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Unbiased Learning with State-Conditioned Rewards in Adversarial Imitation Learning | 1 INTRODUCTION . Inverse reinforcement learning ( IRL ) is an algorithm of recovering the ground truth reward function from observed behavior ( Ng & Russell , 2000 ) . IRL algorithms—followed by appropriate reinforcement learning ( RL ) algorithms—can optimize policy through farsighted cumulative value measures in the ... | This paper builds on a recent inverse-RL method, AIRL. The authors argue that the rewards learned by AIRL are potentially inefficient since they depend on the ratio of state-action visitation distributions of the expert and the policy. To resolve this, CAIRL derives rewards that excludes these visitation distributions;... | SP:08d227e9382cb5eb359462f2e75cca62f3457cf0 |
Transformers for Modeling Physical Systems | 1 INTRODUCTION . The transformer model ( Vaswani et al. , 2017 ) , built on self-attention , has largely become the stateof-the-art approach for a large set of neural language processing ( NLP ) tasks including language modeling , text classification , question answering , etc . Although more recent transformer work is... | The paper proposes to use transformers for modelling dynamical systems. The transformer is combined with a linear dynamical system to enforce Koopman features and is trained using the reconstruction and prediction loss. Finally, the proposed algorithm is applied to the different tasks with 1, 2 & 3 dimensions. On each ... | SP:8ab44295af08f56cc4486f603e7b3c8167d156ce |
Transformers for Modeling Physical Systems | 1 INTRODUCTION . The transformer model ( Vaswani et al. , 2017 ) , built on self-attention , has largely become the stateof-the-art approach for a large set of neural language processing ( NLP ) tasks including language modeling , text classification , question answering , etc . Although more recent transformer work is... | The paper proposes applying transformer models to modeling physical systems. The state at each time step is embedded into a continuous vector using a pretrained encoder-decoder model based on Koopman’s theory. The experiments are performed on three physical systems and generally show that (1) a transformer model outper... | SP:8ab44295af08f56cc4486f603e7b3c8167d156ce |
Layer-adaptive Sparsity for the Magnitude-based Pruning | 1 INTRODUCTION . Neural network pruning is an art of removing “ unimportant weights ” from a model , with an intention to meet practical constraints ( Han et al. , 2015 ) , mitigate overfitting ( Hanson & Pratt , 1988 ) , enhance interpretability ( Mozer & Smolensky , 1988 ) , or deepen our understanding on neural netw... | The paper proposes LAMP, an importance score for unstructured pruning that incorporates layerwise statistics such that the resultant scores for each connection can be compared globally, cutting down on the hyperparameter space for magnitude pruning from the relatively standard practice of requiring hand-specified layer... | SP:08dbd0677de078598537324299a1495f34aa5bc2 |
Layer-adaptive Sparsity for the Magnitude-based Pruning | 1 INTRODUCTION . Neural network pruning is an art of removing “ unimportant weights ” from a model , with an intention to meet practical constraints ( Han et al. , 2015 ) , mitigate overfitting ( Hanson & Pratt , 1988 ) , enhance interpretability ( Mozer & Smolensky , 1988 ) , or deepen our understanding on neural netw... | This paper presents a novel technique (layer-adaptive magnitude based pruning, or LAMP) for pruning neural network weights (pruning can be beneficial in terms of overfitting prevention as well as other practical considerations). LAMP evaluates weights in each layer in terms of the ratio of the magnitude of the weight... | SP:08dbd0677de078598537324299a1495f34aa5bc2 |
When Do Curricula Work? | 1 INTRODUCTION . Inspired by the importance of properly ordering information when teaching humans ( Avrahami et al. , 1997 ) , curriculum learning ( CL ) proposes training models by presenting easier examples earlier during training ( Elman , 1993 ; Sanger , 1994 ; Bengio et al. , 2009 ) . Previous empirical studies ha... | The paper conducts a large-scale evaluation of the impact of curriculum learning (CL) in image classification. The paper progresses nicely through a sequence of well-thought research questions and experiments, with the key findings stated up front. In particular, the notion of "implicit curriculum" is shown to exist. P... | SP:86b2d288cccd05f632414e500f86103956f62ab9 |
When Do Curricula Work? | 1 INTRODUCTION . Inspired by the importance of properly ordering information when teaching humans ( Avrahami et al. , 1997 ) , curriculum learning ( CL ) proposes training models by presenting easier examples earlier during training ( Elman , 1993 ; Sanger , 1994 ; Bengio et al. , 2009 ) . Previous empirical studies ha... | The paper provides a comprehensive analysis of the benefits of curriculum learning in different application scenarios. This includes investigating the phenomenon of implicit curricula, showing if the examples are learned in a consistent order across different architectures, and exploring the influences of explicit curr... | SP:86b2d288cccd05f632414e500f86103956f62ab9 |
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning | 1 INTRODUCTION . A principal challenge in modeling human behavior is in obtaining a transparent understanding of decision-making . In medical diagnosis , for instance , there is often significant regional and institutional variation in clinical practice [ 1 ] , much of it the leading cause of rising healthcare costs [ ... | This work proposes an approach for understanding and explaining decision-making behavior. The authors aim to make the method 1) transparent, 2) able to handle partial observability, and 3) work with offline data. To do this, they develop INTERPOLE, which uses Bayesian techniques to estimate decision dynamics as well as... | SP:af913437115d717862f353ae238f3fb1fc9d72f4 |
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning | 1 INTRODUCTION . A principal challenge in modeling human behavior is in obtaining a transparent understanding of decision-making . In medical diagnosis , for instance , there is often significant regional and institutional variation in clinical practice [ 1 ] , much of it the leading cause of rising healthcare costs [ ... | The paper proposes an algorithm for learning policies and internal models ("decision dynamics") from demonstrations. The key idea is to fit a distribution over policies, observation models, and transition models using an EM-like method. Offline experiments on a healthcare dataset show that the method learns interpretab... | SP:af913437115d717862f353ae238f3fb1fc9d72f4 |
Boundary Effects in CNNs: Feature or Bug? | 1 INTRODUCTION . One of the main intuitions behind the success of CNNs for visual tasks such as image classification ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2015 ; Szegedy et al. , 2015 ; Huang et al. , 2017 ) , video classification ( Karpathy et al. , 2014 ; Yue-Hei Ng et al. , 2015 ; Carreira & Zisserman ... | This paper studies the effect of padding on the Convolutional Neural Network. The authors try to answer the following questions: 1) what type of padding provides the most position information, 2) does the background value affects model accuracy when processing a patch on a canvas, 3) which part of the image suffers the... | SP:a7cbe71d5767df1afbc7795ff5ee10c6550dddca |
Boundary Effects in CNNs: Feature or Bug? | 1 INTRODUCTION . One of the main intuitions behind the success of CNNs for visual tasks such as image classification ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2015 ; Szegedy et al. , 2015 ; Huang et al. , 2017 ) , video classification ( Karpathy et al. , 2014 ; Yue-Hei Ng et al. , 2015 ; Carreira & Zisserman ... | The paper seeks to understand how different padding modes and canvas colors affect the performance of a convolutional neural network in classification and semantic segmentation tasks. The question seems somewhat strange - surely a network should be able to counteract a consistent change in padding or background color. ... | SP:a7cbe71d5767df1afbc7795ff5ee10c6550dddca |
Continual learning in recurrent neural networks | 1 INTRODUCTION . The ability to continually learn from a non-stationary data distribution while transferring and protecting past knowledge is known as continual learning ( CL ) . This ability requires neural networks to be stable to prevent forgetting , but also plastic to learn novel information , which is referred to... | The authors do an evaluation of the application of weight-importance continual learning methods to recurrent neural networks (RNNs). They draw out the tradeoff between complexity of precessing and just remembering (working memory) in terms of the applicability of these weight importance methods. They also provide some ... | SP:fb77e61ebd1844212439bb59e6a07c998486f30a |
