GenAI4ELab/papercli-papers huggingface dataset:
$\Phi$-DVAE: Learning Physically Interpretable Representations with Nonlinear Filtering
Authors: Alex John Glyn-Davies; Connor Duffin; Omer Deniz Akyildiz; Mark Girolami
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Incorporating unstructured data into physical models is a challenging problem that is emerging in data assimilation. Traditional approaches focus on well-defined observation operators whose functional forms are typically assumed to be known. This prevents these methods from achieving a consistent model-data synthesis in configurations where the mapping from data-space to model-space is unknown. To address these shortcomings, in this paper we develop a physics-informed dynamical variational autoencoder ($\Phi$-DVAE) for embedding diverse data streams into time-evolving physical systems described by differential equations. Our approach combines a standard (possibly nonlinear) filter for the latent state-space model and a VAE, to embed the unstructured data stream into the latent dynamical system. A variational Bayesian framework is used for the joint estimation of the embedding, latent states, and unknown system parameters. To demonstrate the method, we look at three examples: video datasets generated by the advection and Korteweg-de Vries partial differential equations, and a velocity field generated by the Lorenz-63 system. Comparisons with relevant baselines show that the $\Phi$-DVAE provides a data efficient dynamics encoding methodology that is competitive with standard approaches, with the added benefit of incorporating a physically interpretable latent space.
CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling
Authors: Yansen Wang; XINYANG JIANG; Kan Ren; Caihua Shan; Xufang Luo; Kaitao Song; Dongsheng Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The successes of artificial neural networks (ANNs) are largely attributed to mimicking the human brain structures. Recent advances in neuroscience revealed that neurons interact with each other through various kinds of connectivity patterns to process information, in which the common connectivity patterns are also called circuit motifs. However, many existing ANNs can only model one or two circuit motifs in their architectures, so that their performance may drastically vary among different types of machine learning tasks. 
In this paper, we propose a new type of neural network inspired by the architectures of neuronal circuits, namely Circuit Neural Network (CircuitNet). In CircuitNet, a group of densely connected neurons, namely circuit motif unit (CMU), form the basic unit of the network, which is capable of modeling universal circuit motifs by adjusting the weights within the CMUs. Compared with traditional feed-forward networks, CircuitNet has the ability to model more types of neuron connections such as feed-back and lateral motifs.
Inspired by the locally dense and globally sparse structure of the human brain, several iterations of signal transmission among different CMUs are achieved by sparse connections through the input ports and output ports of different CMUs. 
Experiments have demonstrated that CircuitNet can outperform popular neural network architectures in function approximation, reinforcement learning, image classification, and time series forecasting tasks.
Reconciling Security and Communication Efficiency in Federated Learning
Authors: Karthik Prasad; Sayan Ghosh; Graham Cormode; Ilya Mironov; Ashkan Yousefpour; Pierre Stock
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Cross-device Federated Learning is an increasingly popular machine learning setting to train a model by leveraging a large population of client devices with high privacy and security guarantees. However, communication efficiency remains a major bottleneck when scaling federated learning to production environments, particularly due to bandwidth constraints during uplink communication. In this paper, we formalize and address the problem of compressing client-to-server model updates under the Secure Aggregation primitive, a core component of Federated Learning pipelines that allows the server to aggregate the client updates without accessing them individually. In particular, we adapt standard scalar quantization and pruning methods to Secure Aggregation and propose Secure Indexing, a variant of Secure Aggregation that supports quantization for extreme compression. We establish state-of-the-art results on LEAF benchmarks in a secure Federated Learning setup with up to 40x compression in uplink communication with no meaningful loss in utility compared to uncompressed baselines.
Neural Agents Struggle to Take Turns in Bidirectional Emergent Communication
Authors: Valentin Taillandier; Dieuwke Hupkes; Benoît Sagot; Emmanuel Dupoux; Paul Michel
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The spontaneous exchange of turns is a central aspect of human communication. Although turn-taking conventions come to us naturally, artificial dialogue agents struggle to coordinate, and must rely on hard-coded rules to engage in interactive conversations with human interlocutors. In this paper, we investigate the conditions under which artificial agents may naturally develop turn-taking conventions in a simple language game. We describe a cooperative task where success is contingent on the exchange of information along a shared communication channel where talking over each other hinders communication. Despite these environmental constraints, neural-network based agents trained to solve this task with reinforcement learning do not systematically adopt turn-taking conventions. However, we find that agents that do agree on turn-taking protocols end up performing better. 
Moreover, agents that are forced to perform turn-taking can learn to solve the task more quickly. 
