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Average Reward Reinforcement Learning with Monotonic Policy Improvement | 1 INTRODUCTION . The goal of Reinforcement Learning ( RL ) is to build agents that can learn high-performing behaviors through trial-and-error interactions with the environment . Broadly speaking , modern RL tackles two kinds of problems : episodic tasks and continuing tasks . In episodic tasks , the agent-environment ... | This paper advocates for the use of the average reward objective in long horizon, continual reinforcement learning settings, and it examines whether a limiting argument for the discount is sufficient for extending theoretical results from the discounted setting to the average-reward setting. In the case of policy optim... | SP:1510815ddfb253f977b3ce9b53ea02b4044ffb90 |
iPTR: Learning a representation for interactive program translation retrieval | Program translation contributes to many real world scenarios , such as porting codebases written in an obsolete or deprecated language to a modern one or reimplementing existing projects in one ’ s preferred programming language . Existing data-driven approaches either require large amounts of training data or neglect ... | This work proposes a retrieval-based approach for program translation. Existing ML/DL models for program translation typically design a decoder to directly generate the code in the target language. On the contrary, this work designs iPTR, which first computes a feature representation of the target code, then retrieves ... | SP:d10de5e45dc6a79787c2167e2ab7fd1dc9c9a4e1 |
iPTR: Learning a representation for interactive program translation retrieval | Program translation contributes to many real world scenarios , such as porting codebases written in an obsolete or deprecated language to a modern one or reimplementing existing projects in one ’ s preferred programming language . Existing data-driven approaches either require large amounts of training data or neglect ... | This paper seeks to solve program translation problem through code retrieval. It proposes an interactive code retrieval system, called iPTR to perform cross-language retrieval with minimum code pairs. The method extracts textual features and structural features from code and transform the features into target-language ... | SP:d10de5e45dc6a79787c2167e2ab7fd1dc9c9a4e1 |
Relevance Attack on Detectors | 1 INTRODUCTION . Adversarial attacks ( Szegedy et al . ( 2014 ) ; Goodfellow et al . ( 2015 ) ; Carlini & Wagner ( 2017 ) ; Mądry et al . ( 2017 ) ; Baluja & Fischer ( 2017 ) ; Su et al . ( 2019 ) ) have revealed the fragility of Deep Neural Networks ( DNNs ) by fooling them with elaborately-crafted imperceptible pert... | This work proposes to attack object detectors by targeting their relevance maps of the different detected objects. The proposed RAD attack shows better black box transferability across different detectors on MSCOCO dataset. The relevance maps are calculated based on SGLRP act as an attention mechanism to the attack to ... | SP:7414e95e25417652c729da914dff9d116c083282 |
Relevance Attack on Detectors | 1 INTRODUCTION . Adversarial attacks ( Szegedy et al . ( 2014 ) ; Goodfellow et al . ( 2015 ) ; Carlini & Wagner ( 2017 ) ; Mądry et al . ( 2017 ) ; Baluja & Fischer ( 2017 ) ; Su et al . ( 2019 ) ) have revealed the fragility of Deep Neural Networks ( DNNs ) by fooling them with elaborately-crafted imperceptible pert... | This paper presents a method for adversarial attacks on object detectors by exploiting relevance maps that are originally intended for model interpretation. Unlike most of the existing methods that attack detection scores directly, the proposed approach focuses on suppressing the relevance map associated with target ob... | SP:7414e95e25417652c729da914dff9d116c083282 |
CoCon: A Self-Supervised Approach for Controlled Text Generation | 1 INTRODUCTION . Transformer-based ( Vaswani et al. , 2017 ; Tay et al. , 2020 ) pretrained language models ( LMs ) have led a wave of new advances in natural language processing tasks as a means to extract contextualized word embeddings ( Devlin et al. , 2018 ; Dai et al. , 2019b ; Yang et al. , 2019 ) and as text gen... | The paper proposed a way to control the content output of a DNN-based language model (GPT-2 in the experiment, but not limited to it). It places an layer (CoCon) that can take an arbitrary phrase as the hint after generating the embedding but before generating the text. Experiments showed that the control is effective ... | SP:a6afe4079b1d42b36701f6c023b003b7a0e49a55 |
CoCon: A Self-Supervised Approach for Controlled Text Generation | 1 INTRODUCTION . Transformer-based ( Vaswani et al. , 2017 ; Tay et al. , 2020 ) pretrained language models ( LMs ) have led a wave of new advances in natural language processing tasks as a means to extract contextualized word embeddings ( Devlin et al. , 2018 ; Dai et al. , 2019b ; Yang et al. , 2019 ) and as text gen... | This paper tackles the problem of controlled text generation by converting it into a conditional text generation similar to (Keskar et al.19). It proposes an architectural modification to the transformer LM used in GPT2, Specifically, a CoCon layer is added as a separate transformer block in the middle allowing self-... | SP:a6afe4079b1d42b36701f6c023b003b7a0e49a55 |
ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations | 1 INTRODUCTION . Convolution Neural Networks ( CNNs ) have resulted in several breakthroughs across various disciplines of deep learning , especially for their effectiveness in extracting complex features . However , these models demand significantly high computational power , making it hard to use them on lowmemory ha... | The paper provides a budget-aware regularizer method to train the network to be pruned. Existing methods based-on the regularizer method suffer from satisfying the user-specified constraints and resort to trial-and-error approach. The authors leverage logistic function and continuous heaviside function for continuous r... | SP:22ec87bbcb57e40a3485af92cb5aa5f6ab8968f6 |
ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations | 1 INTRODUCTION . Convolution Neural Networks ( CNNs ) have resulted in several breakthroughs across various disciplines of deep learning , especially for their effectiveness in extracting complex features . However , these models demand significantly high computational power , making it hard to use them on lowmemory ha... | This paper presents a new method for structure pruning called ChipNet. The ChipNet employs continuous Heaviside function with commonly used logistic curve and crispness loss to estimate sparsity masks. A combination of above three components is helpful to obtain approximately discrete solutions for a continuous optimiz... | SP:22ec87bbcb57e40a3485af92cb5aa5f6ab8968f6 |
Consistent Instance Classification for Unsupervised Representation Learning | 1 INTRODUCTION . Learning good representations from unlabeled images is a land-standing and challenging problem . The mainstream methods include : generative modeling ( Hinton et al. , 2006 ; Kingma & Welling , 2014 ) , colorization ( Zhang et al. , 2016 ) , transformation or spatial relation prediction ( Doersch et al... | This paper proposes adding an additional loss term to instance classification, within the context of self-supervised pre-training. Specifically, in addition to the standard classification loss term that views each image (and its augmentation) as a separate category, a second loss term is added, which is 1 minus the in... | SP:28739f0fcb4a9fb3c661dee4008443d34c25d6d6 |
Consistent Instance Classification for Unsupervised Representation Learning | 1 INTRODUCTION . Learning good representations from unlabeled images is a land-standing and challenging problem . The mainstream methods include : generative modeling ( Hinton et al. , 2006 ; Kingma & Welling , 2014 ) , colorization ( Zhang et al. , 2016 ) , transformation or spatial relation prediction ( Doersch et al... | This paper studies the instance classification solution for an unsupervised representation learning problem. Particularly, this paper proposes an additional consistency loss that is simultaneously optimized with classification loss, in order to penalize feature dissimilarity between augmented views of the same instance... | SP:28739f0fcb4a9fb3c661dee4008443d34c25d6d6 |
ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity | Adversarial attacks pose a major challenge for modern deep neural networks . Recent advancements show that adversarially robust generalization requires a large amount of labeled data for training . If annotation becomes a burden , can unlabeled data help bridge the gap ? In this paper , we propose ARMOURED , an adversa... | This paper considers training an adversarially robust model in a semi-supervised setting. The authors propose an ensemble-based algorithm for this goal. The algorithm uses a regularization term to induce diversity in the ensemble. The algorithm also leverages the idea of multi-view training in semi-supervised learning ... | SP:99532b87b80a2915e725473a1feecff213def1f5 |
ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity | Adversarial attacks pose a major challenge for modern deep neural networks . Recent advancements show that adversarially robust generalization requires a large amount of labeled data for training . If annotation becomes a burden , can unlabeled data help bridge the gap ? In this paper , we propose ARMOURED , an adversa... | This work introduces ARMOURED, a new method for learning models that are robust against adversarial attacks. The method uses **multi-view**-learning (as in using multiple models with different parameters to cast their votes/views on a given sample), semi-supervised learning to pseudo-label new data based on a consensus... | SP:99532b87b80a2915e725473a1feecff213def1f5 |
HalentNet: Multimodal Trajectory Forecasting with Hallucinative Intents | 1 INTRODUCTION . The ability to forecast trajectories of dynamic agents is essential for a variety of autonomous systems such as self-driving vehicles and social robots . It enables an autonomous system to foresee adverse situations and adjust motion planning accordingly to prefer better alternatives . Because agents c... | The present work considers the problem of multi-agent trajectory prediction. Its main contribution is incorporating generative augmentation losses for improving the quality of a trajectory predictor. This is achieved by allowing trajetcory predictors to model intent as an unobserved latent variable in the model and usi... | SP:202c1ae8c7f18f8cc234f8c8cc5d9f89ae9f43d0 |
HalentNet: Multimodal Trajectory Forecasting with Hallucinative Intents | 1 INTRODUCTION . The ability to forecast trajectories of dynamic agents is essential for a variety of autonomous systems such as self-driving vehicles and social robots . It enables an autonomous system to foresee adverse situations and adjust motion planning accordingly to prefer better alternatives . Because agents c... | The paper designed a framework for motion forecasting (trajectory prediction), with emphasis on multimodal distribution modeling and generalization. Specifically, they use latent code to model agent's intents. This latent code combined with historical trajectories and map were used to generate future trajectories, whic... | SP:202c1ae8c7f18f8cc234f8c8cc5d9f89ae9f43d0 |
A Primal Approach to Constrained Policy Optimization: Global Optimality and Finite-Time Analysis | √ T ) convergence rate to the global optimal policy in the constrained policy set and an O ( 1/ √ T ) error bound on constraint satisfaction . This is the first finite-time analysis of SRL algorithms with global optimality guarantee . Our empirical results demonstrate that CRPO can outperform the existing primal-dual b... | This paper considers safe RL through solving a constrained MDP in (1). The authors proposed a primal method which alternates between maximizing the reward and minimizing the constraint violation. The convergence rate of the algorithm is also provided under standard settings, e.g., bounded reward, iid samples, etc.. Int... | SP:49ac2a2ac51be0f41d095bcdf204b6da967b98b2 |
A Primal Approach to Constrained Policy Optimization: Global Optimality and Finite-Time Analysis | √ T ) convergence rate to the global optimal policy in the constrained policy set and an O ( 1/ √ T ) error bound on constraint satisfaction . This is the first finite-time analysis of SRL algorithms with global optimality guarantee . Our empirical results demonstrate that CRPO can outperform the existing primal-dual b... | This paper proposes a new method for constrained MDP. The proposed method does not require primal-dual formulation and is easy to implement given the availability of state-of-the-art policy optimization solvers. When the natural policy gradient is used as the policy optimizer, a sublinear global convergence rate is ... | SP:49ac2a2ac51be0f41d095bcdf204b6da967b98b2 |
Extract Local Inference Chains of Deep Neural Nets | 1 INTRODUCTION . Deep neural networks ( DNNs ) greatly reshape a variety of tasks — object classification , semantic segmentation , natural language processing , speech recognition , robotics , etc . Despite its success on a vast majority of clean data , DNNs are also well-known to be sensitive to small amounts of adve... | The paper proposes a method called NeuroChains for extracting a sub-network from a deep neural network (DNN) that can accurately match the outputs of the full network for inputs in a small region of the input space. The goal is to be able to explain the important steps that a DNN takes to get from inputs in a small reg... | SP:351f676d2fa2b3b216ed7b5ebbb3b058eef5cb1c |
Extract Local Inference Chains of Deep Neural Nets | 1 INTRODUCTION . Deep neural networks ( DNNs ) greatly reshape a variety of tasks — object classification , semantic segmentation , natural language processing , speech recognition , robotics , etc . Despite its success on a vast majority of clean data , DNNs are also well-known to be sensitive to small amounts of adve... | The submission considers the task of extracting a small sub-network of a pre-trained neural network that can explain a local region of the data space (a small number of the same classes). The resulting sub-network can be interpreted as the inference path moving from the input to the prediction and the filters and assoc... | SP:351f676d2fa2b3b216ed7b5ebbb3b058eef5cb1c |
MSFM: Multi-Scale Fusion Module for Object Detection | Feature fusion is beneficial to object detection tasks in two folds . On one hand , detail and position information can be combined with semantic information when high and low-resolution features from shallow and deep layers are fused . On the other hand , objects can be detected in different scales , which improves th... | In this paper, the authors study the problem of scale-friendly feature fusion for object detection. Specifically, the authors propose to process features at each layer of a feature pyramid network at multiple scales and fuse them back into a single scale. To be specific, they resize features at a layer into multiple sc... | SP:81704b6fef077ebf35d792cdb2104722c207bb06 |
MSFM: Multi-Scale Fusion Module for Object Detection | Feature fusion is beneficial to object detection tasks in two folds . On one hand , detail and position information can be combined with semantic information when high and low-resolution features from shallow and deep layers are fused . On the other hand , objects can be detected in different scales , which improves th... | The paper proposes a multi-scale feature fusion block and inserts the block into ResNet backbones for object detection. It is very similar to the inception block in IneceptionNets. The only difference is the proposed feature fusion contains feature map upsampling and downsampling (resize and resize^{-1}) for different ... | SP:81704b6fef077ebf35d792cdb2104722c207bb06 |
Adaptive Universal Generalized PageRank Graph Neural Network | 1 INTRODUCTION . Graph-centered machine learning has received significant interest in recent years due to the ubiquity of graph-structured data and its importance in solving numerous real-world problems such as semisupervised node classification and graph classification ( Zhu , 2005 ; Shervashidze et al. , 2011 ; Lü &... | In this paper, the authors proposed a generalized pagerank version of a graph neural network (GNN). The authors learn a weighted combination of higher powers of the graph adjacency matrix, with the weights themselves being learnable. This allows their method to generalize existing GNN methods that work well when there'... | SP:ffa69b99230ed18f02cbb7acb37cf3cd801ec908 |
Adaptive Universal Generalized PageRank Graph Neural Network | 1 INTRODUCTION . Graph-centered machine learning has received significant interest in recent years due to the ubiquity of graph-structured data and its importance in solving numerous real-world problems such as semisupervised node classification and graph classification ( Zhu , 2005 ; Shervashidze et al. , 2011 ; Lü &... | The paper proposes a new GNN architecture based on Generalized PageRank to handle two weakness in some existing GNNs: the difficulty of neighborhood aggregation on heterophilic graphs, and the oversmoothing problem when stacking GNN layers. The proposed GPR-GNN can be viewed as an extension of the Personalized PageRank... | SP:ffa69b99230ed18f02cbb7acb37cf3cd801ec908 |
TwinDNN: A Tale of Two Deep Neural Networks | 1 INTRODUCTION . Machine learning is one of the most popular fields in the current era . It is used in various ways , such as speech recognition , face recognition , medical diagnosis , etc . However , the problem is that the neural networks for machine learning applications Krizhevsky et al . ( 2012 ) ; He et al . ( 2... | The paper at hand discusses a compressed network inference scheme, where two networks are trained to solve a give classification task. One network aims at achieving a high accuracy, whereas the other network is a highly compressed network which is able to highly speed-up inference. The compression of the network is per... | SP:d90cf59a526832aad0436f9b6e168cb9c08568f8 |
TwinDNN: A Tale of Two Deep Neural Networks | 1 INTRODUCTION . Machine learning is one of the most popular fields in the current era . It is used in various ways , such as speech recognition , face recognition , medical diagnosis , etc . However , the problem is that the neural networks for machine learning applications Krizhevsky et al . ( 2012 ) ; He et al . ( 2... | This paper proposes a framework to accelerate DNN inference on small embedding systems using an extremely low bit network and a moderately quantized network jointly. The mechanism of the proposed work is to first compute the difference using top2 prediction scores from the compressed network to determine if the inferen... | SP:d90cf59a526832aad0436f9b6e168cb9c08568f8 |
Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness | 1 INTRODUCTION . Deep neural networks ( DNNs ) have achieved great success in image recognition ( Russakovsky et al. , 2015 ) , audio recognition ( Graves & Jaitly , 2014 ) , etc . However , as shown by ( Szegedy et al. , 2013 ) , DNNs are vulnerable to adversarial attacks , where slightly perturbed adversarial example... | The authors combine adversarial training with two methods that increase the entropy of the output distribution of neural networks (label smoothing and entropy maximization). The authors find that this combination of ideas increases adversarial robustness on standard benchmarks, especially in the regime of large perturb... | SP:a727adb9f6aec44cd84d176e54d7578ed9ab023a |
Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness | 1 INTRODUCTION . Deep neural networks ( DNNs ) have achieved great success in image recognition ( Russakovsky et al. , 2015 ) , audio recognition ( Graves & Jaitly , 2014 ) , etc . However , as shown by ( Szegedy et al. , 2013 ) , DNNs are vulnerable to adversarial attacks , where slightly perturbed adversarial example... | This paper investigates the complementary mechanisms of adversarial training and uncertainty promoting regularizers. In the field of adversarial machine learning, adversarial training as proposed by Madry et al. 2017 has been the common method. In the field of uncertainty regularization, maximum entropy and label smoot... | SP:a727adb9f6aec44cd84d176e54d7578ed9ab023a |
A Representational Model of Grid Cells' Path Integration Based on Matrix Lie Algebras | 1 INTRODUCTION . Imagine walking in the darkness . Purely based on your sense of self-motion , you can gain a sense of self-position by integrating the self movement - a process often referred to as path integration ( Darwin , 1873 ; Etienne & Jeffery , 2004 ; Hafting et al. , 2005 ; Fiete et al. , 2008 ; McNaughton et... | The authors develop a model for learning the observed responses of grid cells (GC) in the entorhinal cortex from the animal movement vectors. Their key assumption is that the GC activity vector rotates with the movement magnitude according to the Lie group formalism and the corresponding Lie group generator is also rot... | SP:ed01a0233c76094bf814ffeb5fcc10548a26e4bd |
A Representational Model of Grid Cells' Path Integration Based on Matrix Lie Algebras | 1 INTRODUCTION . Imagine walking in the darkness . Purely based on your sense of self-motion , you can gain a sense of self-position by integrating the self movement - a process often referred to as path integration ( Darwin , 1873 ; Etienne & Jeffery , 2004 ; Hafting et al. , 2005 ; Fiete et al. , 2008 ; McNaughton et... | The authors propose a simple recurrent network as a model of spatial navigation in the MEC/Hippocampal network. This model assumes that grid cells only regularly receive egocentric movement information, an important aspect for understanding the origin of these functional cell types in-vitro. Overall, this article shoul... | SP:ed01a0233c76094bf814ffeb5fcc10548a26e4bd |
DeeperGCN: Training Deeper GCNs with Generalized Aggregation Functions | 1 INTRODUCTION . The rise of availability of non-Euclidean data ( Bronstein et al. , 2017 ) has recently shed interest into the topic of Graph Convolutional Networks ( GCNs ) . GCNs provide powerful deep learning architectures for irregular data , like point clouds and graphs . GCNs have proven valuable for application... | The authors propose a generalized neighborhood message aggregation function for GNNs. The proposed choice of generalized aggregation functions is SoftMax and PowerMean, which generalizes Max and Mean functions and interpolates them. Additionally, they propose a variant of these two methods, which can also encompass th... | SP:f6277acaa779b79d58b41fdcbd2a5dc02697cc3f |
DeeperGCN: Training Deeper GCNs with Generalized Aggregation Functions | 1 INTRODUCTION . The rise of availability of non-Euclidean data ( Bronstein et al. , 2017 ) has recently shed interest into the topic of Graph Convolutional Networks ( GCNs ) . GCNs provide powerful deep learning architectures for irregular data , like point clouds and graphs . GCNs have proven valuable for application... | This work proposes a generalized aggregation function for graph neural networks. This generalized aggregation function can cover commonly used aggregation functions (i.e., mean, max, and sum) by particular setting of hyperparameters. Also, these hyperparameters can be learned with model in an end-to-end fashion instead... | SP:f6277acaa779b79d58b41fdcbd2a5dc02697cc3f |
Model information as an analysis tool in deep learning | 1 INTRODUCTION . The ultimate goal of many deep learning research has been improving performance on specific datasets , for example , aiming for superior classification accuracy on the ILSVRC challenge . We have witnessed super-human performance on tasks in vision and language processing , but we are still far from und... | The paper examines different use-cases of a quantity proposed in prior works which is said to capture the model information. It shows that this quantity behaves as expected overall. Quantifying the amount of information a deep neural network is a very interesting question for the community with both theoretical and pra... | SP:b7632acf4753f9670c244597cc702fcab680982e |
Model information as an analysis tool in deep learning | 1 INTRODUCTION . The ultimate goal of many deep learning research has been improving performance on specific datasets , for example , aiming for superior classification accuracy on the ILSVRC challenge . We have witnessed super-human performance on tasks in vision and language processing , but we are still far from und... | The central concept of this paper is model information, a description length of a discriminative model. The authors advocate usage of model information for analyzing several aspects in deep learning. In particular, they show how model information can be used to judge about difficulty of supervised tasks, domain similar... | SP:b7632acf4753f9670c244597cc702fcab680982e |
Constraining Latent Space to Improve Deep Self-Supervised e-Commerce Products Embeddings for Downstream Tasks | The representation of products in a e-commerce marketplace is a key aspect to be exploited when trying to improve the user experience on the site . A well known example of the importance of a good product representation are tasks such as product search or product recommendation . There is however a multitude of lesser ... | The authors study the problem of representation learning of marketplace products to apply in downstream tasks. More specifically, the authors extend the work of BYOL with a new objective function by adding a cross-entropy objective. The main hypothesis of this paper is that different products of the same browsing sessi... | SP:433a47ed8978997d213bc7072f66a4d62e2e92e3 |
Constraining Latent Space to Improve Deep Self-Supervised e-Commerce Products Embeddings for Downstream Tasks | The representation of products in a e-commerce marketplace is a key aspect to be exploited when trying to improve the user experience on the site . A well known example of the importance of a good product representation are tasks such as product search or product recommendation . There is however a multitude of lesser ... | This paper studies representation learning in an e-commerce setting. In particular, the paper explores the use of the recently proposed Bootstrap Your Own Latent (BYOL) framework to learn product representations. Rather than using different views of the same entity (as is done in the image domain), different products w... | SP:433a47ed8978997d213bc7072f66a4d62e2e92e3 |
Differentially Private Generative Models Through Optimal Transport | 1 INTRODUCTION . As the full value of data comes to fruition through a growing number of data-centric applications ( e.g . recommender systems ( Gomez-Uribe & Hunt , 2016 ) , personalized medicine ( Ho et al. , 2020 ) , face recognition ( Wang & Deng , 2020 ) , speech synthesis ( Oord et al. , 2016 ) , etc . ) , the im... | This paper presents a differentially private method for training a generative model. The proposed method takes advantage of the Sinkhorn divergence to achieve robustness against the hyperparameters' choices. The authors also introduce a cost function enabling the generative model to generate images associated with a sp... | SP:3286b16b5791520a5d0e11bb42a8f3f5e5b8604d |
Differentially Private Generative Models Through Optimal Transport | 1 INTRODUCTION . As the full value of data comes to fruition through a growing number of data-centric applications ( e.g . recommender systems ( Gomez-Uribe & Hunt , 2016 ) , personalized medicine ( Ho et al. , 2020 ) , face recognition ( Wang & Deng , 2020 ) , speech synthesis ( Oord et al. , 2016 ) , etc . ) , the im... | The paper proposes a method for training OT GANs using differentially private sinkhorn algorithm. The idea is very simple - train GANs with sinkhorn divergences and add Gaussian noise to the gradient of output wrt generated samples. So, the novelty by itself is minimal as sinkhorn GANs have previously been proposed. Th... | SP:3286b16b5791520a5d0e11bb42a8f3f5e5b8604d |
Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective | 1 INTRODUCTION . The recent development of deep networks significantly contributes to the success of computer vision . Thanks to many efforts by human designers , the performance of deep networks have been significantly boosted ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2014 ; Szegedy et al. , 2015 ; He et al.... | Training-free NAS is a promising direction. This work demonstrates that two theoretically inspired indicators: the spectrum of NTK and number of linear regions, strongly correlate with the network’s performance, and can be leveraged to reduce the search cost and decouple the analysis of the network’s trainability and e... | SP:bb6716468d3bd92b5e7de774aa89d5d52df461cb |
Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective | 1 INTRODUCTION . The recent development of deep networks significantly contributes to the success of computer vision . Thanks to many efforts by human designers , the performance of deep networks have been significantly boosted ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2014 ; Szegedy et al. , 2015 ; He et al.... | This paper introduces a searching framework of neural architectures ranking the candidates with two different metrics: the spectrum of NTK and the number of linear regions in the input space. These two metrics do not require the training of neural networks lightening the computational burdensome. Authors support their ... | SP:bb6716468d3bd92b5e7de774aa89d5d52df461cb |
Quickest change detection for multi-task problems under unknown parameters | 1 INTRODUCTION . Quickest Change Detection ( QCD ) problems arise naturally in settings where a latent state controls observable signals ( Basseville et al. , 1993 ) . In biology , it is applied in genomic sequencing ( Caron et al. , 2012 ) and in reliable healthcare monitoring ( Salem et al. , 2014 ) . In industry , i... | This paper studies the quickest change detection for Markovian data, when both the parameters of pre- and post-change distributions are unknown. The main contribution is a scalable algorithm that sequentially estimates the unknown parameters and plug-in to classical detection schemes to get the stopping rule. A notable... | SP:834881c613fe0577da917a8eb0104a37cb65a6bb |
Quickest change detection for multi-task problems under unknown parameters | 1 INTRODUCTION . Quickest Change Detection ( QCD ) problems arise naturally in settings where a latent state controls observable signals ( Basseville et al. , 1993 ) . In biology , it is applied in genomic sequencing ( Caron et al. , 2012 ) and in reliable healthcare monitoring ( Salem et al. , 2014 ) . In industry , i... | The author(s) propose a quickest change detection technique under known parameter scenario. They use a Markovian dynamics to generate the pre and post change-point distributions and use a Shirayev test-statistic based on the asymptotic behavior of the optimal delay under know parameters. The proposed methodology is val... | SP:834881c613fe0577da917a8eb0104a37cb65a6bb |
Unpacking Information Bottlenecks: Surrogate Objectives for Deep Learning | 1 Introduction The Information Bottleneck ( IB ) principle , introduced by Tishby et al . ( 2000 ) , proposes that training and generalization in deep neural networks ( DNNs ) can be explained by information-theoretic principles ( Tishby and Zaslavsky , 2015 ; Shwartz-Ziv and Tishby , 2017 ; Achille and Soatto , 2018a ... | The authors review the information bottleneck (IB) in the context of deep learning. They discuss the obstacles to applying the IB (and a deterministic variant, the DIB) to modern datasets, review approaches to doing so, and introduce their own scalable approach. Their approach introduces practical surrogate objectives ... | SP:387bec1eff17597a83cf7174a59e8082cd1b32ba |
Unpacking Information Bottlenecks: Surrogate Objectives for Deep Learning | 1 Introduction The Information Bottleneck ( IB ) principle , introduced by Tishby et al . ( 2000 ) , proposes that training and generalization in deep neural networks ( DNNs ) can be explained by information-theoretic principles ( Tishby and Zaslavsky , 2015 ; Shwartz-Ziv and Tishby , 2017 ; Achille and Soatto , 2018a ... | This paper provides several surrogates for the Information Bottleneck (IB) and Deterministic Information Bottleneck (DIB) loss functions that are more friendly to optimization. For the decoder uncertainty part, the authors show that using Dropout and cross-entropy loss provides an unbiased estimator for the decoder cro... | SP:387bec1eff17597a83cf7174a59e8082cd1b32ba |
Reinforcement Learning with Latent Flow | 1 Introduction . Reinforcement learning ( RL ) [ 41 ] holds the promise of enabling artificial agents to solve a diverse set of tasks in uncertain and unstructured environments . Recent developments in RL with deep neural networks have led to tremendous advances in autonomous decision making . Notable examples include ... | This work presents a simple technique (Flare) to incorporate explicit temporal information to enable effective RL policy learning in challenging continuous control environments using pixel-based state representations. The approach is inspired from recent advances in the video recognition approaches which employ optical... | SP:94f20c8d3fe45e4943690240a38f325fb377bf3a |
Reinforcement Learning with Latent Flow | 1 Introduction . Reinforcement learning ( RL ) [ 41 ] holds the promise of enabling artificial agents to solve a diverse set of tasks in uncertain and unstructured environments . Recent developments in RL with deep neural networks have led to tremendous advances in autonomous decision making . Notable examples include ... | The paper brings very little novelty or insight. It is unclear that the introduced architecture complexity worth marginal improvements (given high variance and only 5 random seeds) on 2 out of 11 tasks (5 from the main paper and 6 from appendix). This might be a good workshop paper but it clearly does not meet the high... | SP:94f20c8d3fe45e4943690240a38f325fb377bf3a |
PolarNet: Learning to Optimize Polar Keypoints for Keypoint Based Object Detection | 1 INTRODUCTION . Deep learning based object detection techniques have achieved remarkable success in many real-world applications ( Krizhevsky et al. , 2012 ; He et al. , 2016 ; Goodfellow et al. , 2016 ) . The mainstream stateof-the-art detectors are often based on the anchor-based detection methods ( Ren et al. , 201... | This paper proposes an anchor-free object detector that does bounding box regression in the polar coordinate instead of in the Cartesian coordinate. The motivation of doing this is because there are larger variance in offset vectors in the Cartesian coordinate (the extreme case when a point is on one of the four corner... | SP:5fbdd9020e410152a667b9c6551cab9cc3f14af3 |
PolarNet: Learning to Optimize Polar Keypoints for Keypoint Based Object Detection | 1 INTRODUCTION . Deep learning based object detection techniques have achieved remarkable success in many real-world applications ( Krizhevsky et al. , 2012 ; He et al. , 2016 ; Goodfellow et al. , 2016 ) . The mainstream stateof-the-art detectors are often based on the anchor-based detection methods ( Ren et al. , 201... | This paper proposes a new key-point based object detector, PolarNet, which predicts the distances between key-points and corner pairs (such as top-left and bottom-right pair or top-right and bottom-left pair) on polar coordinates. This is different from other key-point based object detectors such as FCOS which predicts... | SP:5fbdd9020e410152a667b9c6551cab9cc3f14af3 |
Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification | Graph neural network ( GNN ) and label propagation algorithm ( LPA ) are both message passing algorithms , which have achieved superior performance in semisupervised classification . GNN performs feature propagation by a neural network to make predictions , while LPA uses label propagation across graph adjacency matrix... | The paper proposes a new Graph Transformer (UniMP) based model with the motive of combining two powerful semi-supervised node classification techniques, GNN and LPA. Proposed Graph Transformer unifies feature and label propagation in conjunction to provide a better performance in semi-supervised node property classific... | SP:f0a5dede9342f691734c7279082a8898f54e9883 |
Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification | Graph neural network ( GNN ) and label propagation algorithm ( LPA ) are both message passing algorithms , which have achieved superior performance in semisupervised classification . GNN performs feature propagation by a neural network to make predictions , while LPA uses label propagation across graph adjacency matrix... | The authors proposed a unified message passing model to make a graph neural network to be able to incorporate both label propagation and feature propagation. Compared to previous work, the proposed model can also make use of partial label information in both training and inference stages. Experiments on three OGBN data... | SP:f0a5dede9342f691734c7279082a8898f54e9883 |
A frequency domain analysis of gradient-based adversarial examples | 1 INTRODUCTION . Recently , deep neural networks ( DNN ) have achieved great success in the field of image processing , but it was found that DNNs are vulnerable to some synthetic data called adversarial examples ( ( Szegedy et al. , 2013 ) , ( Kurakin et al. , 2016 ) ) . Adversarial examples are natural samples plus a... | This paper analyzes the frequency spectrum of adversarial perturbations during normal training. The authors show that the low frequency component (LFC) of adversarial perturbation is increasing during training, but it is not increasing fast enough, so the LFC of adversarial perturbation is not as dense as the input nat... | SP:8d9605171acd6c661b43648ad897a66665e5ea9f |