Continual learning in recurrent neural networks | 1 INTRODUCTION . The ability to continually learn from a non-stationary data distribution while transferring and protecting past knowledge is known as continual learning ( CL ) . This ability requires neural networks to be stable to prevent forgetting , but also plastic to learn novel information , which is referred to... | This paper provides a systematic evaluation of the performance of different CL methods on RNN. The study suggests that high working memory requirements increase difficulty of learning new tasks, while the average length of input sequence is not strictly related to the difficulty of learning new tasks. The author propos... | SP:fb77e61ebd1844212439bb59e6a07c998486f30a |
Primal Wasserstein Imitation Learning | 1 INTRODUCTION . Reinforcement Learning ( RL ) has solved a number of difficult tasks whether in games ( Tesauro , 1995 ; Mnih et al. , 2015 ; Silver et al. , 2016 ) or robotics ( Abbeel & Ng , 2004 ; Andrychowicz et al. , 2020 ) . However , RL relies on the existence of a reward function , that can be either hard to s... | The authors proposed an imitation learning algorithm that utilizes the primal form of Wasserstein distance to match agent’s and expert’s state-action visitation distributions. They considered the upper bound of the primal form and devise the optimization method based on greedy coupling which makes learning suitable for... | SP:475019f17c9bf4c7e167222d56f920d12f8c8439 |
Primal Wasserstein Imitation Learning | 1 INTRODUCTION . Reinforcement Learning ( RL ) has solved a number of difficult tasks whether in games ( Tesauro , 1995 ; Mnih et al. , 2015 ; Silver et al. , 2016 ) or robotics ( Abbeel & Ng , 2004 ; Andrychowicz et al. , 2020 ) . However , RL relies on the existence of a reward function , that can be either hard to s... | This paper proposes to use Wasserstein distance in the primal form for imitation learning. Compared with its dual form and f-divergence minimization variants, it avoids the unstable minimax optimization. In order to compute the Wasserstein distance in primal form, they also propose a greedy approximation. Their experim... | SP:475019f17c9bf4c7e167222d56f920d12f8c8439 |
Relational Learning with Variational Bayes | 1 INTRODUCTION . American Psychological Association defines relational learning as ( VandenBos & APA , 2007 ) : Definition 1.1 ( Relational learning ) . Learning to differentiate among stimuli on the basis of relational properties rather than absolute properties . In other words , relational learning refers to the abil... | This paper proposes a model to infer the relationship between multiple instances in a dataset by inferring a latent variable. The authors accomplish this by defining an optimization problem that optimizes the ELBO of the proposed graphical model. The paper presents a nice solution to some of the identification issues t... | SP:e3a0b2cb1a7e2ed24eb413cbd4545cfcddc30a69 |
Relational Learning with Variational Bayes | 1 INTRODUCTION . American Psychological Association defines relational learning as ( VandenBos & APA , 2007 ) : Definition 1.1 ( Relational learning ) . Learning to differentiate among stimuli on the basis of relational properties rather than absolute properties . In other words , relational learning refers to the abil... | The paper proposes variational relational learning by learning relations between two inputs via variational inference on a probabilistic graphical model (PGM). The PGM that they use factors as p(a)p(z)p(b|a,z) where a,b are the two inputs and z is the supposed relationship between them. The example shown in the experim... | SP:e3a0b2cb1a7e2ed24eb413cbd4545cfcddc30a69 |
Generative Fairness Teaching | 1 INTRODUCTION . Automated learning systems are ubiquitous across a wide variety of sectors . Such systems can be used in many sensitive environments to make important and even life-changing decisions . Traditionally , decisions are made primary by human and the basis are usually highly regulated . For example in the E... | This paper describes a pre-processing method to reduce certain statistical disparities in the classifier obtained from the training data. The proposed approach involves learning a latent probability model that simulates the training data. The authors then manipulate the learned model to generate "counterfactual" sample... | SP:7ff567aac68a3492029136828f1b45dd7c358e8a |
Generative Fairness Teaching | 1 INTRODUCTION . Automated learning systems are ubiquitous across a wide variety of sectors . Such systems can be used in many sensitive environments to make important and even life-changing decisions . Traditionally , decisions are made primary by human and the basis are usually highly regulated . For example in the E... | This paper combines counterfactual modeling with adversarial training for fair machine learning tasks. For a given fairness metric chosen from a variety of canonical examples, the method ensures fairness by augmenting the data with counterfactual examples during training. The approach has potential, which is best demon... | SP:7ff567aac68a3492029136828f1b45dd7c358e8a |
Faster Training of Word Embeddings | 1 INTRODUCTION . Word embeddings have a long history ( Rumelhart et al. , 1986 ; Bengio et al. , 2003 ; Collobert & Weston , 2008 ) , but have received much attention in recent years due to word2vec ( Mikolov et al. , 2013 ) and its computationally efficient implementation via skip-gram with negative sampling . Word em... | This paper applies several algorithmic and code optimization techniques to reduce the training time for word2vec and fastText. The final implementation runs 3-20x times faster than the baselines on manycore CPUs, with almost no quality loss. The improved implementation is a result of the combination of many techniques,... | SP:c00be5119de90c410bab1b23fc36e559aca76041 |
Faster Training of Word Embeddings | 1 INTRODUCTION . Word embeddings have a long history ( Rumelhart et al. , 1986 ; Bengio et al. , 2003 ; Collobert & Weston , 2008 ) , but have received much attention in recent years due to word2vec ( Mikolov et al. , 2013 ) and its computationally efficient implementation via skip-gram with negative sampling . Word em... | This paper proposes the approaches that reduce the training times of word2vec and fastText. To improve the efficiency, it uses several techniques such as negative sample sharing, batched updates, and a byte-pair encoding-based alternative for subword units. By using English, German, and Russian languages data, it shows... | SP:c00be5119de90c410bab1b23fc36e559aca76041 |
Continual Invariant Risk Minimization | Empirical risk minimization can lead to poor generalization behaviour on unseen environments if the learned model does not capture invariant feature representations . Invariant risk minimization ( IRM ) is a recent proposal for discovering environment-invariant representations . It was introduced by Arjovsky et al . ( ... | In this work, the authors consider the problem of continual learning with distribution shifts. The work extends the recent work invariant risk minimization (IRM) from Arjovsky et al. to a continual learning setup. IRM was designed as an offline learning framework. In this work, the authors consider the setting where t... | SP:326dad16a8e4a1aaf80950dbed74ac096c0d5fef |
Continual Invariant Risk Minimization | Empirical risk minimization can lead to poor generalization behaviour on unseen environments if the learned model does not capture invariant feature representations . Invariant risk minimization ( IRM ) is a recent proposal for discovering environment-invariant representations . It was introduced by Arjovsky et al . ( ... | This paper extends the idea of invariant risk minimization (IRM) initially introduced by Arjovsky et al. (2019) to the setting of continual learning in which environments are observed sequentially rather than concurrently. This extension is implemented under a variational Bayesian and bilevel framework and the optimiza... | SP:326dad16a8e4a1aaf80950dbed74ac096c0d5fef |
Learned ISTA with Error-based Thresholding for Adaptive Sparse Coding | 1 INTRODUCTION . Sparse coding is widely used in many machine learning applications ( Xu et al. , 2012 ; Dabov et al. , 2007 ; Yang et al. , 2010 ; Ikehata et al. , 2012 ) , and its core problem is to deduce the high-dimensional sparse code from the obtained low-dimensional observation , for example , under the assumpt... | In the paper, authors propose a new error-based thresholding mechanism for LISTA which introduces a function of the evolving estimation error to provide each threshold in the shrinkage functions. They provided the theoretical analysis for EBT-LISTA and EBT-LISTA with support selection and proved that the estimation er... | SP:f93ca5c4d2f3a07efc1eea35c1e188156c981287 |
Learned ISTA with Error-based Thresholding for Adaptive Sparse Coding | 1 INTRODUCTION . Sparse coding is widely used in many machine learning applications ( Xu et al. , 2012 ; Dabov et al. , 2007 ; Yang et al. , 2010 ; Ikehata et al. , 2012 ) , and its core problem is to deduce the high-dimensional sparse code from the obtained low-dimensional observation , for example , under the assumpt... | This paper disentangles the threshold parameters in LISTA-type models from the reconstruction errors, proposing the Error-Based Threholding (EBT) mechanism which mainly follows a theoretical results in (Chen et al., 2019; Liu et al., 2018), where the threshold at one layer is proportional to the recovery error of curre... | SP:f93ca5c4d2f3a07efc1eea35c1e188156c981287 |
