control scenarios. We study the anatomy of randomly delayed environments, and show that partially resampling trajectory fragments in hindsight allows for off-policy multi-step value estimation. We apply this principle to derive Delay-Correcting Actor-Critic (DCAC), an algorithm based on Soft Actor-Critic with significantly better performance in environments with delays. This is shown theoretically and also demonstrated practically on a delay-augmented version of the MuJoCo continuous control benchmark. | Accept (Poster) |
ICLR.cc/2022/Conference | Effective Model Sparsification by Scheduled Grow-and-Prune Methods | Deep neural networks (DNNs) are effective in solving many real-world problems. Larger DNN models usually exhibit better quality (e.g., accuracy) but their excessive computation results in long inference time. Model sparsification can reduce the computation and memory cost while maintaining model quality. Most existing sparsification algorithms unidirectionally remove weights, while others randomly or greedily explore a small subset of weights in each layer for pruning. The limitations of these algorithms reduce the level of achievable sparsity. In addition, many algorithms still require pre-trained dense models and thus suffer from large memory footprint. In this paper, we propose a novel scheduled grow-and-prune (GaP) methodology without having to pre-train a dense model. It addresses the shortcomings of the previous works by repeatedly growing a subset of layers to dense and then pruning them back to sparse after some training. Experiments show that the models pruned using the proposed methods match or beat the quality of the highly optimized dense models at 80% sparsity on a variety of tasks, such as image classification, objective detection, 3D object part segmentation, and translation. They also outperform other state-of-the-art (SOTA) methods for model sparsification. As an example, a 90% non-uniform sparse ResNet-50 model obtained via GaP achieves 77.9% top-1 accuracy on ImageNet, improving the previous SOTA results by 1.5%. Code available at: https://github.com/boone891214/GaP. | Accept (Poster) |
ICLR.cc/2022/Conference | Learning Pseudometric-based Action Representations for Offline Reinforcement Learning | Offline reinforcement learning is a promising approach for practical applications since it does not require interactions with real-world environments. However, existing offline RL methods only work well in environments with continuous or small discrete action spaces. In environments with large and discrete action spaces, such as recommender systems and dialogue systems, the performance of existing methods decreases drastically because they suffer from inaccurate value estimation for a large proportion of o.o.d. actions. While recent works have demonstrated that online RL benefits from incorporating semantic information in action representations, unfortunately, they fail to learn reasonable relative distances between action representations, which is key to offline RL to reduce the influence of out-of-distribution (o.o.d.) actions. This paper proposes an action representation learning framework for offline RL based on a pseudometric, which measures both the behavioral relation and the data-distributional relation between actions. We provide theoretical analysis on the continuity and the bounds of the expected Q-values using the learned action representations. Experimental results show that our methods significantly improve the performance of two typical offline RL methods in environments with large and discrete action spaces. | Reject |
ICLR.cc/2019/Conference | Stability of Stochastic Gradient Method with Momentum for Strongly Convex Loss Functions | While momentum-based methods, in conjunction with the stochastic gradient descent, are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In practice, the momentum parameter is often chosen in a heuristic fashion with little theoretical guidance. In this work, we use the framework of algorithmic stability to provide an upper-bound on the generalization error for the class of strongly convex loss functions, under mild technical assumptions. Our bound decays to zero inversely with the size of the training set, and increases as the momentum parameter is increased. We also develop an upper-bound on the expected true risk, in terms of the number of training steps, the size of the training set, and the momentum parameter. | Reject |
ICLR.cc/2021/Conference | Neural Architecture Search without Training | The time and effort involved in hand-designing deep neural networks is immense. This has prompted the development of Neural Architecture Search (NAS) techniques to automate this design. However, NAS algorithms tend to be slow and expensive; they need to train vast numbers of candidate networks to inform the search process. This could be remedied if we could infer a network's trained accuracy from its initial state. In this work, we examine the correlation of linear maps induced by augmented versions of a single image in untrained networks and motivate how this can be used to give a measure which is highly indicative of a network’s trained performance. We incorporate this measure into a simple algorithm that allows us to search for powerful networks without any training in a matter of seconds on a single GPU, and verify its effectiveness on NAS-Bench-101 and NAS-Bench-201. Finally, we show that our approach can be readily combined with more expensive search methods for added value: we modify regularised evolutionary search to produce a novel algorithm that outperforms its predecessor. | Reject |
ICLR.cc/2019/Conference | Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator | Measuring divergence between two distributions is essential in machine learning and statistics and has various applications including binary classification, change point detection, and two-sample test. Furthermore, in the era of big data, designing divergence measure that is interpretable and can handle high-dimensional and complex data becomes extremely important. In this paper, we propose a post selection inference (PSI) framework for divergence measure, which can select a set of statistically significant features that discriminate two distributions. Specifically, we employ an additive variant of maximum mean discrepancy (MMD) for features and introduce a general hypothesis test for PSI. A novel MMD estimator using the incomplete U-statistics, which has an asymptotically normal distribution (under mild assumptions) and gives high detection power in PSI, is also proposed and analyzed theoretically. Through synthetic and real-world feature selection experiments, we show that the proposed framework can successfully detect statistically significant features. Last, we propose a sample selection framework for analyzing different members in the Generative Adversarial Networks (GANs) family. | Accept (Poster) |
