pwc-archive/papers-with-abstracts huggingface dataset:
{Learning disentangled representations with the Wasserstein Autoencoder
Authors: Benoit Gaujac, Ilya Feige, David Barber
Date: 2021-01-01 00:00:00
Tasks: Disentanglement, Representation Learning

Disentangled representation learning has undoubtedly benefited from objective function surgery. However, a delicate balancing act of tuning is still required in order to trade off reconstruction fidelity versus disentanglement. Building on previous successes of penalizing the total correlation in the latent variables, we propose TCWAE (Total Correlation Wasserstein Autoencoder). Working in the WAE paradigm naturally enables the separation of the total-correlation term, thus providing disentanglement control over the learned representation, while offering more flexibility in the choice of reconstruction cost. We propose two variants using different KL estimators and perform extensive quantitative comparisons on data sets with known generative factors, showing competitive results relative to state-of-the-art techniques. We further study the trade off between disentanglement and reconstruction on more-difficult data sets with unknown generative factors, where we expect improved reconstructions due to the flexibility of the WAE paradigm.
PAC-Bayesian Randomized Value Function with Informative Prior
Authors: Yuankun Jiang, Chenglin Li, Junni Zou, Wenrui Dai, Hongkai Xiong
Date: 2021-01-01 00:00:00
Tasks: Reinforcement Learning (RL)

Randomized value function has been shown as an effective exploration strategy for reinforcement learning (RL), which samples from a learned estimation of the distribution over the randomized Q-value function and then selects the optimal action. However, value function methods are known to suffer from value estimation error. Overfitting of value function is one of the main reasons to estimation error. To address this, in this paper, we propose a Bayesian linear regression with informative prior (IP-BLR) operator to leverage the data-dependent prior in the learning process of randomized value function, which can leverage the statistics of training results from previous iterations. We theoretically derive a generalization error bound for the proposed IP-BLR operation at each learning iteration based on PAC-Bayesian theory, showing a trade-off between the distribution obtained by IP-BLR and the informative prior. Since the optimal posterior that minimizes this generalization error bound is intractable, we alternatively develop an adaptive noise parameter update algorithm to balance this trade-off. The performance of the proposed IP-BLR deep Q-network (DQN) with adaptive noise parameter update is validated through some classical control tasks. It demonstrates that compared to existing methods using non-informative prior, the proposed IP-BLR DQN can achieve higher accumulated rewards in fewer interactions with the environment, due to the capabilities of more accurate value function approximation and better generalization.
Rethinking Compressed Convolution Neural Network from a Statistical Perspective
Authors: Feiqing Huang, Yuefeng Si, Guodong Li
Date: 2021-01-01 00:00:00
Tasks: Tensor Decomposition

Many designs have recently been proposed to improve the model efficiency of convolutional neural networks (CNNs) at a fixed resource budget, while there is a lack of theoretical analysis to justify them. This paper first formulates CNNs with high-order inputs into statistical models, which have a special "Tucker-like" formulation. This makes it possible to further conduct the sample complexity analysis to CNNs as well as compressed CNNs via tensor decomposition. Tucker and CP decompositions are commonly adopted to compress CNNs in the literature. The low rank assumption is usually imposed on the output channels, which according to our study, may not be beneficial to obtain a computationally efficient model while a similar accuracy can be maintained. Our finding is further supported by ablation studies on CIFAR10, SVNH and UCF101 datasets.
DQSGD: DYNAMIC QUANTIZED STOCHASTIC GRADIENT DESCENT FOR COMMUNICATION-EFFICIENT DISTRIBUTED LEARNING
Authors: Guangfeng Yan, Shao-Lun Huang, Tian Lan, Linqi Song
Date: 2021-01-01 00:00:00
Tasks: Quantization