A frequency domain analysis of gradient-based adversarial examples | 1 INTRODUCTION . Recently , deep neural networks ( DNN ) have achieved great success in the field of image processing , but it was found that DNNs are vulnerable to some synthetic data called adversarial examples ( ( Szegedy et al. , 2013 ) , ( Kurakin et al. , 2016 ) ) . Adversarial examples are natural samples plus a... | This submission deals with understanding the gradient based adversarial examples. For this means, it analyzes the adversarial examples in the frequency domain, where it identifies that the ratio of high-frequency and low frequency parts is quite large in adversarial examples compared with the natural ones. As a result,... | SP:8d9605171acd6c661b43648ad897a66665e5ea9f |
Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets | 1 INTRODUCTION . Offline reinforcement learning ( RL ) , the task of training a sequential decision-making agent with a static offline dataset , holds the promise of a data-driven approach to reinforcement learning , thereby bypassing the laborious and often dangerous process of sample collection ( Levine et al. , 2020... | This paper considers the problem of policy learning in Markov Decision Process (MDP) from the combination of online and offline samples. The offline samples are generated by a behavior policy in the same MDP model, i.e., the behavior agent and the learning agent share the same state-action space. The learning procedure... | SP:b562599572cd55c95dd0f8e1449ba6c6cfdd14e1 |
Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets | 1 INTRODUCTION . Offline reinforcement learning ( RL ) , the task of training a sequential decision-making agent with a static offline dataset , holds the promise of a data-driven approach to reinforcement learning , thereby bypassing the laborious and often dangerous process of sample collection ( Levine et al. , 2020... | This paper proposes to deal with distribution shift problem between online and offline samples when the agent trained by offline data is fine-tuned with online interactions. Two mechanisms are introduced: (1) using two replay buffers for offline and online data respectively, and training the agent with data sampled fro... | SP:b562599572cd55c95dd0f8e1449ba6c6cfdd14e1 |
Online Continual Learning Under Domain Shift | 1 INTRODUCTION . Continual learning is a promising framework towards human-level intelligence by developing models that can continuously learn over time ( Ring , 1997 ; Parisi et al. , 2019 ) . Unlike traditional learning paradigms , continual learning methods observe a continuum of tasks and have to simultaneously per... | This paper investigates continual learning under domain shift and proposes a conditional invariant experience replay (CIER) method accordingly. CIER aims to retain old knowledge, acquire new information, and generalize to unseen domains. In particular, CIER uses adversarial training to correct the domain shift. Experim... | SP:256b4cc8d05297bb43d7259d1af082452f937024 |
Online Continual Learning Under Domain Shift | 1 INTRODUCTION . Continual learning is a promising framework towards human-level intelligence by developing models that can continuously learn over time ( Ring , 1997 ; Parisi et al. , 2019 ) . Unlike traditional learning paradigms , continual learning methods observe a continuum of tasks and have to simultaneously per... | This paper studies the domain adaptation problem when the source data comes from multiple domains continuously and the test domain for adaptation is unknown. The assumption used for domain shift is that the domain label would change the features but not the labels. So the main idea is to learn invariant representations... | SP:256b4cc8d05297bb43d7259d1af082452f937024 |
R-MONet: Region-Based Unsupervised Scene Decomposition and Representation via Consistency of Object Representations | 1 INTRODUCTION . In recent years , supervised object detection and segmentation ( He et al . ( 2017 ) ; Ren et al . ( 2015 ) ; Fan et al . ( 2019 ) ; Liao et al . ( 2001 ) ; Lin et al . ( 2017 ) ; Ronneberger et al . ( 2015 ) ) have made great progress with the extensive human labels . However , these supervised method... | This paper presents a variation of the MONet model where an additional Region Proposal Network generates bounding boxes for various objects in the scene. An additional loss is introduced during training to make the segmentations produced by the MONet segmenter consistent with the proposed bounding boxes. Results are de... | SP:0f566a0c7fbad17455e65c8befd25b317f67e177 |
R-MONet: Region-Based Unsupervised Scene Decomposition and Representation via Consistency of Object Representations | 1 INTRODUCTION . In recent years , supervised object detection and segmentation ( He et al . ( 2017 ) ; Ren et al . ( 2015 ) ; Fan et al . ( 2019 ) ; Liao et al . ( 2001 ) ; Lin et al . ( 2017 ) ; Ronneberger et al . ( 2015 ) ) have made great progress with the extensive human labels . However , these supervised method... | In this paper, the authors introduce a region-based approach for unsupervised scene decomposition. It extends the previous MONet by introducing the region-based self-supervised training. Instead of purely generating foreground masks in an RNN, they simultaneously predict the bounding boxes and segmentation masks using ... | SP:0f566a0c7fbad17455e65c8befd25b317f67e177 |
Cut out the annotator, keep the cutout: better segmentation with weak supervision | 1 INTRODUCTION . Automated image segmentation has seen rapid improvements with recent developments in deep learning ( Li et al. , 2018 ; Chen et al. , 2017 ; Milletari et al. , 2016 ) . Convolutional neural networks ( CNNs ) achieve high segmentation performance—but can require large , labeled training datasets . Acqui... | This paper presents weakly supervised framework for image segmentation tasks with limited annotated data. It first builds several labeling functions with limited annotation and then uses probability graph to fuse the labels. Last, the final output will be generated via CNN network. There are two key challenges in suc... | SP:63f8a0b2517c67e8d33f6522e9ee0402a2ed574b |
Cut out the annotator, keep the cutout: better segmentation with weak supervision | 1 INTRODUCTION . Automated image segmentation has seen rapid improvements with recent developments in deep learning ( Li et al. , 2018 ; Chen et al. , 2017 ; Milletari et al. , 2016 ) . Convolutional neural networks ( CNNs ) achieve high segmentation performance—but can require large , labeled training datasets . Acqui... | This paper studies few-shot segmentation for medical images where labeled data is hard to obtain. It proposed a stage-wise pipeline where pseudo labels are first proposed by noisy learning functions (LFs), then aggregated through a PGM, and finally used to train a segmentation model. The authors argue the two biggest ... | SP:63f8a0b2517c67e8d33f6522e9ee0402a2ed574b |
Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search) | 1 INTRODUCTION . Adaptive gradient methods such as AdaGrad ( Duchi et al. , 2011 ) , RMSProp ( Tieleman & Hinton , 2012 ) , AdaDelta ( Zeiler , 2012 ) , Adam ( Kingma & Ba , 2015 ) , and AMSGrad ( Reddi et al. , 2018 ) are popular optimizers for training deep neural networks ( Goodfellow et al. , 2016 ) . These methods... | This paper analyzes adaptive algorithms such as adagrad and AMSGrad in a finite-sum optimization problem. The proofs appear to treat this setting through online convex optimization and online-to-batch conversion. It is shown that both AdaGrad and AMSGrad improve when the individual losses are all minimized at the same ... | SP:5dadee976ef100a6bd77cd4e3a0f02b8376556af |
Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search) | 1 INTRODUCTION . Adaptive gradient methods such as AdaGrad ( Duchi et al. , 2011 ) , RMSProp ( Tieleman & Hinton , 2012 ) , AdaDelta ( Zeiler , 2012 ) , Adam ( Kingma & Ba , 2015 ) , and AMSGrad ( Reddi et al. , 2018 ) are popular optimizers for training deep neural networks ( Goodfellow et al. , 2016 ) . These methods... | This paper studies adaptive gradient methods under the over-parametrized settings, where the authors study the converge in the interpolation setting. In this setting, the optimal objective is 0. The authors show that the convergence rate is O(1/T). In addition, when the interpolation is approximately satisfied, the aut... | SP:5dadee976ef100a6bd77cd4e3a0f02b8376556af |
Abductive Knowledge Induction from Raw Data | 1 INTRODUCTION . Inductive bias , background knowledge , is an essential component in machine learning . Despite the success of data-driven end-to-end deep learning in many traditional machine learning tasks , it has been shown that incorporating domain knowledge is still necessary for some complex learning problems ( ... | In this paper, the author proposes Meta_abd which is a hybrid model that learns a deep recognition model and FOL rules the same time. The goal of this work is to learn FOL rules from raw data such as digits presented in image patches in an end-to-end fashion. The model is evaluated with 3 induction benchmarks associate... | SP:5be59a79316e4eeac5d6da1804b61a484a9ac2aa |