ICLR.cc/2022/Conference | Avoiding Overfitting to the Importance Weights in Offline Policy Optimization | Offline policy optimization has a critical impact on many real-world decision-making problems, as online learning is costly and concerning in many applications. Importance sampling and its variants are a widely used type of estimator in offline policy evaluation, which can be helpful to remove assumptions on the chosen function approximations used to represent value functions and process models. In this paper, we identify an important overfitting phenomenon in optimizing the importance weighted return, and propose an algorithm to avoid this overfitting. We provide a theoretical justification of the proposed algorithm through a better per-state-neighborhood normalization condition and show the limitation of previous attempts to this approach through an illustrative example. We further test our proposed method in a healthcare-inspired simulator and a logged dataset collected from real hospitals. These experiments show the proposed method with less overfitting and better test performance compared with state-of-the-art batch reinforcement learning algorithms. | Reject |
ICLR.cc/2020/Conference | Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems | In many complex dynamical systems, artificial or natural, one can observe self-organization of patterns emerging from local rules. Cellular automata, like the Game of Life (GOL), have been widely used as abstract models enabling the study of various aspects of self-organization and morphogenesis, such as the emergence of spatially localized patterns. However, findings of self-organized patterns in such models have so far relied on manual tuning of parameters and initial states, and on the human eye to identify interesting patterns. In this paper, we formulate the problem of automated discovery of diverse self-organized patterns in such high-dimensional complex dynamical systems, as well as a framework for experimentation and evaluation. Using a continuous GOL as a testbed, we show that recent intrinsically-motivated machine learning algorithms (POP-IMGEPs), initially developed for learning of inverse models in robotics, can be transposed and used in this novel application area. These algorithms combine intrinsically-motivated goal exploration and unsupervised learning of goal space representations. Goal space representations describe the interesting features of patterns for which diverse variations should be discovered. In particular, we compare various approaches to define and learn goal space representations from the perspective of discovering diverse spatially localized patterns. Moreover, we introduce an extension of a state-of-the-art POP-IMGEP algorithm which incrementally learns a goal representation using a deep auto-encoder, and the use of CPPN primitives for generating initialization parameters. We show that it is more efficient than several baselines and equally efficient as a system pre-trained on a hand-made database of patterns identified by human experts. | Accept (Talk) |
ICLR.cc/2021/Conference | Quantile Regularization : Towards Implicit Calibration of Regression Models | Recent works have shown that most deep learning models are often poorly calibrated, i.e., they may produce overconfident
predictions that are wrong, implying that their uncertainty estimates are unreliable. While a number of approaches have been proposed recently to calibrate classification models, relatively little work exists on calibrating regression models. Isotonic Regression has recently been advocated for regression calibration. We provide a detailed formal analysis of the \emph{side-effects} of Isotonic Regression when used for regression calibration. To address this, we recast quantile calibration as entropy estimation, and leverage this idea to construct a novel quantile regularizer, which can be used in any optimization based probabilisitc regression models. Unlike most of the existing approaches for calibrating regression models, which are based on \emph{post-hoc} processing of the model's output, and require an additional dataset, our method is trainable in an end-to-end fashion, without requiring an additional dataset. We provide empirical results demonstrating that our approach improves calibration for regression models trained on diverse architectures that provide uncertainty estimates, such as Dropout VI, Deep Ensembles | Reject |
ICLR.cc/2021/Conference | On Trade-offs of Image Prediction in Visual Model-Based Reinforcement Learning | Model-based reinforcement learning (MBRL) methods have shown strong sample efficiency and performance across a variety of tasks, including when faced with high-dimensional visual observations. These methods learn to predict the environment dynamics and expected reward from interaction and use this predictive model to plan and perform the task. However, MBRL methods vary in their fundamental design choices, and it there is no strong consensus in the literature on how these design decisions affect performance. In this paper, we study a number of design decisions for the predictive model in visual MBRL algorithms, focusing specifically on methods that use a predictive model for planning. We find that a range of design decisions that are often considered crucial, such as the use of latent spaces, have little effect on task performance. A big exception to this finding is that predicting future observations (i.e., images) leads to significant task performance improvement compared to only predicting rewards. We also empirically find that image prediction accuracy, somewhat surprisingly, correlates more strongly with downstream task performance than reward prediction accuracy. We show how this phenomenon is related to exploration and how some of the lower-scoring models on standard benchmarks (that require exploration) will perform the same as the best-performing models when trained on the same training data. Simultaneously, in the absence of exploration, models that fit the data better usually perform better on the down-stream task as well, but surprisingly, these are often not the same models that perform the best when learning and exploring from scratch. These findings suggest that performance and exploration place important and potentially contradictory requirements on the model. | Reject |