Gradient quantization is widely adopted to mitigate communication costs in distributed learning systems. Existing gradient quantization algorithms often rely on design heuristics and/or empirical evidence to tune the quantization strategy for different learning problems. To the best of our knowledge, there is no theoretical framework characterizing the trade-off between communication cost and model accuracy under dynamic gradient quantization strategies. This paper addresses this issue by proposing a novel dynamic quantized SGD (DQSGD) framework, which enables us to optimize the quantization strategy for each gradient descent step by exploring the trade-off between communication cost and modeling error. In particular, we derive an upper bound, tight in some cases, of the modeling error for arbitrary dynamic quantization strategy. By minimizing this upper bound, we obtain an enhanced quantization algorithm with significantly improved modeling error under given communication overhead constraints. Besides, we show that our quantization scheme achieves a strengthened communication cost and model accuracy trade-off in a wide range of optimization models. Finally, through extensive experiments on large-scale computer vision and natural language processing tasks on CIFAR-10, CIFAR-100, and AG-News datasets, respectively. we demonstrate that our quantization scheme significantly outperforms the state-of-the-art gradient quantization methods in terms of communication costs.
Secure Federated Learning of User Verification Models
Authors: Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling
Date: 2021-01-01 00:00:00
Tasks: Federated Learning

We consider the problem of training User Verification (UV) models in federated setup, where the conventional loss functions are not applicable due to the constraints that each user has access to the data of only one class and user embeddings cannot be shared with the server or other users. To address this problem, we propose Federated User Verification (FedUV), a framework for private and secure training of UV models. In FedUV, users jointly learn a set of vectors and maximize the correlation of their instance embeddings to a secret user-defined linear combination of those vectors. We show that choosing the linear combinations from the codewords of an error-correcting code allows users to collaboratively train the model without revealing their embedding vectors. 
We present experimental results for user verification with voice, face and handwriting data and show that FedUV is on par with existing approaches, while providing security by not sharing the embeddings with other users or the server.
Multi-scale Network Architecture Search for Object Detection
Authors: Yuxin Yue, Quanquan Li, Yujie Wang
Date: 2021-01-01 00:00:00
Tasks: feature selection, Object, object-detection, Object Detection

Many commonly-used detection frameworks aim to handle the multi-scale object detection problem. The input image is always encoded to multi-scale features and objects grouped by scale range are assigned to the corresponding features. However, the design of multi-scale feature production is quite hand-crafted or partially automatic. In this paper, we show that more possible architectures of encoder network and different strategies of feature utilization can lead to superior performance. Specifically, we propose an efficient and effective multi-scale network architecture search method (MSNAS) to improve multi-scale object detection by jointly optimizing network stride search of the encoder and appropriate feature selection for detection heads. We demonstrate the effectiveness of the method on COCO dataset and obtain a remarkable performance gain with respect to the original Feature Pyramid Networks.
Non-Markovian Predictive Coding For Planning In Latent Space
Authors: Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon
Date: 2021-01-01 00:00:00
Tasks: Model-based Reinforcement Learning, Representation Learning

High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments. In order to handle high-dimensional sensory inputs, existing MBRL approaches use representation learning to map high-dimensional observations into a lower-dimensional latent space that is more amenable to dynamics estimation and planning. Crucially, the task-relevance and predictability of the learned representations play critical roles in the success of planning in latent space. In this work, we present Non-Markovian Predictive Coding (NMPC), an information-theoretic approach for planning from high-dimensional observations with two key properties: 1) it formulates a mutual information objective that prioritizes the encoding of task-relevant components of the environment; and 2) it employs a recurrent neural network capable of modeling non-Markovian latent dynamics. To demonstrate NMPC’s ability to prioritize task-relevant information, we evaluate our new model on a challenging modification of standard DMControl tasks where the DMControl background is replaced with natural videos, containing complex but irrelevant information to the planning task. Our experiments show that NMPC is superior to existing methods in the challenging complex-background setting while remaining competitive with current state-of-the-art MBRL models in the standard setting.
Sparse matrix products for neural network compression
Authors: Luc Giffon, Hachem Kadri, Stephane Ayache, Ronan Sicre, Thierry Artieres
Date: 2021-01-01 00:00:00
Tasks: Neural Network Compression