Abductive Knowledge Induction from Raw Data | 1 INTRODUCTION . Inductive bias , background knowledge , is an essential component in machine learning . Despite the success of data-driven end-to-end deep learning in many traditional machine learning tasks , it has been shown that incorporating domain knowledge is still necessary for some complex learning problems ( ... | In this paper, the authors have presented a framework that combines meta-interpretive learning and the abductive learning of neural networks. The high-level idea is to formulate a unified probabilistic interpretation of the entire algorithm so that both the inductive logic programming module and the neural network modu... | SP:5be59a79316e4eeac5d6da1804b61a484a9ac2aa |
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning | 1 INTRODUCTION . Distributional reinforcement learning ( DRL ) algorithms ( Jaquette , 1973 ; Sobel , 1982 ; White , 1988 ; Morimura et al. , 2010 ; Bellemare et al. , 2017 ) , different from the value based methods ( Watkins , 1989 ; Mnih et al. , 2013 ) which focus on the expectation of the return , characterize the ... | This paper proposes to use a monotonic quantile function for distributional RL by: (i) estimating the quantile values at the supported quantiles via a cumulative sum of non-negative incremental value, and (ii) interpolate the quantile values at the unsupported quantiles via linear combination of the nearest supported q... | SP:99a3db8fd1a8bbbde0338a4927c74ed71594aaf2 |
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning | 1 INTRODUCTION . Distributional reinforcement learning ( DRL ) algorithms ( Jaquette , 1973 ; Sobel , 1982 ; White , 1988 ; Morimura et al. , 2010 ; Bellemare et al. , 2017 ) , different from the value based methods ( Watkins , 1989 ; Mnih et al. , 2013 ) which focus on the expectation of the return , characterize the ... | This paper studies distributional RL and proposed two extensions. One is a method to enforce a non-decreasing ordering of quantile functions by a linear and non-negative increments. The other is extends the idea of DLTV which adds exploration bonus in action selection by using the random network distillation method, wh... | SP:99a3db8fd1a8bbbde0338a4927c74ed71594aaf2 |
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds | 1 INTRODUCTION . Robotic grasping of arbitrary objects is a challenging task . A robot needs to deal with objects it has never seen before , and generates a motion trajectory to grasp an object . Due to the complexity of the problem , majority works in the literature focus on bin-picking tasks , where top-down grasping... | This paper tackles the task of closed loop 6-DOF grasping of objects in simulation. The learned policy is a closed-loop policy, in that the gripper pose is continuously adjusted as the gripper approaches the object. The paper employs a combination of imitation learning, reinforcement learning, and auxiliary losses for ... | SP:228d410d5d9e184b9c920f56f0a6011db801b77b |
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds | 1 INTRODUCTION . Robotic grasping of arbitrary objects is a challenging task . A robot needs to deal with objects it has never seen before , and generates a motion trajectory to grasp an object . Due to the complexity of the problem , majority works in the literature focus on bin-picking tasks , where top-down grasping... | The paper targets the problem of closed-loop 6D robotic grasping with a parallel gripper based on RGB-D in-hand-camera images. The policy takes an aggregated point cloud (computed from image history) as input (using a PointNet++) and outputs the pose transformation of the gripper. There are several contributions in the... | SP:228d410d5d9e184b9c920f56f0a6011db801b77b |
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters | 1 INTRODUCTION . Language representation models , which are pre-trained on large-scale text corpus through unsupervised objectives like ( masked ) language modeling , such as BERT ( Devlin et al. , 2019 ) , GPT ( Radford et al. , 2018 ; 2019 ) , XLNet ( Yang et al. , 2019 ) , RoBERTa ( Liu et al. , 2019 ) and T5 ( Raff... | The authors propose a plug-in based adapter approach to allow for task specific parameter settings without updating the original pre-trained model which prevents the potential for catastrophic forgetting while also removing the need for separate models for separate tasks. The work seems to build off Houlsby 19 as bri... | SP:b01077ca319b19401044d1ec4a62f5dcaf0eeb21 |
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters | 1 INTRODUCTION . Language representation models , which are pre-trained on large-scale text corpus through unsupervised objectives like ( masked ) language modeling , such as BERT ( Devlin et al. , 2019 ) , GPT ( Radford et al. , 2018 ; 2019 ) , XLNet ( Yang et al. , 2019 ) , RoBERTa ( Liu et al. , 2019 ) and T5 ( Raff... | This submission proposes a general method (K-Adapter) for injecting knowledge (either factual or linguistic) into pre-trained language models. The key architectural property of the approach is that K-Adapters are isolated from one another, allowing the use of multiple adapters without interference. These K-Adapter mod... | SP:b01077ca319b19401044d1ec4a62f5dcaf0eeb21 |
Intelligent Matrix Exponentiation | 1 INTRODUCTION . Deep neural networks ( DNNs ) synthesize highly complex functions by composing a large number of neuronal units , each featuring a basic and usually 1-dimensional nonlinear activation function f : R1 → R1 . While highly successful in practice , this approach also has disadvantages . In a conventional D... | This paper explores matrix exponentiation as an alternative non-linearity in a neural network. The key idea is to compute an affine transform of the inputs, there by generating an nxn feature map, followed by applying a matrix exponential to this feature map, that is subsequently used for classification. The paper prov... | SP:e9ac6b48ea0d07d528dea9a78266a2474789e139 |
Intelligent Matrix Exponentiation | 1 INTRODUCTION . Deep neural networks ( DNNs ) synthesize highly complex functions by composing a large number of neuronal units , each featuring a basic and usually 1-dimensional nonlinear activation function f : R1 → R1 . While highly successful in practice , this approach also has disadvantages . In a conventional D... | This work proposes a novel machine learning architecture (or M-layer) that is in the form matrix exponential. This architecture can effectively model the interaction of feature components and learn multivariate polynomial functions and periodic functions. The architecture of the M-layer is well described. Its universal... | SP:e9ac6b48ea0d07d528dea9a78266a2474789e139 |
Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks | In contrast to traditional weight optimization in a continuous space , we demonstrate the existence of effective random networks whose weights are never updated . By selecting a weight among a fixed set of random values for each individual connection , our method uncovers combinations of random weights that match the p... | The paper investigates a type of neural network in which one of K possible fixed weights is chosen in each neuronal connection. The weights themselves are fixed and random, but the scores that determine which of the weights is chosen are updated through back-propagation using a straight-through estimator. The accuracy ... | SP:c0c3373f6d4e54dc4ce24da2bd90ac9644593e30 |
Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks | In contrast to traditional weight optimization in a continuous space , we demonstrate the existence of effective random networks whose weights are never updated . By selecting a weight among a fixed set of random values for each individual connection , our method uncovers combinations of random weights that match the p... | This paper proposes a method to train a neural network by selecting a weight from a set of $K$ randomly generated weights for each edge in the network. Each edge has a different set of random weights. Quality score is assinged to each of $K$ random weights, which determines the weight used in the forward calculation. I... | SP:c0c3373f6d4e54dc4ce24da2bd90ac9644593e30 |
Learning a Non-Redundant Collection of Classifiers | 1 INTRODUCTION . The Empirical Risk Minimization ( ERM ) principle ( Vapnik , 2013 ) , which underpins many machine learning models , is built on the assumption that training and testing samples are drawn i.i.d . from some hypothetical distribution . It has been demonstrated that certain violations of this assumption (... | This paper proposes an approach of training an ensemble of DNN classifiers while also minimizing the total correlation (TC) between the last layers (learned feature representations) of the classifiers to increase robustness to spurious correlations. To compute gradients for this new TC regularization term, they use th... | SP:956a4740077d285cf5d544664a99ebd3338400aa |
Learning a Non-Redundant Collection of Classifiers | 1 INTRODUCTION . The Empirical Risk Minimization ( ERM ) principle ( Vapnik , 2013 ) , which underpins many machine learning models , is built on the assumption that training and testing samples are drawn i.i.d . from some hypothetical distribution . It has been demonstrated that certain violations of this assumption (... | This paper proposes a method for training an ensemble of classifiers for problems where spurious correlations may be present. The models in the ensemble are encouraged to learn conditionally disentangled representations. It is suggested that this disentanglement will result in the spurious correlations being consigne... | SP:956a4740077d285cf5d544664a99ebd3338400aa |