Lossless Compression of Structured Convolutional Models via Lifting | 1 INTRODUCTION . Lifted , often referred to as templated , models use highly expressive representation languages , typically based in weighted predicate logic , to capture symmetries in relational learning problems ( Koller et al. , 2007 ) . This includes learning from data such as chemical , biological , social , or t... | The paper provides an interesting work in the scale/speed up of structured convolutional models. In particular, it proposes an idea using a technique named lifting which is used in scaling up of graphical models to detect the symmetries and compress the neural model such as Graph Neural Network. Authors show that this ... | SP:95a668e44a54b5e8def5ea2abb2e2a06026637b8 |
Lossless Compression of Structured Convolutional Models via Lifting | 1 INTRODUCTION . Lifted , often referred to as templated , models use highly expressive representation languages , typically based in weighted predicate logic , to capture symmetries in relational learning problems ( Koller et al. , 2007 ) . This includes learning from data such as chemical , biological , social , or t... | The authors of this paper propose an compression technique for GNNs that was inspired by lifted inference. The compression consists of removing asymmetries by merging nodes. They define two algorithms for compression: a non-exact algorithm that merges two nodes that are "functional" equivalent and an exact algorithm th... | SP:95a668e44a54b5e8def5ea2abb2e2a06026637b8 |
Unsupervised Anomaly Detection by Robust Collaborative Autoencoders | Unsupervised anomaly detection plays a critical role in many real-world applications , from computer security to healthcare . A common approach based on deep learning is to apply autoencoders to learn a feature representation of the normal ( non-anomalous ) observations and use the reconstruction error of each observat... | This paper presents a Robust Collaborative Autoencoder (RCA) for unsupervised anomaly detection. The authors focused on the overparameterization of existing NN-based unsupervised anomaly detection methods, and the proposed method aims to overcome the overparameterization problem. The main contibutinos of the proposed m... | SP:cae76295c4e38ce51c6bf1ed147ee4ea0569faed |
Unsupervised Anomaly Detection by Robust Collaborative Autoencoders | Unsupervised anomaly detection plays a critical role in many real-world applications , from computer security to healthcare . A common approach based on deep learning is to apply autoencoders to learn a feature representation of the normal ( non-anomalous ) observations and use the reconstruction error of each observat... | The submission tackles unsupervised anomaly detection, specifically in a scenario where supervision labels are not available, only information about the ratio of anomalous examples in the data set. They suggest an architecture consisting of two auto-encoders collaboratively determining anomalous samples and updating th... | SP:cae76295c4e38ce51c6bf1ed147ee4ea0569faed |
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation | 1 INTRODUCTION . Image semantic segmentation is a core computer vision task that has been studied for decades . Compared with other vision tasks , such as image classification and object detection , human annotation of pixel-accurate segmentation is dramatically more expensive . Given sufficient pixellevel labeled trai... | This work addresses the task of semi-supervised learning (SSL) in semantic segmentation. Following recent SOTAs in SSL, this work also advocates for the use of pseudo-labels on unlabeled data and heavy data augmentation. The main novelty of this work is the novel way to construct higher-quality pseudo-labels: besides t... | SP:776851b803da21fa83071c6f5c41e82a1ccc765a |
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation | 1 INTRODUCTION . Image semantic segmentation is a core computer vision task that has been studied for decades . Compared with other vision tasks , such as image classification and object detection , human annotation of pixel-accurate segmentation is dramatically more expensive . Given sufficient pixellevel labeled trai... | This paper focuses on the problem of semi-supervised semantic segmentation, where less pixel-level annotations are used to train the network. A new one-stage training framework is proposed to include the process of localization cue generation, pseudo label refinement and training of semantic segmentation. Inspire by re... | SP:776851b803da21fa83071c6f5c41e82a1ccc765a |
AriEL: Volume Coding for Sentence Generation Comparisons | 1 Introduction . Representation regularization , through the normalization and bounding of data , representations and gradients , is fundamental to fast deep learning training ( Ioffe and Szegedy , 2015 ; Kingma and Welling , 2014 ; He et al. , 2015 ; Perez et al. , 2018 ) . However , it seldom offers guarantees for bo... | This paper proposes a sentence embedding called AriEL. Specifically, based on arithmetic coding and k-d trees, AriEL maps sequences of discrete data into volumes in the latent space, and can then retrieve sequences by random sampling. AriEL is compared to other standard techniques such as Transformer and Variational Au... | SP:f88a0263fe87db598ed9d3b537430324ee29ddf2 |
AriEL: Volume Coding for Sentence Generation Comparisons | 1 Introduction . Representation regularization , through the normalization and bounding of data , representations and gradients , is fundamental to fast deep learning training ( Ioffe and Szegedy , 2015 ; Kingma and Welling , 2014 ; He et al. , 2015 ; Perez et al. , 2018 ) . However , it seldom offers guarantees for bo... | This paper proposes AriEL, a sentence encoding method onto the compact space [0, 1]^d. It leverages essences of arithmetic coding and kd-tree to encode/decode sentences with a fixed region of the space. With the property of arithmetic coding, in theory, it can map sentences with any lengths into individual values, and ... | SP:f88a0263fe87db598ed9d3b537430324ee29ddf2 |
Mastering Atari with Discrete World Models | 1 INTRODUCTION To successfully operate in unknown environments , reinforcement learning agents need to learn about their environments over time . World models are an explicit way to represent an agent ’ s knowledge about its environment . Compared to model-free reinforcement learning that learns through trial and error... | The authors introduce DreamerV2, a modification of the influential Dreamer RL agent (hereafter refered to as DreamerV1). The primary changes from DreamerV1 are a discrete latent space and a modified loss function (and with it, a modified optimization scheme). As in DreamerV1, the agent trains a world model with environ... | SP:e5b6ac071882028ba6191098c718861340728918 |
Mastering Atari with Discrete World Models | 1 INTRODUCTION To successfully operate in unknown environments , reinforcement learning agents need to learn about their environments over time . World models are an explicit way to represent an agent ’ s knowledge about its environment . Compared to model-free reinforcement learning that learns through trial and error... | The authors build on the Dreamer architecture, that is able to learn models of an environment, to build DreamerV2, which learns a model of an environment in latent space. The authors then train their agent in this latent space. DreamerV2 was evaluated on the Atari learning environment and results showed that it was com... | SP:e5b6ac071882028ba6191098c718861340728918 |
Learning to Dynamically Select Between Reward Shaping Signals | 1 INTRODUCTION . Although numerous successes have been reported in reinforcement learning ( RL ) , it still suffers from several drawbacks that prevent it from performing to expectation in many real-life situations . One critical limitation is the sample complexity . In order to arrive at an acceptable solution , RL re... | The paper presents an approach to select the best reward shaping potential signal out of multiple available shaping potentials. The main idea seems to be to select the shaping signal that minimizes the inverse of the difference of potentials between the next state and the current state. The experiments show the propose... | SP:5247942ad6db19ef5124aa562fd1d4186358c779 |
Learning to Dynamically Select Between Reward Shaping Signals | 1 INTRODUCTION . Although numerous successes have been reported in reinforcement learning ( RL ) , it still suffers from several drawbacks that prevent it from performing to expectation in many real-life situations . One critical limitation is the sample complexity . In order to arrive at an acceptable solution , RL re... | The idea of potential-based reward shaping (PBRS) is to improve the performance of learning agents by incorporating additional domain knowledge into their reward function (making it dense), while maintaining the same asymptotic convergence guarantees. However, one aspect of PBRS, that is introduced in this work, is how... | SP:5247942ad6db19ef5124aa562fd1d4186358c779 |