ICLR.cc/2020/Conference | Mixed Precision DNNs: All you need is a good parametrization | Efficient deep neural network (DNN) inference on mobile or embedded devices typically involves quantization of the network parameters and activations. In particular, mixed precision networks achieve better performance than networks with homogeneous bitwidth for the same size constraint. Since choosing the optimal bitwidths is not straight forward, training methods, which can learn them, are desirable. Differentiable quantization with straight-through gradients allows to learn the quantizer's parameters using gradient methods. We show that a suited parametrization of the quantizer is the key to achieve a stable training and a good final performance. Specifically, we propose to parametrize the quantizer with the step size and dynamic range. The bitwidth can then be inferred from them. Other parametrizations, which explicitly use the bitwidth, consistently perform worse. We confirm our findings with experiments on CIFAR-10 and ImageNet and we obtain mixed precision DNNs with learned quantization parameters, achieving state-of-the-art performance. | Accept (Poster) |
ICLR.cc/2019/Conference | RESIDUAL NETWORKS CLASSIFY INPUTS BASED ON THEIR NEURAL TRANSIENT DYNAMICS | In this study, we analyze the input-output behavior of residual networks from a dynamical system point of view by disentangling the residual dynamics from the output activities before the classification stage. For a network with simple skip connections between every successive layer, and for logistic activation function, and shared weights between layers, we show analytically that there is a cooperation and competition dynamics between residuals corresponding to each input dimension.
Interpreting these kind of networks as nonlinear filters, the steady state value of the residuals in the case of attractor networks are indicative of the common features between different input dimensions that the network has observed during training, and has encoded in those components. In cases where residuals do not converge to an attractor state, their internal dynamics are separable for each input class, and the network can reliably approximate the output. We bring analytical and
empirical evidence that residual networks classify inputs based on the integration of the transient dynamics of the residuals, and will show how the network responds to input perturbations. We compare the network dynamics for a ResNet and a
Multi-Layer Perceptron and show that the internal dynamics, and the noise evolution are fundamentally different in these networks, and ResNets are more robust to noisy inputs. Based on these findings, we also develop a new method to adjust the depth for residual networks during training. As it turns out, after pruning the depth of a ResNet using this algorithm,the network is still capable of classifying inputs with a high accuracy. | Reject |
ICLR.cc/2020/Conference | Efficient meta reinforcement learning via meta goal generation | Meta reinforcement learning (meta-RL) is able to accelerate the acquisition of new tasks by learning from past experience. Current meta-RL methods usually learn to adapt to new tasks by directly optimizing the parameters of policies over primitive actions. However, for complex tasks which requires sophisticated control strategies, it would be quite inefficient to to directly learn such a meta-policy. Moreover, this problem can become more severe and even fail in spare reward settings, which is quite common in practice. To this end, we propose a new meta-RL algorithm called meta goal-generation for hierarchical RL (MGHRL) by leveraging hierarchical actor-critic framework. Instead of directly generate policies over primitive actions for new tasks, MGHRL learns to generate high-level meta strategies over subgoals given past experience and leaves the rest of how to achieve subgoals as independent RL subtasks. Our empirical results on several challenging simulated robotics environments show that our method enables more efficient and effective meta-learning from past experience and outperforms state-of-the-art meta-RL and Hierarchical-RL methods in sparse reward settings. | Reject |
ICLR.cc/2022/Conference | ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity | When the available hardware cannot meet the memory and compute requirements to efficiently train high performing machine learning models, a compromise in either the training quality or the model complexity is needed. In Federated Learning (FL), nodes are orders of magnitude more constrained than traditional server-grade hardware and are often battery powered, severely limiting the sophistication of models that can be trained under this paradigm. While most research has focused on designing better aggregation strategies to improve convergence rates and in alleviating the communication costs of FL, fewer efforts have been devoted to accelerating on-device training. Such stage, which repeats hundreds of times (i.e. every round) and can involve thousands of devices, accounts for the majority of the time required to train federated models and, the totality of the energy consumption at the client side. In this work, we present the first study on the unique aspects that arise when introducing sparsity at training time in FL workloads. We then propose ZeroFL, a framework that relies on highly sparse operations to accelerate on-device training. Models trained with ZeroFL and 95% sparsity achieve up to 2.3% higher accuracy compared to competitive baselines obtained from adapting a state-of-the-art sparse training framework to the FL setting. | Accept (Poster) |
ICLR.cc/2018/Conference | ShakeDrop regularization | This paper proposes a powerful regularization method named \textit{ShakeDrop regularization}.