This suggests that turn-taking may help to generate conversations that are easier for speakers to interpret.
Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing
Authors: Renyu Zhang; Aly A Khan; Robert L. Grossman; Yuxin Chen
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Active learning has demonstrated data efficiency in many fields. Existing active learning algorithms, especially in the context of batch-mode deep Bayesian active models, rely heavily on the quality of uncertainty estimations of the model, and are often challenging to scale to large batches. In this paper, we propose Batch-BALanCe, a scalable batch-mode active learning algorithm, which combines insights from decision-theoretic active learning, combinatorial information measure, and diversity sampling. At its core, Batch-BALanCe relies on a novel decision-theoretic acquisition function that facilitates differentiation among different equivalence classes. Intuitively, each equivalence class consists of hypotheses (e.g., posterior samples of deep neural networks) with similar predictions, and Batch-BALanCe adaptively adjusts the size of the equivalence classes as learning progresses. To scale up the computation of queries to large batches, we further propose an efficient batch-mode acquisition procedure, which aims to maximize a novel combinatorial information measure defined through the acquisition function. We show that our algorithm can effectively handle realistic multi-class classification tasks, and achieves compelling performance on several benchmark datasets for active learning under both low- and large-batch regimes.
Free Lunch for Domain Adversarial Training: Environment Label Smoothing
Authors: YiFan Zhang; xue wang; Jian Liang; Zhang Zhang; Liang Wang; Rong Jin; Tieniu Tan
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: A fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features by Domain Adversarial Training (DAT) received widespread attention. Despite its success, we observe training instability from DAT, mostly due to over-confident domain discriminator and environment label noise. To address this issue, we proposed Environment Label Smoothing (ELS), which encourages the discriminator to output soft probability, which thus reduces the confidence of the discriminator and alleviates the impact of noisy environment labels. We demonstrate, both experimentally and theoretically, that ELS can improve training stability, local convergence, and robustness to noisy environment labels. By incorporating ELS with DAT methods, we are able to yield state-of-art results on a wide range of domain generalization/adaptation tasks, particularly when the environment labels are highly noisy.
A Probabilistic Approach to Self-Supervised Learning using Cyclical Stochastic Gradient MCMC 
Authors: Masoumeh Javanbakhat; Christoph Lippert
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this paper we present a practical Bayesian formulation for self-supervised learning method with Cyclical Stochastic Gradient Hamiltonian Monte Carlo (cSGHMC). Within this framework, we place a prior over the parameters of a self-supervised learning model and use cSGHMC to approximate the high dimensional and multimodal posterior distribution over the embeddings. By exploring an expressive posterior over the embeddings, the Bayesian self-supervised learning produces interpretable and diverse representations. Marginalising over these representations results improvement in semi-supervised learning and out-of-distribution detection tasks. We provide experimental results on multiple classification tasks in semi-supervised learning including Cifar10 and Cifar100. Moreover we demonstrate the effectiveness of the proposed method in out-of distribution detection task using SVHN dataset.
SIMPLE: A Gradient Estimator for k-Subset Sampling
Authors: Kareem Ahmed; Zhe Zeng; Mathias Niepert; Guy Van den Broeck
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: $k$-subset sampling is ubiquitous in machine learning, enabling regularization and interpretability through sparsity. The challenge lies in rendering $k$-subset sampling amenable to end-to-end learning. This has typically involved relaxing the reparameterized samples to allow for backpropagation, but introduces both bias and variance. In this work, we fall back to discrete $k$-subset sampling on the forward pass. This is coupled with using the gradient with respect to the exact marginals, computed efficiently, as a proxy for the true gradient. We show that our gradient estimator exhibits lower bias and variance compared to state-of-the-art estimators. Empirical results show improved performance on learning to explain and sparse models benchmarks. We provide an algorithm for computing the exact ELBO for the $k$-subset distribution, obtaining significantly lower loss compared to state-of-the-art discrete sparse VAEs. All of our algorithms are exact and efficient.
TopoZero: Digging into  Topology Alignment on Zero-Shot Learning
Authors: Yang Liu; Fei Wang; Jiankang Deng; Chen WeiTao; Lei Shang; Baigui Sun; Xuansong Xie
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Common space learning, associating semantic and visual domains in a common
latent space, is essential to transfer knowledge from seen classes to unseen ones
on Zero-Shot Learning (ZSL) realm. Existing methods for common space learning
rely heavily on structure alignment due to the heterogeneous nature between
semantic and visual domains, but the existing design is sub-optimal. In this paper,
we utilize persistent homology to investigate geometry structure alignment,
and observe two following issues: (i) The sampled mini-batch data points present
a distinct structure gap compared to global data points, thus the learned structure
alignment space inevitably neglects abundant and accurate global structure
information. (ii) The latent visual and semantic space fail to preserve multiple
dimensional geometry structure, especially high dimensional structure information.