Fantastic Four: Differentiable and Efficient Bounds on Singular Values of Convolution Layers | 1 INTRODUCTION . Bounding singular values of different layers of a neural network is a way to control the complexity of the model and has been used in different problems including robustness , generalization , optimization , generative modeling , etc . In particular , the spectral norm ( the maximum singular value ) of... | This paper provides an method for computing an upper bound for the spectral norm of the linear transformation induced by a convolutional layer. An upper bound was first introduced as a heuristic by Miyato et al, but they did not prove any bounds. The authors use the exact computation of singular values of a convolution... | SP:5c782fa6fa0245b510c2aa33ea661b2f8cf09062 |
Fantastic Four: Differentiable and Efficient Bounds on Singular Values of Convolution Layers | 1 INTRODUCTION . Bounding singular values of different layers of a neural network is a way to control the complexity of the model and has been used in different problems including robustness , generalization , optimization , generative modeling , etc . In particular , the spectral norm ( the maximum singular value ) of... | This paper propose to study the Lipschitz constant of convolutional layers and to give an easy to compute and differentiable upper bound. The upper bound is composed of 4 different bounds, based on tensor unfolding of the Jacobian, and taking the min of these 4 values. This upper bound is then used to train networks wi... | SP:5c782fa6fa0245b510c2aa33ea661b2f8cf09062 |
Distributionally Robust Learning for Unsupervised Domain Adaptation | 1 INTRODUCTION . In many real-world applications , the target domain for deployment of a machine-learning ( ML ) model can significantly differ from the source training domain . Furthermore , labels in the target domain are often more expensive to obtain compared to the source domain . An example is synthetic training ... | I find the paper to be well motivated. Self-labeling has proven to be a useful approach for unsupervised domain adaptation. And since wrong pseudo-labels in the target domain result catastrophic failure in early iterations, it makes sense to calibrate the production of pseudo-labels through the use of uncertainty estim... | SP:677142c2fc75609c7728334a2adeebf0b4620453 |
Distributionally Robust Learning for Unsupervised Domain Adaptation | 1 INTRODUCTION . In many real-world applications , the target domain for deployment of a machine-learning ( ML ) model can significantly differ from the source training domain . Furthermore , labels in the target domain are often more expensive to obtain compared to the source domain . An example is synthetic training ... | The paper proposes to use the distributionally robust learning (DRL) for unsupervised domain adaptation. First, the authors demonstrate how differentiable density ratio estimation can be done for source and target domains in an end-to-end manner. Following this, the authors demonstrate how confidence estimation (relian... | SP:677142c2fc75609c7728334a2adeebf0b4620453 |
Matrix Shuffle-Exchange Networks for Hard 2D Tasks | 1 INTRODUCTION . Data often comes in a form of two-dimensional matrices . Neural networks are often used for processing such data usually involving convolution as the primary processing method . But convolutions are local , capable of analyzing only neighbouring positions in the data matrix . That is good for images si... | The paper proposes a network architecture called Matrix Shuffle-Exchange (Matrix-SE) that can learn many logical reasoning tasks on 2D data and graph. It has complexity O(n^2 log n) for 2D input of size n x n, which is much smaller than the complexity of naive attention applied to 2D data (O(n^4)). The proposed archite... | SP:05e0d4b6ccaac1bdf0ffa78bad02722d4dfd4659 |
Matrix Shuffle-Exchange Networks for Hard 2D Tasks | 1 INTRODUCTION . Data often comes in a form of two-dimensional matrices . Neural networks are often used for processing such data usually involving convolution as the primary processing method . But convolutions are local , capable of analyzing only neighbouring positions in the data matrix . That is good for images si... | This work proposes the Neural Shuffle-Exchange Network to capture both local and global dependencies for 2D data. The idea extends the 1D Neural Shuffle-Exchange Network to its 2D application. The proposed method first converts 2D data to 1D following the Z-order, then apply several Quaternary Switch and Quaternary Shu... | SP:05e0d4b6ccaac1bdf0ffa78bad02722d4dfd4659 |
What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space | 1 INTRODUCTION . Deep neural networks ( DNNs ) are a family of powerful models that have demonstrated superior learning capabilities in a wide range of applications such as image classification , object detection and natural language processing . However , DNNs are often applied as a black box with limited understandin... | This paper proposes a visualization method to reveal the class-specific discriminative patterns of DNNs in the input space. When added to images from another class, such patterns can lead the DNN to classify the images into the pattern's class. From the experimental results, the authors conjecture that images trained o... | SP:244bbe4a2152e42292a91a9a05290205ee8deb5f |
What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space | 1 INTRODUCTION . Deep neural networks ( DNNs ) are a family of powerful models that have demonstrated superior learning capabilities in a wide range of applications such as image classification , object detection and natural language processing . However , DNNs are often applied as a black box with limited understandin... | The papers proposes a simple method for visualizing the patterns learned by deep neural networks in the supervised classification setting. Informally, suppose you have an image x that is "representative" of the class y and let X be a set of images that belong to other classes. The authors propose an optimization proble... | SP:244bbe4a2152e42292a91a9a05290205ee8deb5f |
Optimism in Reinforcement Learning with Generalized Linear Function Approximation | ( H √ d3T ) whereH is the horizon , d is the dimensionality of the state-action features and T is the number of episodes . This is the first statistically and computationally efficient algorithm for reinforcement learning with generalized linear functions . 1 INTRODUCTION . We study episodic reinforcement learning prob... | This paper analyses an existing algorithm (LSVI-UCB) with generalized linear function approximation instead of conventional linear function approximation. Under this generalized linear setting, they propose a so-called “optimistic closure” assumption which is shown to be strictly weaker than the expressivity assumptio... | SP:b9477062862ad7bab901f295b471a254dcf78e1f |
Optimism in Reinforcement Learning with Generalized Linear Function Approximation | ( H √ d3T ) whereH is the horizon , d is the dimensionality of the state-action features and T is the number of episodes . This is the first statistically and computationally efficient algorithm for reinforcement learning with generalized linear functions . 1 INTRODUCTION . We study episodic reinforcement learning prob... | The authors studies an episodic MDP learning problem, where they propose to study an Optimistic Closure assumption which allows the Q function to be expressed as a generalized linear function plus a positive semi-definite quadratic form. They motivate the assumption by showing that the assumption allows the tabular MDP... | SP:b9477062862ad7bab901f295b471a254dcf78e1f |
Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling | 1 INTRODUCTION Recently , energy-based models ( EBMs ) ( Zhu et al. , 1998 ; LeCun et al. , 2006 ) parameterized by modern neural networks have drawn much attention from the deep learning communities . Successful applications with EBMs include generations of images ( Xie et al. , 2016 ; 2018b ; Du & Mordatch , 2019 ) ,... | Much like progressive growing of GANs two years ago, this paper adopts a similar coarse-to-fine procedure for scaling EBMs to higher resolutions. In particular, the approach starts from learning EBMs on low-resolution images and then smoothly transitions to higher resolution by carefully designing an expand layer and a... | SP:bc69ea7519d15ff99678ebc5a228da631480c39d |
Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling | 1 INTRODUCTION Recently , energy-based models ( EBMs ) ( Zhu et al. , 1998 ; LeCun et al. , 2006 ) parameterized by modern neural networks have drawn much attention from the deep learning communities . Successful applications with EBMs include generations of images ( Xie et al. , 2016 ; 2018b ; Du & Mordatch , 2019 ) ,... | This paper presents a number of methods to scale up training and sampling of EBMs on image data. The main contribution consists of an approach for progressively growing the model by increasing the image resolution as training progresses. This approach echos similar approaches used for scaling up GAN training. The appro... | SP:bc69ea7519d15ff99678ebc5a228da631480c39d |