ShakeDrop is inspired by Shake-Shake regularization that decreases error rates by disturbing learning.
While Shake-Shake can be applied to only ResNeXt which has multiple branches, ShakeDrop can be applied to not only ResNeXt but also ResNet, Wide ResNet and PyramidNet in a memory efficient way.
Important and interesting feature of ShakeDrop is that it strongly disturbs learning by multiplying even a negative factor to the output of a convolutional layer in the forward training pass.
The effectiveness of ShakeDrop is confirmed by experiments on CIFAR-10/100 and Tiny ImageNet datasets.
| Reject |
ICLR.cc/2023/Conference | An Exact Poly-Time Membership-Queries Algorithm for Extracting a Three-Layer ReLU Network | We consider the natural problem of learning a ReLU network from queries, which was recently remotivated by model extraction attacks. In this work, we present a polynomial-time algorithm that can learn a depth-two ReLU network from queries under mild general position assumptions. We also present a polynomial-time algorithm that, under mild general position assumptions, can learn a rich class of depth-three ReLU networks from queries. For instance, it can learn most networks where the number of first layer neurons is smaller than the dimension and the number of second layer neurons.
These two results substantially improve state-of-the-art: Until our work, polynomial-time algorithms were only shown to learn from queries depth-two networks under the assumption that either the underlying distribution is Gaussian (Chen et al. (2021)) or that the weights matrix rows are linearly independent (Milli et al. (2019)). For depth three or more, there were no known poly-time results. | Accept: poster |
ICLR.cc/2021/Conference | Multi-View Disentangled Representation | Learning effective representations for data with multiple views is crucial in machine learning and pattern recognition. Recently great efforts have focused on learning unified or latent representations to integrate information from different views for specific tasks. These approaches generally assume simple or implicit relationships between different views and as a result are not able to flexibly and explicitly depict the correlations among these views. To address this, we firstly propose the definition and conditions for multi-view disentanglement providing general instructions for disentangling representations between different views. Furthermore, a novel objective function is derived to explicitly disentangle the multi-view data into a shared part across different views and a (private) exclusive part within each view. Experiments on a variety of multi-modal datasets demonstrate that our objective can effectively disentangle information from different views while satisfying the disentangling conditions. | Reject |
ICLR.cc/2019/Conference | Novel positional encodings to enable tree-structured transformers | With interest in program synthesis and similarly flavored problems rapidly increasing, neural models optimized for tree-domain problems are of great value. In the sequence domain, transformers can learn relationships across arbitrary pairs of positions with less bias than recurrent models. Under the intuition that a similar property would be beneficial in the tree domain, we propose a method to extend transformers to tree-structured inputs and/or outputs. Our approach abstracts transformer's default sinusoidal positional encodings, allowing us to substitute in a novel custom positional encoding scheme that represents node positions within a tree. We evaluated our model in tree-to-tree program translation and sequence-to-tree semantic parsing settings, achieving superior performance over the vanilla transformer model on several tasks.