Over-parameterization of neural networks is a well known issue that comes along with their great performance. Among the many approaches proposed to tackle this problem, low-rank tensor decompositions are largely investigated to compress deep neural networks. Such techniques rely on a low-rank assumption of the layer weight tensors that does not always hold in practice. Following this observation, this paper studies sparsity inducing techniques to build new sparse matrix product layer for high-rate neural networks compression. Specifically, we explore recent advances in sparse optimization to replace each layer's weight matrix, either convolutional or fully connected, by a product of sparse matrices. Our experiments validate that our approach provides a better compression-accuracy trade-off than most popular low-rank-based compression techniques.
Cross-Attention Guided Network for Visual Tracking
Authors: Yue Zhao, Zhibin Yu
Date: 2021-01-01 00:00:00
Tasks: Visual Tracking

Most traditional Siamese trackers are used to process the feature information of the template branch and search branch respectively, and to correlate with each other to get the location of the max response which regarded as the target location. However, it is difficult for these traditional methods to reduce the influence of the similar object and background clutters because of ignoring the information between target template and search frame. Accordingly, a novel cross-attention guided network (SiamCAN), which involving cross-channel attention and self-spatial attention to learn discriminative object representations, is proposed to solve the above problem in this paper. Here, the channel attention of target template is introduced to guide the feature learning for search branch, and then the self-spatial attention is used to localize the informative part location after the correlation processing. Moreover, in order to get more accurate target estimation, an anchor-free mechanism and a distance-IoU (DIoU) loss are applied to minimize the distance between the center points of the ground-truth bounding boxes and predicted boundary boxes in the model training process. The proposed method achieves state-of-the-art performance on four visual tracking benchmarks including UAV123, OTB100, VOT2018 and VOT2019, outperforming the strong baseline, SiamRPN++, by 0.417 $\displaystyle \rightarrow$ 0.445 and 0.292 $\displaystyle \rightarrow$ 0.323 EAO on VOT2018 and VOT2019.
ON NEURAL NETWORK GENERALIZATION VIA PROMOTING WITHIN-LAYER ACTIVATION DIVERSITY
Authors: Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj
Date: 2021-01-01 00:00:00
Tasks: Diversity

During the last decade, neural networks have been intensively used to tackle various problems and they have often led to state-of-the-art results. These networks are composed of multiple jointly optimized layers arranged in a hierarchical structure. At each layer, the aim is to learn to extract hidden patterns needed to solve the problem at hand and forward it to the next layers. In the standard form, a neural network is trained with gradient-based optimization, where the errors are back-propagated from the last layer back to the first one. Thus at each optimization step, neurons at a given layer receive feedback from neurons belonging to higher layers of the hierarchy. In this paper, we propose to complement this traditional 'between-layer' feedback with additional 'within-layer' feedback to encourage diversity of the activations within the same layer. To this end, we measure the pairwise similarity between the outputs of the neurons and use it to model the layer's overall diversity. By penalizing similarities and promoting diversity, we encourage each neuron to learn a distinctive representation and, thus, to enrich the data representation learned within the layer and to increase the total capacity of the model. We theoretically study how the within-layer activation diversity affects the generalization performance of a neural network in a supervised context and we prove that increasing the diversity of hidden activations reduces the estimation error. In addition to the theoretical guarantees, we present an empirical study confirming that the proposed approach enhances the performance of neural networks.
Factored Action Spaces in Deep Reinforcement Learning
Authors: Thomas Pierrot, Valentin Macé, Jean-Baptiste Sevestre, Louis Monier, Alexandre Laterre, Nicolas Perrin, Karim Beguir, Olivier Sigaud
Date: 2021-01-01 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, Dota 2, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft

Very large action spaces constitute a critical challenge for deep Reinforcement Learning (RL) algorithms. An existing approach consists in splitting the action space into smaller components and choosing either independently or sequentially actions in each dimension. This approach led to astonishing results for the StarCraft and Dota 2 games, however it remains underexploited and understudied. In this paper, we name this approach Factored Actions Reinforcement Learning (FARL) and study both its theoretical impact and practical use. Notably, we provide a theoretical analysis of FARL on the Proximal Policy Optimization (PPO) and Soft Actor Critic (SAC) algorithms and evaluate these agents in different classes of problems. We show that FARL is a very versatile and efficient approach to combinatorial and continuous control problems.
Indirect Supervision to Mitigate Perturbations
Authors: Mayank Kumar Kundalwal, Azad Singh, Deepak Mishra
Date: 2021-01-01 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Vulnerability of state-of-the-art computer vision models to image perturbations has drawn considerable attention recently. Often these perturbations are imperceptible to humans because they target the perception of deep neural networks (DNNs) employed in the corresponding computer vision task. Recent studies have revealed that DNNs, which are unable to handle targeted perturbation often fail to handle untargeted perturbations as well such as Gaussian noise. Various techniques in past have been explored to mitigate both these types of perturbations ranging from classical preprocessing to current supervised and self-supervised deep discriminative and generative models. However, a common challenge with most of these techniques is that they approach the problem from a quality enhancement point of view, which is primarily driven by human perception. In addition, the supervised models require a large volume of gold standard unperturbed data, whereas others fail to take into account the feedback of the targeted downstream DNN. We propose to model this problem in indirect supervision framework, where we assume that the gold standard data is missing, however, a variable dependent on it is available and the dependency of the observed variable is stated by the considered downstream DNN. The proposed method maintains the advantages of supervised models while relaxing the requirement of gold standard unperturbed data. To prove its utility, we conduct several experiments on various network architectures for downstream tasks of classification and medical image segmentation. We used MNIST, CIFAR-10-C and ISIC skin lesion dataset in our experiments. In all the experiments, a considerable restoration in the performance of the considered downstream model is observed.
A Near-Optimal Recipe for Debiasing Trained Machine Learning Models
Authors: Ibrahim Alabdulmohsin, Mario Lucic
Date: 2021-01-01 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

We present an efficient and scalable algorithm for debiasing trained models, including deep neural networks (DNNs), which we prove to be near-optimal by bounding its excess Bayes risk.  Unlike previous black-box reduction methods to cost-sensitive classification rules, the proposed algorithm operates on models that have been trained without having to retrain the model. Furthermore, as the algorithm is based on projected stochastic gradient descent (SGD), it is particularly attractive for deep learning applications. We empirically validate the proposed algorithm on standard benchmark datasets across both classical algorithms and modern DNN architectures and demonstrate that it outperforms previous post-processing approaches for unbiased classification.
Attacking Few-Shot Classifiers with Adversarial Support Sets
Authors: Elre Talea Oldewage, John F Bronskill, Richard E Turner
Date: 2021-01-01 00:00:00
Tasks: Few-Shot Learning, General Classification, Meta-Learning