Ensemble-based Adversarial Defense Using Diversified Distance Mapping | We propose an ensemble-based defense against adversarial examples using distance map layers ( DMLs ) . Similar to fully connected layers , DMLs can be used to output logits for a multi-class classification model . We show in this paper how DMLs can be deployed to prevent transferability of attacks across ensemble membe... | This paper aims to improve the robustness of ensembles of neural networks to adversarial attacks. If the models in the ensemble are susceptible to similar attacks, then the ensemble is also vulnerable. This has been shown to be true in practice (He et al.). The authors propose appending distance map layers (DMLs) to th... | SP:85d86e241a772c7b195fbc189ab6df0c67872ceb |
Ensemble-based Adversarial Defense Using Diversified Distance Mapping | We propose an ensemble-based defense against adversarial examples using distance map layers ( DMLs ) . Similar to fully connected layers , DMLs can be used to output logits for a multi-class classification model . We show in this paper how DMLs can be deployed to prevent transferability of attacks across ensemble membe... | This paper concerns on developing neural network ensembles that can avoid adversarial attacks. The authors propose a new concept of Distance Map Layers (DML) that can be used as the one just before the final layer in a neural network for classification. DML is mainly used to improve the diversity of predictions from th... | SP:85d86e241a772c7b195fbc189ab6df0c67872ceb |
Offline Meta Learning of Exploration | 1 INTRODUCTION . A central question in reinforcement learning ( RL ) is how to learn quickly ( i.e. , with few samples ) in a new environment . Meta-RL addresses this issue by assuming a distribution over possible environments , and having access to a large set of environments from this distribution during training ( D... | This submission studies the meta-learning problem in RL under offline settings. A new algorithm is proposed to address this problem by extending the recent VariBAD algorithm designed for online meta-RL. The key modifications to adapt the original VariBAD to offline settings are the state re-labelling and reward re-labe... | SP:c9bc6f77b7493e1cadb84f89759d5a89e61320de |
Offline Meta Learning of Exploration | 1 INTRODUCTION . A central question in reinforcement learning ( RL ) is how to learn quickly ( i.e. , with few samples ) in a new environment . Meta-RL addresses this issue by assuming a distribution over possible environments , and having access to a large set of environments from this distribution during training ( D... | The paper studies the problem of offline Meta-Reinforcement Learning (RL). In this problem, N separate RL environments are considered which are drawn from a specific underlying distribution. For each such environment, M trajectories each of horizon H are provided beforehand. The task is to train an RL agent that perfor... | SP:c9bc6f77b7493e1cadb84f89759d5a89e61320de |
Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS | 1 INTRODUCTION . In the past few years , one of the most seminal discoveries in the theory of neural networks is the neural tangent kernel ( NTK ) ( Jacot et al. , 2018 ) . The gradient flow on a normally initialized , fully connected neural network with a linear output layer in the infinite-width limit turns out to be... | This paper uses singularity analysis developed in the context of analytic combinatorics to study the relationship between the reproducing kernel Hilbert spaces of the NTK in a deep fully connected ReLU network, the Laplace kernel, and exponential power kernels. The main results are when these kernels are restricted to ... | SP:c5727500bc787fd26fe060b0a06892ee4430175e |
Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS | 1 INTRODUCTION . In the past few years , one of the most seminal discoveries in the theory of neural networks is the neural tangent kernel ( NTK ) ( Jacot et al. , 2018 ) . The gradient flow on a normally initialized , fully connected neural network with a linear output layer in the infinite-width limit turns out to be... | This paper proves that the reproducing kernel Hilbert spaces of a deep neural tangent kernel and the Laplace kernel have the same set of functions when they restricted to the sphere $S^{d-1}$, which improves the results established in Geifman et al., 2020. Moreover, the paper proves that more non-smooth of the exponent... | SP:c5727500bc787fd26fe060b0a06892ee4430175e |
Quantifying Task Complexity Through Generalized Information Measures | 1 INTRODUCTION . Deep networks have shown remarkable progress in both simple and complex machine learning tasks . But how does one measure the “ complexity ” of a learning task ? Is it possible to ascertain in a principled manner which tasks are “ harder ” to solve than others ? How “ close ” is one task to another ? A... | The paper claims that existing measures of complexity such as entropy are not suitable for measuring task complexity since they focus on the complexity of X rather than the predictive relationship from X to Y. The paper argues that mutual information is not useful for comparing different learning tasks, as two tasks (M... | SP:66169cd52e7746d7ca61a767f7ae2c1693727025 |
Quantifying Task Complexity Through Generalized Information Measures | 1 INTRODUCTION . Deep networks have shown remarkable progress in both simple and complex machine learning tasks . But how does one measure the “ complexity ” of a learning task ? Is it possible to ascertain in a principled manner which tasks are “ harder ” to solve than others ? How “ close ” is one task to another ? A... | This paper proposes a method to quantify the complexity of a learning task. The paper is motivated from the “20 questions“ game where an agent computes the answer (label) via a sequence of questions asked on the input data with answers given by an Oracle (simple functions of the data, in this case). The authors formali... | SP:66169cd52e7746d7ca61a767f7ae2c1693727025 |
On the Certified Robustness for Ensemble Models and Beyond | 1 INTRODUCTION . Deep neural networks ( DNN ) have been widely applied in various applications , such as image classification ( Krizhevsky , 2012 ; He et al. , 2016 ) , face recognition ( Sun et al. , 2014 ) , and natural language processing ( Vaswani et al. , 2017 ; Devlin et al. , 2019 ) . However , it is well-known ... | Certified robustness approaches have been studied for single models based on interval propagation as well as randomized smoothing. The use of ensembles for empirical robustness has also been studied in the literature. This paper attempts to theoretically study the certifiable defense achieved by ensembles. The paper an... | SP:6a3c4ae05d582f8896840483b08c735ced2976bc |
On the Certified Robustness for Ensemble Models and Beyond | 1 INTRODUCTION . Deep neural networks ( DNN ) have been widely applied in various applications , such as image classification ( Krizhevsky , 2012 ; He et al. , 2016 ) , face recognition ( Sun et al. , 2014 ) , and natural language processing ( Vaswani et al. , 2017 ; Devlin et al. , 2019 ) . However , it is well-known ... | This paper studies the following problem: How to train a certifiably robust classifier with ensemble methods? The authors considered two types of ensembles: the weighted-average ensemble and large-margin ensemble. They first derived theoretically sufficient and necessary conditions for robustness under two types of ens... | SP:6a3c4ae05d582f8896840483b08c735ced2976bc |
Learning Task-General Representations with Generative Neuro-Symbolic Modeling | People can learn rich , general-purpose conceptual representations from only raw perceptual inputs . Current machine learning approaches fall well short of these human standards , although different modeling traditions often have complementary strengths . Symbolic models can capture the compositional and causal knowled... | In this paper, the authors seek to combine the advantages of symbolic, compositional models and neural approaches, in particular by using "probabilistic programs with neural network subroutines" which should reflect a causal generative process. This is certainly an interesting area to explore. But I'm not convinced tha... | SP:c9481aa3660d329ae9727810687f68930d1f2d5e |
Learning Task-General Representations with Generative Neuro-Symbolic Modeling | People can learn rich , general-purpose conceptual representations from only raw perceptual inputs . Current machine learning approaches fall well short of these human standards , although different modeling traditions often have complementary strengths . Symbolic models can capture the compositional and causal knowled... | Motivated by few-shot learning challenges such as Omniglot, the authors propose a human-like model for learning how to draw visual concepts that consists of three components: (1) a location model for picking the starting point of the next stroke given the current "canvas", (2) a stroke model that continues an existing ... | SP:c9481aa3660d329ae9727810687f68930d1f2d5e |
Undistillable: Making A Nasty Teacher That CANNOT teach students | 1 INTRODUCTION . Knowledge Distillation ( KD ) ( Hinton et al. , 2015 ) aims to transfer useful knowledge from a teacher neural network to a student network by imitating the input-output behaviors . The student model imitates logit outputs or activation maps from the teacher by optimizing a distillation loss . The effi... | This paper explores an interesting and novel research problem: how to make a teacher model undistillable. This can be a promising countermeasure to model extraction/stealing. The proposed Nasty Teacher approach is a two stage method, which first trains a good teacher network, then utilizes a self-undermining KD strateg... | SP:81eb96286ff437475310246667130918695e12b6 |