| Reject |
ICLR.cc/2023/Conference | Private and Efficient Meta-Learning with Low Rank and Sparse decomposition | Meta-learning is critical for a variety of practical ML systems -- like personalized recommendations systems -- that are required to generalize to new tasks despite a small number of task-specific training points. Existing meta-learning techniques use two complementary approaches of either learning a low-dimensional representation of points for all tasks, or task-specific fine-tuning of a global model trained using all the tasks. In this work, we propose a novel meta-learning framework that combines both the techniques to enable handling of a large number of data-starved tasks. Our framework models network weights as a sum of low-rank and sparse matrices. This allows us to capture information from multiple domains together in the low-rank part while still allowing task specific personalization using the sparse part. We instantiate and study the framework in the linear setting, where the problem reduces to that of estimating the sum of a rank-$r$ and a $k$-column sparse matrix using a small number of linear measurements. We propose an alternating minimization method with hard thresholding -- AMHT-LRS -- to learn the low-rank and sparse part effectively and efficiently. For the realizable, Gaussian data setting, we show that AMHT-LRS indeed solves the problem efficiently with nearly optimal samples. We extend AMHT-LRS to ensure that it preserves privacy of each individual user in the dataset, while still ensuring strong generalization with nearly optimal number of samples. Finally, on multiple datasets, we demonstrate that the framework allows personalized models to obtain superior performance in the data-scarce regime. | Reject |
ICLR.cc/2023/Conference | Scaling Laws For Deep Learning Based Image Reconstruction | Deep neural networks trained end-to-end to map a measurement of a (noisy) image to a clean image perform excellent for a variety of linear inverse problems.
Current methods are only trained on a few hundreds or thousands of images as opposed to the millions of examples deep networks are trained on in other domains.
In this work, we study whether major performance gains are expected from scaling up the training set size.
We consider image denoising, accelerated magnetic resonance imaging, and super-resolution and empirically determine the reconstruction quality as a function of training set size, while simultaneously scaling the network size.
For all three tasks we find that an initially steep power-law scaling slows significantly already at moderate training set sizes.
Interpolating those scaling laws suggests that even training on millions of images would not significantly improve performance.
To understand the expected behavior, we analytically characterize the performance of a linear estimator learned with early stopped gradient descent.
The result formalizes the intuition that once the error induced by learning the signal model is small relative to the error floor, more training examples do not improve performance. | Accept: poster |
ICLR.cc/2022/Conference | Universalizing Weak Supervision | Weak supervision (WS) frameworks are a popular way to bypass hand-labeling large datasets for training data-hungry models.
These approaches synthesize multiple noisy but cheaply-acquired estimates of labels into a set of high-quality pseudo-labels for downstream training. However, the synthesis technique is specific to a particular kind of label, such as binary labels or sequences, and each new label type requires manually designing a new synthesis algorithm. Instead, we propose a universal technique that enables weak supervision over any label type while still offering desirable properties, including practical flexibility, computational efficiency, and theoretical guarantees. We apply this technique to important problems previously not tackled by WS frameworks including learning to rank, regression, and learning in hyperbolic space. Theoretically, our synthesis approach produces a consistent estimators for learning some challenging but important generalizations of the exponential family model. Experimentally, we validate our framework and show improvement over baselines in diverse settings including real-world learning-to-rank and regression problems along with learning on hyperbolic manifolds. | Accept (Poster) |
ICLR.cc/2022/Conference | Data Scaling Laws in NMT: The Effect of Noise and Architecture | In this work, we empirically study the data scaling properties of neural machine translation (NMT). We first establish that the test loss of encoder-decoder transformer models scales as a power law in the number of training samples, with a dependence on the model size. We then systematically vary various aspects of the training setup to understand how they impact the data scaling laws. In particular, we change the (1) Architecture and task setup, to a Transformer-LSTM Hybrid as well as a Decoder-only transformer with language modeling loss (2) Noise level in the training distribution, starting with noisy data with filtering applied as well as clean data corrupted with synthetic iid noise. In all the above cases, we find that the data scaling exponents are minimally impacted, suggesting that marginally worse architectures or training data quality can be compensated for by adding more data. Lastly, we find that changing the training distribution to use back-translated data instead of parallel data, can impact the scaling exponent. | Reject |
ICLR.cc/2022/Conference | Simpler Calibration for Survival Analysis | Survival analysis, also known as time-to-event analysis, is the problem to predict the distribution of the time of the occurrence of an event. This problem has applications in various fields such as healthcare, security, and finance. While there have been many neural network models proposed for survival analysis, none of them are calibrated. This means that the average of the predicted distribution is different from the actual distribution in the dataset. Therefore, X-CAL has recently been proposed for the calibration, which is supposed to be used as a regularization term in the loss function of a neural network. X-CAL is formulated on the basis of the widely used definition of calibration for distribution regression. In this work, we propose new calibration definitions for distribution regression and survival analysis, and demonstrate a simpler alternative to X-CAL based on the new calibration definition for survival analysis.
| Reject |