To address the first issue, we propose a Topology-guided Sampling Strategy
(TGSS) to mitigate the gap between sampled and global data points. Both theoretical
analyses and empirical results guarantee the effectiveness of the TGSS.
To solve the second issue, we introduce a Topology Alignment Module (TAM)
to preserve multi-dimensional geometry structure in latent visual and semantic
space, respectively. The proposed method is dubbed TopoZero. Empirically, our
TopoZero achieves superior performance on three authoritative ZSL benchmark
datasets.
CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Alignment
Authors: Hongwei Xue; Yuchong Sun; Bei Liu; Jianlong Fu; Ruihua Song; Houqiang Li; Jiebo Luo
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Pre-trained image-text models, like CLIP, have demonstrated the strong power of vision-language representation learned from a large scale of web-collected image-text data. In light of the well-learned visual features, there are works that transfer image representation to the video domain and achieve good results. However, adapting image-text pre-trained models to video-text pre-training (i.e., post-pretraining) has not demonstrated a significant advantage yet. In this paper, we tackle this challenge by raising and addressing two questions: 1) what are the factors hindering post-pretraining CLIP from improving performance on video-text tasks, and 2) how to mitigate the impact of these factors. Through a series of comparative experiments and analyses, we find that the data scale and domain gap between language sources have large impacts. By these observations, we propose an Omnisource Cross-modal Learning method equipped with a Video Proxy mechanism on the basis of CLIP, namely CLIP-ViP. Extensive results show that our approach improves the performance of CLIP on video-text retrieval by a large margin. Our model achieves state-of-the-art results on a variety of datasets, including MSR-VTT, DiDeMo, LSMDC, and ActivityNet. We release our code and pre-trained CLIP-ViP models at \url{https://github.com/microsoft/XPretrain/tree/main/CLIP-ViP}.
Quadratic models for understanding neural network dynamics
Authors: Libin Zhu; Chaoyue Liu; Adityanarayanan Radhakrishnan; Misha Belkin
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this work, we show that recently proposed quadratic models capture optimization and generalization properties of wide neural networks that cannot be captured by linear models.  In particular, we prove that quadratic models for shallow ReLU networks exhibit the "catapult phase" from Lewkowycz et al. (2020) that arises when training such models with large learning rates. We then empirically show that the behaviour of quadratic models parallels that of neural networks in generalization, especially in the catapult phase regime. Our analysis further demonstrates that quadratic models are an effective tool for analysis of neural networks.
ISS: Image as Stepping Stone for Text-Guided 3D Shape Generation
Authors: Zhengzhe Liu; Peng Dai; Ruihui Li; XIAOJUAN QI; Chi-Wing Fu
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Text-guided 3D shape generation remains challenging due to the absence of large paired text-shape dataset, the substantial semantic gap between these two modalities, and the structural complexity of 3D shapes. This paper presents a new framework called Image as Stepping Stone (ISS) for the task by introducing 2D image as a stepping stone to connect the two modalities and to eliminate the need for paired text-shape data. Our key contribution is a two-stage feature-space-alignment approach that maps CLIP features to shapes by harnessing a pre-trained single-view reconstruction (SVR) model with multi-view supervisions: first map the CLIP image feature to the detail-rich shape space in the SVR model, then map the CLIP text feature to the shape space and optimize the mapping by encouraging CLIP consistency between the input text and the rendered images. Further, we formulate a textguided shape stylization module to dress up the output shapes with novel structures and textures. Beyond existing works on 3D shape generation from text, our new approach is general for creating shapes in a broad range of categories, without requiring paired text-shape data. Experimental results manifest that our approach outperforms the state-of-the-arts and our baselines in terms of fidelity and consistency with text. Further, our approach can stylize the generated shapes with both realistic and fantasy structures and textures. Codes are available at https://github.com/liuzhengzhe/ISS-Image-as-Stepping-Stone-for-Text-Guided-3D-Shape-Generation.