Undistillable: Making A Nasty Teacher That CANNOT teach students | 1 INTRODUCTION . Knowledge Distillation ( KD ) ( Hinton et al. , 2015 ) aims to transfer useful knowledge from a teacher neural network to a student network by imitating the input-output behaviors . The student model imitates logit outputs or activation maps from the teacher by optimizing a distillation loss . The effi... | This paper’s main idea is refreshing and attractive: proposing a defensive method called nasty teacher, to avoid knowledge leaking or cloning through KD. A nasty teacher model is a specially trained network that yields nearly the same performance itself, while significantly degrading the performance of student models l... | SP:81eb96286ff437475310246667130918695e12b6 |
Predicting What You Already Know Helps: Provable Self-Supervised Learning | 1 Introduction . Self-supervised learning revitalizes machine learning models in computer vision , NLP , and control problems ( see reference therein [ 36 , 38 , 15 , 63 , 35 ] ) . Training a model with auxiliary tasks based only on input features reduces the extensive costs of data collection and semantic annotations ... | This paper proposes a mechanism based on approximate conditional independence (ACI) to explain why solving pretext tasks created from known information can learn representations that provably reduce downstream sample complexity, as a sufficient condition. In specific, they measure the downstream performance using the a... | SP:f9ece9e53f7d9bac9c921f1e85c270b826993a5a |
Predicting What You Already Know Helps: Provable Self-Supervised Learning | 1 Introduction . Self-supervised learning revitalizes machine learning models in computer vision , NLP , and control problems ( see reference therein [ 36 , 38 , 15 , 63 , 35 ] ) . Training a model with auxiliary tasks based only on input features reduces the extensive costs of data collection and semantic annotations ... | This paper attempts to understand why self-supervised learning works in the following sense: will the sample complexity for a downstream task be decreased (compared to the standard supervised learning without pretraining) if it is pre-trained according to some related auxiliary task? The relation between tasks is formu... | SP:f9ece9e53f7d9bac9c921f1e85c270b826993a5a |
Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships | 1 INTRODUCTION . Recent advances in imaging and recording techniques have enabled monitoring the activity of hundreds to several thousands of neurons simultaneously ( Jun et al. , 2017 ; Helmchen , 2009 ; Dombeck et al. , 2007 ) . These recordings can be made in awake animals engaged in specifically designed tasks or n... | This manuscript models the conditional joint distribution over variables by using Copula models and copula vines. The experimental data shows that when the observed variables are highly correlated that the proposed approach improves estimation of entropy over competing benchmark approaches (MINE and KSG) when the vari... | SP:72bbc4f02bdaf85da48d797942c0fba7e4cb0881 |
Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships | 1 INTRODUCTION . Recent advances in imaging and recording techniques have enabled monitoring the activity of hundreds to several thousands of neurons simultaneously ( Jun et al. , 2017 ; Helmchen , 2009 ; Dombeck et al. , 2007 ) . These recordings can be made in awake animals engaged in specifically designed tasks or n... | The authors exploit the expressive power of Copula mixtures to model time-varying multi-modal data, and employ Gaussian Processes to model the time-varying copula parameters. They demonstrate the efficacy of their method using information theoretic metrics on a synthetic dataset and a real-world joint neural-behavioral... | SP:72bbc4f02bdaf85da48d797942c0fba7e4cb0881 |
Trojans and Adversarial Examples: A Lethal Combination | 1 INTRODUCTION . Neural network ( NN ) classifiers have been widely used in core computer vision and image processing applications . However , NNs are sensitive and are easily attacked by exploiting vulnerabilities in training and model inference ( Szegedy et al. , 2014 ; Gu et al. , 2017 ) . We broadly categorize exis... | This paper proposes a new type of attack: AdvTrojan. This new attack is activated only when the test examples contain two things: backdoor trigger pattern and adversarial perturbation. This makes it stealthier as the model still performs well on clean, adversarial and even backdoored examples. A set of experiments were... | SP:bb9e587a38647060cbd38e3aad4376b19b18bd4d |
Trojans and Adversarial Examples: A Lethal Combination | 1 INTRODUCTION . Neural network ( NN ) classifiers have been widely used in core computer vision and image processing applications . However , NNs are sensitive and are easily attacked by exploiting vulnerabilities in training and model inference ( Szegedy et al. , 2014 ; Gu et al. , 2017 ) . We broadly categorize exis... | Based on the framework proposed by Pang et al. (2020), this paper unifies adversarial examples and Trojan backdoors into a synergistic attack. The inference results are dominated by the Trojan trigger and the adversarial perturbations. Such a mechanism extends the ability of the Trojan trigger. Incorporated with advers... | SP:bb9e587a38647060cbd38e3aad4376b19b18bd4d |
Recovering Geometric Information with Learned Texture Perturbations | 1 INTRODUCTION Since neural networks are trained to generalize to unseen data , regularization is important for reducing overfitting , see e.g . Goodfellow et al . ( 2016 ) ; Scholkopf & Smola ( 2001 ) . However , regularization also removes some of the high variance characteristic of much of the physical world . Even ... | The paper proposes a method to correct high-frequencies details in the textures of animated clothes. The main idea is to train a network to learn the 2D offset in the UV space for a given pose and view. The paper shows results using a t-shirt, on interpolating to novel views and 3D reconstruction by correcting the outp... | SP:a6950f04dd32e542da2dbb2ef34603274b2e178f |
Recovering Geometric Information with Learned Texture Perturbations | 1 INTRODUCTION Since neural networks are trained to generalize to unseen data , regularization is important for reducing overfitting , see e.g . Goodfellow et al . ( 2016 ) ; Scholkopf & Smola ( 2001 ) . However , regularization also removes some of the high variance characteristic of much of the physical world . Even ... | This paper presents a general approach to embed high frequency information into low-frequency data with a particular focus on improving the performance of virtual clothing. To address over-smoothing issues in the predicted meshes, authors proposed the texture sliding method that changes texture coordinates on each came... | SP:a6950f04dd32e542da2dbb2ef34603274b2e178f |
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks | 1 INTRODUCTION . Variational Auto-encoders ( VAEs ) are deep generative latent variable models that transform simple distributions over a latent space to model complex data distributions Kingma & Welling ( 2013 ) . They have been used for a wide range of downstream tasks , including : generating realistic looking synth... | The paper presents two analysis: (1) Characterization of when the training of VAEs using the ELBO leads to suboptimal generative models (biased towards ones with simple posteriors); and (2) How this suboptimality may affect downstream tasks that use the learned models. Specifically, the work focuses on VAEs using mean-... | SP:6b329d6858b6dd7bda5fa7a547c086a7fb98116e |
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks | 1 INTRODUCTION . Variational Auto-encoders ( VAEs ) are deep generative latent variable models that transform simple distributions over a latent space to model complex data distributions Kingma & Welling ( 2013 ) . They have been used for a wide range of downstream tasks , including : generating realistic looking synth... | The paper presents a characterization of failure modes of Gaussian VAEs. It is known that Gaussian VAEs can fail to produce good models either by failing to match the data distribution or by learning latent variables that are uninformative. The paper builds upon prior work that suggests that the VAE objective can cause... | SP:6b329d6858b6dd7bda5fa7a547c086a7fb98116e |
Adaptive Extra-Gradient Methods for Min-Max Optimization and Games | 1 Introduction The surge of recent breakthroughs in generative adversarial networks ( GANs ) [ 20 ] , robust reinforcement learning [ 41 ] , and other adversarial learning models [ 27 ] has sparked renewed interest in the theory of min-max optimization problems and games . In this broad setting , it has become empirica... | This paper propose a novel algorithm that solves the min-max problems and games based on the extra gradient (EG) framework. One of the main goal of this paper is to achieve the optimal convergence rate for both smooth/nonsmooth setttings without assuming the Lipchitz continuity/boundedness conditions. A "Bregman-Proxim... | SP:c5d1720922dfde389abbce3110a7f049e972192a |
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