Near-Optimal Glimpse Sequences for Training Hard Attention Neural Networks | 1 INTRODUCTION . Attention can be defined as the “ allocation of limited cognitive processing resources ” ( Anderson , 2005 ) . In humans the density of photoreceptors varies across the retina . It is much greater in the centre ( Bear et al. , 2007 ) and covers an approximately 210 degree field of view ( Traquair , 194... | The paper trains hard attention for image classification. The network is partially supervised by attention locations proposed to maximally reduce the entropy of the image label distribution. To propose these locations, the method needs an already trained image classifier conditioned on glimpses and their locations. Add... | SP:223bbaf9169ba486cbfbc0d8c35d662ea211c358 |
Near-Optimal Glimpse Sequences for Training Hard Attention Neural Networks | 1 INTRODUCTION . Attention can be defined as the “ allocation of limited cognitive processing resources ” ( Anderson , 2005 ) . In humans the density of photoreceptors varies across the retina . It is much greater in the centre ( Bear et al. , 2007 ) and covers an approximately 210 degree field of view ( Traquair , 194... | This paper presents a learning framework for a hard attention mechanism. The glimpses captured by the attention mechanism are guided by the goal of minimizing output uncertainty for a downstream task such as classification. The authors pose this problem in a probabilistic framework which is based on Bayesian optimal ex... | SP:223bbaf9169ba486cbfbc0d8c35d662ea211c358 |
Bayesian Metric Learning for Robust Training of Deep Models under Noisy Labels | 1 INTRODUCTION . Deep learning has been shown as a dominant learning framework in various domains of machine learning and computer vision . One of the major limitations of deep learning is that it often requires relatively clean data sets that do not contain label noise naturally caused by human labeling errors , measu... | This paper introduces a Bayesian deep metric learning framework that is robust against noise labels. The proposed method is inspired by the BLMNN (Wang & Tan, 2018), deep metric learning (Hoffer & Ailon, 2015; Hu et al., 2015; Wang et al., 2017; Lu et al., 2017; Do et al., 2019), and Bayes by Backprop (Blundell et al.,... | SP:d26682cab15475af1eedf1431fb8596e311b965d |
Bayesian Metric Learning for Robust Training of Deep Models under Noisy Labels | 1 INTRODUCTION . Deep learning has been shown as a dominant learning framework in various domains of machine learning and computer vision . One of the major limitations of deep learning is that it often requires relatively clean data sets that do not contain label noise naturally caused by human labeling errors , measu... | This paper proposes a robust Bayesian deep metric learning framework against noise label inspired the BLMNN (Wang & Tan, 2018), deep metric learning (Hoffer & Ailon, 2015; Hu et al., 2015; Wang et al., 2017; Lu et al., 2017; Do et al., 2019), and Bayes by Backprop (Blundell et al., 2015). Directly applying the variatio... | SP:d26682cab15475af1eedf1431fb8596e311b965d |
Monotonic Robust Policy Optimization with Model Discrepancy | 1 INTRODUCTION . With deep neural network approximation , deep reinforcement learning ( DRL ) has extended classical reinforcement learning ( RL ) algorithms to successfully solving complex control tasks , e.g. , playing computer games with human-level performance ( Mnih et al. , 2013 ; Silver et al. , 2018 ) and conti... | This paper focuses on the generalization issue in reinforcemetn leanring, specifically aims to address the problems of domain randomization(DR) technique. Different from standard DR which treats all the sample environment as equal, this paper proposed to improve the performance over all possible environments and the wo... | SP:e8a08f3ad14ae96021ec69070a156d57811c88be |
Monotonic Robust Policy Optimization with Model Discrepancy | 1 INTRODUCTION . With deep neural network approximation , deep reinforcement learning ( DRL ) has extended classical reinforcement learning ( RL ) algorithms to successfully solving complex control tasks , e.g. , playing computer games with human-level performance ( Mnih et al. , 2013 ; Silver et al. , 2018 ) and conti... | This paper introduces Monotonic Robust Policy Optimization (MRPO), an RL algorithm that aims to jointly optimize policy and domain sampling distribution, with the goal of improving policy performance for both average and worst-case scenarios and addressing the model discrepancy between the training and target environme... | SP:e8a08f3ad14ae96021ec69070a156d57811c88be |
Learning Private Representations with Focal Entropy | 1 INTRODUCTION . Lately , the topics of privacy and security are enjoying increased interest in the machine learning community . This can largely be attributed to the success of big data in conjunction with deep learning and the urge to create and process ever-larger data sets for mining . However , with the emergence ... | This paper gives a method in the class of learning representations which have some information censored. In particular, the authors propose a setup where there are many “private” classes, and some classes are more similar than others – this maps (I think) onto the privacy setting, where each class is like one individua... | SP:bbaaeb718f346e866e91cf7e6f9278f0a2bfbab4 |
Learning Private Representations with Focal Entropy | 1 INTRODUCTION . Lately , the topics of privacy and security are enjoying increased interest in the machine learning community . This can largely be attributed to the success of big data in conjunction with deep learning and the urge to create and process ever-larger data sets for mining . However , with the emergence ... | ########################################################################## Summary: The paper studies how to learn private representations that only captures the non-sensitive attributes of the dataset. They propose an adversarial representation learning method that employs VAEs. Specifically, the architecture in the V... | SP:bbaaeb718f346e866e91cf7e6f9278f0a2bfbab4 |
Protecting DNNs from Theft using an Ensemble of Diverse Models | 1 INTRODUCTION . MS attacks allow an adversary with black-box access to the predictions of the target model to copy its functionality and create a high-accuracy clone model , posing a threat to the confidentiality of proprietary DNNs . Such attacks also open the door to a wide range of security vulnerabilities includin... | The paper proposes a method to protect deep neural networks against model stealing. The propose defense trains an ensemble of classifiers using two losses one targeting accuracy and the other diversity of the ensemble. In particular, the trained classifiers are consistent on in-distribution data, but contradict each ot... | SP:fcd72bc92c431b2f991d9e765dbdba684cada4e7 |
Protecting DNNs from Theft using an Ensemble of Diverse Models | 1 INTRODUCTION . MS attacks allow an adversary with black-box access to the predictions of the target model to copy its functionality and create a high-accuracy clone model , posing a threat to the confidentiality of proprietary DNNs . Such attacks also open the door to a wide range of security vulnerabilities includin... | This paper tackles a timely problem of protecting deep neural networks from mode stealing attacks. This paper proposed an Ensemble of Diverse Model to provide diversity prediction for the adversary’s OOD query. The main contribution of this paper is the introduction of the diversity loss function on OOD data and the di... | SP:fcd72bc92c431b2f991d9e765dbdba684cada4e7 |
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary | 1 INTRODUCTION . Modern deep reinforcement learning agents ( Mnih et al. , 2015 ; Levine et al. , 2015 ; Lillicrap et al. , 2015 ; Silver et al. , 2016 ; Fujimoto et al. , 2018 ) typically use neuron networks as function approximators . Since the discovery of adversarial examples in image classification tasks ( Szegedy... | This paper proposes to improve the robustness of a reinforcement learning agent by alternatively training an agent and an adversary who perturbs the state observations. The learning of an “optimal” adversary for a fixed policy is based on the theory of SA-MDP in prior work. The learning of an optimal policy under a fix... | SP:586149146ed5e74dd231b134fa6ba582f6e1f72b |
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary | 1 INTRODUCTION . Modern deep reinforcement learning agents ( Mnih et al. , 2015 ; Levine et al. , 2015 ; Lillicrap et al. , 2015 ; Silver et al. , 2016 ; Fujimoto et al. , 2018 ) typically use neuron networks as function approximators . Since the discovery of adversarial examples in image classification tasks ( Szegedy... | of the paper: The paper studies adversarial attacks in RL, focusing both on the design of optimal attack strategies on RL agents, as well as robust training RL procedures for mitigating attacks. Building on the results of (Zhang et al., 2020), the paper proposes a new learning framework (ATLA), that simultaneously tra... | SP:586149146ed5e74dd231b134fa6ba582f6e1f72b |