Emergence of Exploration in Policy Gradient Reinforcement Learning via Resetting
Authors: Sotetsu Koyamada; Paavo Parmas; Tadashi Kozuno; Shin Ishii
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In reinforcement learning (RL), many exploration methods explicitly promote stochastic policies, e.g., by adding an entropy bonus. We argue that exploration only matters in RL because the agent repeatedly encounters the same or similar states, so that it is beneficial to gradually improve the performance over the encounters; otherwise, the greedy policy would be optimal. Based on this intuition, we propose ReMax, an objective for RL whereby stochastic exploration arises as an emergent property, without adding any explicit exploration bonus. In ReMax, an episode is modified so that the agent can reset to previous states in the trajectory, and the agent’s goal is to maximize the best return in the trajectory tree. We show that this ReMax objective can be directly optimized with an unbiased policy gradient method. Experiments confirm that ReMax leads to the emergence of a stochastic exploration policy, and improves the performance compared to RL with no exploration bonus.
Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks
Authors: Eeshaan Jain; Tushar Nandy; Gaurav Aggarwal; Ashish V. Tendulkar; Rishabh K Iyer; Abir De
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Subset selection, in recent times, has emerged as a successful approach toward efficient training of models by significantly reducing the amount of data and computational resources required. However, existing methods employ discrete combinatorial and model-specific approaches which lack generalizability--- for each new model, the algorithm has to be executed from the beginning. Therefore, for data subset selection for an unseen architecture, one cannot use the subset chosen for a different model. In this work, we propose SubSelNet, a non-adaptive  subset selection framework, which tackles these problems with two main components. First, we introduce an attention-based neural gadget that leverages the graph structure of architectures and acts as a surrogate to trained deep neural networks for quick model prediction. Then, we use these predictions to build subset samplers. This leads us to develop two variants of  SubSelNet. The first variant is transductive (called as Transductive-SubSelNet) which computes the subset separately for each model by solving a small optimization problem. Such an optimization is still super fast, thanks to the replacement of explicit model training by the model approximator. The second variant is inductive (called as Inductive-SubSelNet) which computes the subset using a trained subset selector, without any optimization.  Most state-of-the-art data subset selection approaches are adaptive, in that the subset selection adapts as the training progresses, and as a result, they require access to the entire data at training time.  Our approach, in contrast, is non-adaptive and does the subset selection only once in the beginning, thereby achieving resource and memory efficiency along with compute-efficiency at training time. Our experiments show that both transductive and inductive variants of our models outperform several methods on the quality of the subset chosen and further demonstrate that our method can be used for choosing the best architecture from a set of architectures.
Explainability as statistical inference
Authors: Hugo Henri Joseph Senetaire; Damien Garreau; Jes Frellsen; Pierre-Alexandre Mattei
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: A wide variety of model explanation approaches have been proposed in recent years, all guided by very different rationales and heuristics. In this paper, we take a new route and cast interpretability as a statistical inference problem. We propose a general deep probabilistic model designed to produce interpretable predictions. The model’s parameters can be learned via maximum likelihood, and the method can be adapted to any predictor network architecture, and any type of prediction problem. Our method is a case of amortized interpretability models, where a neural network is used as a selector to allow for fast interpretation at inference time. Several popular interpretability methods are shown to be particular cases of regularised maximum likelihood for our general model. We propose new datasets with ground truth selection which allow for the evaluation of the features importance map. Using these datasets, we show experimentally that using multiple imputation provides more reasonable interpretation.
A distinct unsupervised reference model from the environment helps continual learning
Authors: Seyyed AmirHossein Ameli Kalkhoran; Mohammadamin Banayeeanzade; Mahdi Samiei; Mahdieh Soleymani Baghshah
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The existing continual learning methods are mainly focused on fully-supervised scenarios and are still not able to take advantage of unlabeled data available in the environment. Some recent works tried to investigate semi-supervised continual learning (SSCL) settings in which the unlabeled data are available, but it is only from the same distribution as the labeled data. This assumption is still not general enough for real-world applications and restricts the utilization of unsupervised data. In this work, we introduce Open-Set Semi-Supervised Continual Learning (OSSCL), a more realistic semi-supervised continual learning setting in which out-of-distribution (OoD) unlabeled samples in the environment are assumed to coexist with the in-distribution ones. Under this configuration, we present a model with two distinct parts: (i) the reference network captures general-purpose and task-agnostic knowledge in the environment by using a broad spectrum of unlabeled samples, (ii) the learner network is designed to learn task-specific representations by exploiting supervised samples. The reference model both provides a pivotal representation space and also segregates unlabeled data to exploit them more efficiently. By performing a diverse range of experiments, we show the superior performance of our model compared with other competitors and prove the effectiveness of each component of the proposed model.