Few-shot learning systems, especially those based on meta-learning, have recently made significant advances, and are now being considered for real world problems in healthcare, personalization, and science. In this paper, we examine the robustness of such deployed few-shot learning systems when they are fed an imperceptibly perturbed few-shot dataset, showing that the resulting predictions on test inputs can become worse than chance. This is achieved by developing a novel Adversarial Support Set Attack which crafts an adversarial set of examples. When even a small subset of adversarial data points is inserted into the support set of a meta-learner, accuracy is significantly reduced. For example, the average classification accuracy of CNAPs on the Aircraft dataset in the META-DATASET benchmark drops from 69.2% to 9.1% when only 20% of the support set is poisoned by imperceptible perturbations. We evaluate the new attack on a variety of few-shot classification algorithms including MAML, prototypical networks, and CNAPs, on both small scale (miniImageNet) and large scale (META-DATASET) few-shot classification problems. Interestingly, adversarial support sets produced by attacking a meta-learning based few-shot classifier can also reduce the accuracy of a fine-tuning based few-shot classifier when both models use similar feature extractors.
Combining Imitation and Reinforcement Learning with Free Energy Principle
Authors: Ryoya Ogishima, Izumi Karino, Yasuo Kuniyoshi
Date: 2021-01-01 00:00:00
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Imitation Learning (IL) and Reinforcement Learning (RL) from high dimensional sensory inputs are often introduced as separate problems, but a more realistic problem setting is how to merge the techniques so that the agent can reduce exploration costs by partially imitating experts at the same time it maximizes its return. Even when the experts are suboptimal (e.g. Experts learned halfway with other RL methods or human-crafted experts), it is expected that the agent outperforms the suboptimal experts’ performance. In this paper, we propose to address the issue by using and theoretically extending Free Energy Principle, a unified brain theory that explains perception, action and model learning in a Bayesian probabilistic way. Our results show that our approach achieves at least equivalent or better performance than standard IL or RL in visual control tasks with sparse rewards.
On the Importance of Distraction-Robust Representations for Robot Learning
Authors: Andy Wang, Antoine Cully
Date: 2021-01-01 00:00:00
Tasks: Decoder, Dimensionality Reduction, Representation Learning

Representation Learning methods can allow the application of Reinforcement Learning algorithms when a high dimensionality in a robot's perceptions would otherwise prove prohibitive. Consequently, unsupervised Representation Learning components often feature in robot control algorithms that assume high-dimensional camera images as the principal source of information.
In their design and performance, these algorithms often benefit from the controlled nature of the simulation or laboratory conditions they are evaluated in. However, these settings fail to acknowledge the stochasticity of most real-world environments.
In this work, we introduce the concept of Distraction-Robust Representation Learning. We argue that environment noise and other distractions require learned representations to encode the robot's expected perceptions rather than the observed ones. Our experimental evaluations demonstrate that representations learned with a traditional dimensionality reduction algorithm are strongly susceptible to distractions in a robot's environment.
We propose an Encoder-Decoder architecture that produces representations that allow the learning outcomes of robot control tasks to remain unaffected by these distractions.
Zero-shot Transfer Learning for Gray-box Hyper-parameter Optimization
Authors: Hadi Samer Jomaa, Lars Schmidt-Thieme, Josif Grabocka
Date: 2021-01-01 00:00:00
Tasks: Bayesian Optimization, Transfer Learning

Zero-shot hyper-parameter optimization refers to the process of selecting hyper- parameter configurations that are expected to perform well for a given dataset upfront, without access to any observations of the losses of the target response. Existing zero-shot approaches are posed as initialization strategies for Bayesian Optimization and they often rely on engineered meta-features to measure dataset similarity, operating under the assumption that the responses of similar datasets behaves similarly with respect to the same hyper-parameters. Solutions for zero- shot HPO are embarrassingly parallelizable and thus can reduce vastly the required wallclock time of learning a single model. We propose a very simple HPO model called Gray-box Zero(0)-Shot Initialization (GROSI) as a conditional parametric surrogate that learns a universal response model by exploiting the relationship between the hyper-parameters and the dataset meta-features directly. In contrast to existing HPO solutions, we achieve transfer of knowledge without engineered meta- features, but rather through a shared model that is trained simultaneously across all datasets. We design and optimize a novel loss function that allows us to regress from the dataset/hyper-parameter pair unto the response. Experiments on 120 datasets demonstrate the strong performance of GROSI, compared to conventional initialization strategies. We also show that by fine-tuning GROSI to the target dataset, we can outperform state-of-the-art sequential HPO algorithms.