Learning Discrete Adaptive Receptive Fields for Graph Convolutional Networks | 1 INTRODUCTION . After a series of explorations and modifications ( Bruna et al. , 2014 ; Kipf & Welling , 2017 ; Velickovic et al. , 2017 ; Xu et al. , 2019 ; Li et al. , 2019 ; Abu-El-Haija et al. , 2019 ) , Graph Convolutional Networks ( GCNs ) 1 have gained considerable attention in the machine learning community .... | The paper proposes a method for avoiding the oversmoothing happening in standard GNN methods. It defines a receptive field of a node, as the set of nodes that send messages to that node and proposes a method to create adaptive receptive fields specific to each node. Instead of using all the nodes in a multi-hop neighbo... | SP:4addc1c9c0f91be7fc176425cb41c22cb4e562ba |
Learning Discrete Adaptive Receptive Fields for Graph Convolutional Networks | 1 INTRODUCTION . After a series of explorations and modifications ( Bruna et al. , 2014 ; Kipf & Welling , 2017 ; Velickovic et al. , 2017 ; Xu et al. , 2019 ; Li et al. , 2019 ; Abu-El-Haija et al. , 2019 ) , Graph Convolutional Networks ( GCNs ) 1 have gained considerable attention in the machine learning community .... | The authors theoretically and empirically show that soft-attention mechanism uses in GCNs suffers from over-smoothness in large neighborhoods. For addressing this shortcoming, they propose a neighborhood sampling approach called adaptive receptive fields (ARFs) which discretely select nodes among the multi-hop neighbor... | SP:4addc1c9c0f91be7fc176425cb41c22cb4e562ba |
Model-centric data manifold: the data through the eyes of the model | 1 INTRODUCTION . In machine learning , models are categorized as discriminative models or generative models . From its inception , deep learning has focused on classification and discriminative models ( Krizhevsky et al. , 2012 ; Hinton et al. , 2012 ; Collobert et al. , 2011 ) . Another perspective came with the const... | The authors propose a so called data matrix that is induced in the input space of a deep neural network classifier. This matrix is similar to the Fisher-Rao metric, but for the input and not the parameters of the model. The analysis of this matrix shows that the classifier induces in the input space a specific structur... | SP:11f0323635f0647b3407ac61faad5b149754b06c |
Model-centric data manifold: the data through the eyes of the model | 1 INTRODUCTION . In machine learning , models are categorized as discriminative models or generative models . From its inception , deep learning has focused on classification and discriminative models ( Krizhevsky et al. , 2012 ; Hinton et al. , 2012 ; Collobert et al. , 2011 ) . Another perspective came with the const... | In this work, the authors showed that deep ReLU networks can model the low dimensional manifold structure of the dataset. The authors first define a local data matrix G which is analogous to Fisher matrix. Then they proved that the tangent space of the data manifold is spanned by the eigen vectors of G corresponding to... | SP:11f0323635f0647b3407ac61faad5b149754b06c |
Impact of Representation Learning in Linear Bandits | √ kN + √ dkNT ) regret , where N is the number of rounds we play for each bandit . When T is sufficiently large , our algorithm significantly outperforms the naive algorithm ( playing T bandits independently ) that achieves Õ ( T √ dN ) regret . We also provide an Ω ( T √ kN + √ dkNT ) regret lower bound , showing tha... | This paper studies the benefits of learning a low-rank feature extractor in multi-task linear bandits. Specifically, the paper studies the setting where an unknown common linear feature extractor $B \in R^{d \times k}$ maps the original $d$-dimensional contexts $x$ to a $k$-dimensional representation. Essentially, for ... | SP:0fa4963f1d57d48a6271fe726358d204b1e286e8 |
Impact of Representation Learning in Linear Bandits | √ kN + √ dkNT ) regret , where N is the number of rounds we play for each bandit . When T is sufficiently large , our algorithm significantly outperforms the naive algorithm ( playing T bandits independently ) that achieves Õ ( T √ dN ) regret . We also provide an Ω ( T √ kN + √ dkNT ) regret lower bound , showing tha... | This paper theoretically studies the benefits of representation learning in linear bandit problems. The key assumption is the existence of a common linear feature extractor. Two different setting are studied. In the finite-action setting, the authors provide the MLinGreedy algorithm that achieves matching upper and low... | SP:0fa4963f1d57d48a6271fe726358d204b1e286e8 |
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients | 1 INTRODUCTION . Cooperative multi-agent reinforcement learning ( MARL ) has achieved great progress in recent years ( Hughes et al. , 2018 ; Jaques et al. , 2019 ; Vinyals et al. , 2019 ; Zhang et al. , 2019 ; Baker et al. , 2020 ; Wang et al. , 2020c ) . Advances in valued-based MARL ( Sunehag et al. , 2018 ; Rashid ... | In the context centralized training distributed execution in cooperative multi-agent reinforcement learning (MARL), the paper proposes an architecture to learn a decomposed action value function expressed as a weighted sum of the agent's individual functions (plus an additional weight). Those weights are themselves lea... | SP:a0cbc9dde2539645b847f40af560afe953f001ee |
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients | 1 INTRODUCTION . Cooperative multi-agent reinforcement learning ( MARL ) has achieved great progress in recent years ( Hughes et al. , 2018 ; Jaques et al. , 2019 ; Vinyals et al. , 2019 ; Zhang et al. , 2019 ; Baker et al. , 2020 ; Wang et al. , 2020c ) . Advances in valued-based MARL ( Sunehag et al. , 2018 ; Rashid ... | This paper focuses on the problem of multi-agent reinforcement learning (MARL) for CTDE scenario which is well studied in recent literature. The work discusses shortcomings of actor-critic methods for MARL and proposes a solution using linearly factored critic. The paper is somewhat difficult to read and can be made b... | SP:a0cbc9dde2539645b847f40af560afe953f001ee |
Shapley Explanation Networks | Shapley values have become one of the most popular feature attribution explanation methods . However , most prior work has focused on post-hoc Shapley explanations , which can be computationally demanding due to its exponential time complexity and preclude model regularization based on Shapley explanations during train... | This work proposes a new model class designed to make SHAP value calculations more efficient. The proposed method exploits sparsity and additivity among intermediate values to provide fast exact SHAP values for shallow ShapNets, and fast approximate SHAP values for Deep ShapNets. This approach enables SHAP-based regula... | SP:585ea7586283caf39965101656d1dc17abe1b331 |
Shapley Explanation Networks | Shapley values have become one of the most popular feature attribution explanation methods . However , most prior work has focused on post-hoc Shapley explanations , which can be computationally demanding due to its exponential time complexity and preclude model regularization based on Shapley explanations during train... | The paper proposes to incorporate Shapley values as latent representations in deep models. Specifically, the paper constructs Shallow SHAPNETs that computes the exact Shapley values. The paper also constructs Deep SHAPNETs that maintain the missingness and accuracy properties of Shapley values. The effectiveness of the... | SP:585ea7586283caf39965101656d1dc17abe1b331 |
Learning Incompressible Fluid Dynamics from Scratch - Towards Fast, Differentiable Fluid Models that Generalize | 1 INTRODUCTION . Simulating the behavior of fluids by solving the incompressible Navier-Stokes equations is of great importance for a wide range of applications and accurate as well as fast fluid simulations are a long-standing research goal . On top of simulating the behavior of fluids , several applications such as s... | This paper proposes to learn the dynamics of an incompressible fluid via a physics informed loss formulation using an unsupervised training framework. It employs a custom solver that is executed at training time to learn a Navier-Stokes residual with a incompressible (curl of a stream function) formulation. This setup ... | SP:cd3d672f555b7a88704ad3142aca702ec7154258 |
Learning Incompressible Fluid Dynamics from Scratch - Towards Fast, Differentiable Fluid Models that Generalize | 1 INTRODUCTION . Simulating the behavior of fluids by solving the incompressible Navier-Stokes equations is of great importance for a wide range of applications and accurate as well as fast fluid simulations are a long-standing research goal . On top of simulating the behavior of fluids , several applications such as s... | This paper presents a "physics-informed" deep learning model of fluid dynamics. The underlying deep learning architecture employed is a somewhat standard u-net, but one of the proposed method's distinguishing features is that it enforces its adherence to physical behavior at its loss terms, by penalizing predictions th... | SP:cd3d672f555b7a88704ad3142aca702ec7154258 |
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning | 1 INTRODUCTION . Federated learning has become an important paradigm in large-scale machine learning where the training data remains distributed over a large number of clients , which may be mobile phones or network sensors ( Konečnỳ et al. , 2016b ; a ; McMahan et al. , 2017 ; Mohri et al. , 2019 ; Kairouz et al. , ... | This paper proposes a way to apply various variance reduction/momentum based method to the federated learning scenario, especially when there is distribution drift among the clients. The main claim of this paper is that the global statistics (momentum, control variance et al) should be update at the server side only, w... | SP:7ec1aeb5e1e9e0ef6759fa1d57de00d2170526c8 |
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning | 1 INTRODUCTION . Federated learning has become an important paradigm in large-scale machine learning where the training data remains distributed over a large number of clients , which may be mobile phones or network sensors ( Konečnỳ et al. , 2016b ; a ; McMahan et al. , 2017 ; Mohri et al. , 2019 ; Kairouz et al. , ... | The paper proposes a new framework for solving federated learning. The authors consider a specific setting that there are many clients, and each client is allowed to compute the full gradient. The authors claim that the current setting’s main issue is the client drift, and the proposed framework can reduce such an issu... | SP:7ec1aeb5e1e9e0ef6759fa1d57de00d2170526c8 |
DQSGD: DYNAMIC QUANTIZED STOCHASTIC GRADIENT DESCENT FOR COMMUNICATION-EFFICIENT DISTRIBUTED LEARNING | 1 INTRODUCTION . Recently , with the booming of Artificial Intelligence ( AI ) , 5G wireless communications , and CyberPhysical Systems ( CPS ) , distributed learning plays an increasingly important role in improving the efficiency and accuracy of learning , scaling to a large input data size , and bridging different w... | This paper proposed an adaptive quantized method which is derived by minimizing a constrained quantization error bound. The theoretical analysis suggests adjusting the quantization level according to the gradient norm, convergence rate of the model, and the current iteration number. Theoretical results show that the dy... | SP:9378bcacf7befab93b6850366fea16d477c01dc6 |
DQSGD: DYNAMIC QUANTIZED STOCHASTIC GRADIENT DESCENT FOR COMMUNICATION-EFFICIENT DISTRIBUTED LEARNING | 1 INTRODUCTION . Recently , with the booming of Artificial Intelligence ( AI ) , 5G wireless communications , and CyberPhysical Systems ( CPS ) , distributed learning plays an increasingly important role in improving the efficiency and accuracy of learning , scaling to a large input data size , and bridging different w... | 1. The authors considered uniform upper bound of the stochastic gradients g_i. The authors may argue that "The classical theoretical analysis of SGD assumes that the stochastic gradients are uniformly bounded". But one can even strongly argue that this bound is actually $\infty$. Moreover, an even stronger argument can... | SP:9378bcacf7befab93b6850366fea16d477c01dc6 |
Addressing Some Limitations of Transformers with Feedback Memory | 1 INTRODUCTION . In recent years , the Transformer architecture ( Vaswani et al. , 2017 ) has brought large improvements to a wide range of Natural Language Processing tasks such as machine translation , sentence representation ( Devlin et al. , 2019 ) , and summarization ( Edunov et al. , 2019 ) . Transformers are als... | > Summary: This paper proposes some changes to the classical Transformer architecture to address its major limitations, such as limited access to higher-level representations. It specifically introduces recurrence to the Transformer architecture by feeding the activations of all previous time steps to a later time step... | SP:0a31bc4cda9cbbfc9b5ab0e951eb529579842300 |
Addressing Some Limitations of Transformers with Feedback Memory | 1 INTRODUCTION . In recent years , the Transformer architecture ( Vaswani et al. , 2017 ) has brought large improvements to a wide range of Natural Language Processing tasks such as machine translation , sentence representation ( Devlin et al. , 2019 ) , and summarization ( Edunov et al. , 2019 ) . Transformers are als... | This paper modifies transformers with feedback memory. Specifically, for each timestep, it merges hidden representations of all layers into a high-level single vector and stores it in memory. For the current timestep, it attends past memory vectors. The authors claim that in this way, low layers of the current timestep... | SP:0a31bc4cda9cbbfc9b5ab0e951eb529579842300 |
Information Theoretic Meta Learning with Gaussian Processes | 1 INTRODUCTION . Meta learning ( Ravi & Larochelle , 2017 ; Vinyals et al. , 2016 ; Edwards & Storkey , 2017 ; Finn et al. , 2017 ; Lacoste et al. , 2019 ; Nichol et al. , 2018 ) and few-shot learning ( Li et al. , 2006 ; Lake et al. , 2011 ) aim to derive data efficient learning algorithms that can rapidly adapt to ne... | The paper derives a meta-learning framework based on the information bottleneck principle. By adapting the variational approximation proposed in [1] to the meta-learning setting, the authors come up with a tractable objective that generalises both gradient based and memory meta-learning methods. Based on this framework... | SP:05b195c7ce6d65c3a48ac79b6fe9d511ae5a3b5d |
Information Theoretic Meta Learning with Gaussian Processes | 1 INTRODUCTION . Meta learning ( Ravi & Larochelle , 2017 ; Vinyals et al. , 2016 ; Edwards & Storkey , 2017 ; Finn et al. , 2017 ; Lacoste et al. , 2019 ; Nichol et al. , 2018 ) and few-shot learning ( Li et al. , 2006 ; Lake et al. , 2011 ) aim to derive data efficient learning algorithms that can rapidly adapt to ne... | The paper presents a method for Bayesian meta-learning. This method combines a NN feature extractor with a Gaussian Process on top. The GP kernel is linear. The information bottleneck is used to motivate a choice of approximate posterior. Using MAML to adapt the NN feature extractor weights improves performance of the ... | SP:05b195c7ce6d65c3a48ac79b6fe9d511ae5a3b5d |
How to Train Your Super-Net: An Analysis of Training Heuristics in Weight-Sharing NAS | 1 INTRODUCTION . Neural architecture search ( NAS ) has received growing attention in the past few years , yielding stateof-the-art performance on several machine learning tasks ( Liu et al. , 2019a ; Wu et al. , 2019 ; Chen et al. , 2019b ; Ryoo et al. , 2020 ) . One of the milestones that led to the popularity of NAS... | Neural Architecture Search (NAS) aims to find a model with the best possible accuracy (or best possible accuracy/size tradeoff) from within a human-defined search space. One popular strategy for speeding up NAS is to train a one-shot model -- a single set of shared weights -- that can then be used to evaluate any candi... | SP:cf90c83667e2974b91b0d93c12c5987491314005 |
How to Train Your Super-Net: An Analysis of Training Heuristics in Weight-Sharing NAS | 1 INTRODUCTION . Neural architecture search ( NAS ) has received growing attention in the past few years , yielding stateof-the-art performance on several machine learning tasks ( Liu et al. , 2019a ; Wu et al. , 2019 ; Chen et al. , 2019b ; Ryoo et al. , 2020 ) . One of the milestones that led to the popularity of NAS... | This work analyzes commonly used heuristics for training the supernet in weight sharing NAS. The authors first proposes a new metric, sparse Kendall-Tau, to measure the quality of the supernet. Then extensive experiments are conducted on three NAS benchmarks to empirically evaluate the heuristics, and pick the best set... | SP:cf90c83667e2974b91b0d93c12c5987491314005 |
Non-iterative Parallel Text Generation via Glancing Transformer | 1 INTRODUCTION . Non-autoregressive transformer ( NAT ) has attracted wide attention in neural machine translation ( Gu et al. , 2018 ) , which generates sentences simultaneously rather than sequentially . To enable parallel decoding , NAT imposes a conditional independence assumption among words in the output sentence... | The authors propose Glancing Transformer for single step parallel text generation. The approach is inspired from curriculum learning i.e. the training task is adaptively controlled based on the model's current performance. Specifically, the paper proposes a glancing strategy which compares the model's generation and re... | SP:dbc876d7c158f89a0f22a4688ff05abca2ad5ddc |
Non-iterative Parallel Text Generation via Glancing Transformer | 1 INTRODUCTION . Non-autoregressive transformer ( NAT ) has attracted wide attention in neural machine translation ( Gu et al. , 2018 ) , which generates sentences simultaneously rather than sequentially . To enable parallel decoding , NAT imposes a conditional independence assumption among words in the output sentence... | This submission improves non-autoregressive translation (NAT) by proposing a non-iterative parallel text generation model called Glancing Transformer (GLAT), which includes the explicit word dependency modeling in NAT via a proposed Glancing Language Model (GLM). Compared to previous work, biggest contribution of the p... | SP:dbc876d7c158f89a0f22a4688ff05abca2ad5ddc |
Sparse matrix products for neural network compression | 1 Introduction . The success of neural networks in the processing of structured data is in part due to their over-parametrization which plays a key role in their ability to learn rich features from the data ( Neyshabur et al. , 2018 ) . Unfortunately , this also makes most state-of-the-art models so huge that they are ... | The paper proposes compressing the layers of the neural networks using a product of sparse matrices. This approach is in line with the initial methods on neural network compression: direct (task-independent) compression of weights, which is followed by NN task-dependent fine-tuning. In this case, the direct compression... | SP:6e9cc976b5835221dd518f26e3a9beaaaf6b890a |
Sparse matrix products for neural network compression | 1 Introduction . The success of neural networks in the processing of structured data is in part due to their over-parametrization which plays a key role in their ability to learn rich features from the data ( Neyshabur et al. , 2018 ) . Unfortunately , this also makes most state-of-the-art models so huge that they are ... | The authors introduced a neural network compressing method, based on factorization of weight matrix to the products of multiple sparse matrices. The goal is to achieve high compression rate. The author used a previous algorithm (Palm4MSA) to implement the method. The experiment result is better than other low-rank-base... | SP:6e9cc976b5835221dd518f26e3a9beaaaf6b890a |
Linear Mode Connectivity in Multitask and Continual Learning | 1 INTRODUCTION . One major consequence of learning multiple tasks in a continual learning ( CL ) setting — where tasks are learned sequentially , and the model can only have access to one task at a time — is catastrophic forgetting ( McCloskey & Cohen , 1989 ) . This is in contrast to multitask learning ( MTL ) , where... | The paper studies the relation between the geometry of solutions of continual (CL) and multi-task learning (MTL). Towards this end, the authors empirically identify that all the solutions of CL (i.e. solutions obtained after each task) and MTL are connected by a linear region of low error. This is a very interesting fi... | SP:3a07b9f25dd5216e3183232f305f5eeb2333427e |
Linear Mode Connectivity in Multitask and Continual Learning | 1 INTRODUCTION . One major consequence of learning multiple tasks in a continual learning ( CL ) setting — where tasks are learned sequentially , and the model can only have access to one task at a time — is catastrophic forgetting ( McCloskey & Cohen , 1989 ) . This is in contrast to multitask learning ( MTL ) , where... | The paper starts by that observing the local minima obtained in a multi task scenario are connected with a linear path of low error regime to the local minima of each task in a continual learning scenario in contrast to the path between the different minima of tasks incrementally learned, provided the both training of ... | SP:3a07b9f25dd5216e3183232f305f5eeb2333427e |
Divide-and-Conquer Monte Carlo Tree Search | 1 INTRODUCTION . This is the first sentence of this paper , but it was not the first one we wrote . In fact , the entire introduction section was actually one of the last sections to be added to this manuscript . The discrepancy between the order of inception of ideas and the order of their presentation in this paper p... | This paper proposes Divide-and-Conquer Monte Carlo Tree Search (DC-MCTS) for for goal-directed planning problems (i.e. problems where reaching a specific goal state is the objective, like traversing a maze with specified start and goal positions). The assumed setting is one where transition and reward models of the env... | SP:6deef1227ab2e0bf5dd2880ea7f3947490fb521d |
Divide-and-Conquer Monte Carlo Tree Search | 1 INTRODUCTION . This is the first sentence of this paper , but it was not the first one we wrote . In fact , the entire introduction section was actually one of the last sections to be added to this manuscript . The discrepancy between the order of inception of ideas and the order of their presentation in this paper p... | This paper proposes Divide-and-Conquer Monte Carlo Tree Search (DC-MCTS), a planning algorithm for goal-directed decision-making problems, which makes a plan of the trajectory via recursive hierarchical partitioning. DC-MCTS assumes a (suboptimal) goal-directed low-level policy and its oracle value function. Then, it f... | SP:6deef1227ab2e0bf5dd2880ea7f3947490fb521d |
Asynchronous Advantage Actor Critic: Non-asymptotic Analysis and Linear Speedup | Asynchronous and parallel implementation of standard reinforcement learning ( RL ) algorithms is a key enabler of the tremendous success of modern RL . Among many asynchronous RL algorithms , arguably the most popular and effective one is the asynchronous advantage actor-critic ( A3C ) algorithm . Although A3C is becom... | This paper studied the two time scale A3C in discounted MDP based on recent development in the finite sample analysis of A2C. The sample complexity result in this paper matches previous result in two time-scale A2C in terms of the dependence of \epsilon, and this paper further shows the benefit of "linear speed up" bro... | SP:7a1fd3da1fb6af86b3a25f133d0cfe1fa23b71fa |
Asynchronous Advantage Actor Critic: Non-asymptotic Analysis and Linear Speedup | Asynchronous and parallel implementation of standard reinforcement learning ( RL ) algorithms is a key enabler of the tremendous success of modern RL . Among many asynchronous RL algorithms , arguably the most popular and effective one is the asynchronous advantage actor-critic ( A3C ) algorithm . Although A3C is becom... | This paper revisits the A3C algorithm with TD(0) for the critic update to provide better theoretical analysis of A3C. A3C-TD(0) achieves linear speedup and it also matches our intuition. To show the empirical results, the authors provide convergence results of A3C-TD(0) with Markovian sampling in synthetic environments... | SP:7a1fd3da1fb6af86b3a25f133d0cfe1fa23b71fa |
Dataset Condensation with Gradient Matching | 1 INTRODUCTION . Large-scale datasets , comprising millions of samples , are becoming the norm to obtain state-ofthe-art machine learning models in multiple fields including computer vision , natural language processing and speech recognition . At such scales , even storing and preprocessing the data becomes burdensome... | The paper proposes a novel dataset condensation technique that generates synthetic samples by matching model gradients with those obtained on the original input dataset. This technique is investigated empirically on several smaller datasets like MNIST, SVHN and CIFAR10. Two applications to continual learning and neural... | SP:6d80f796adf8ca9c35f6fb2eee898eab1d71ad8e |
Dataset Condensation with Gradient Matching | 1 INTRODUCTION . Large-scale datasets , comprising millions of samples , are becoming the norm to obtain state-ofthe-art machine learning models in multiple fields including computer vision , natural language processing and speech recognition . At such scales , even storing and preprocessing the data becomes burdensome... | This paper tackles the challenging dataset condensation problem. The goal is to learn to synthesize a small dataset, so that a neural network trained on the small synthetic dataset can have similar performance as a network trained on the full dataset. The proposed method tackles the problem by gradient matching. The pr... | SP:6d80f796adf8ca9c35f6fb2eee898eab1d71ad8e |
Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks | 1 INTRODUCTION . Deep neural networks have driven a shift from feature engineering to feature learning . The great progress largely comes from well-designed networks with increasing capacity of models ( He et al. , 2016a ; Xie et al. , 2017 ; Huang et al. , 2017 ; Tan & Le , 2019 ) . To achieve the superior performance... | This work proposes a novel method, called Dynamic Graph Network (DG-Net), for optimizing the architecture of a neural network. Building on the previous work introduced by (Xie et al., 2019), the authors propose to consider the network as a complete directed acyclic graph (DAG). Then, the edge weights of the DAG are gen... | SP:925ffc4463ef78ca77f5ae77a63b86d7fa87a1cd |
Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks | 1 INTRODUCTION . Deep neural networks have driven a shift from feature engineering to feature learning . The great progress largely comes from well-designed networks with increasing capacity of models ( He et al. , 2016a ; Xie et al. , 2017 ; Huang et al. , 2017 ; Tan & Le , 2019 ) . To achieve the superior performance... | This paper presents a novel approach (DG-Net) to “generate” a dynamic structure for the neural network, by learning to predict and select the edges between computational nodes in an end-to-end manner. The method is based on a gating mechanism, applied on top of a fully connected graph (similar to the connectivity in a ... | SP:925ffc4463ef78ca77f5ae77a63b86d7fa87a1cd |
To Understand Representation of Layer-aware Sequence Encoders as Multi-order-graph | In this paper , we propose a unified explanation of representation for layer-aware neural sequence encoders , which regards the representation as a revisited multigraph called multi-order-graph ( MoG ) , so that model encoding can be viewed as a processing to capture all subgraphs in MoG . The relationship reflected by... | The authors propose a new Transformer variant for neural machine translation. Compared with the standard Transformer framework, this work explains the representation generation process of the encoder via a multi-ordered-graph MoG and develops a novel Graph-Transformer method based on MoG, which is capable of capturing ... | SP:548aec7a3eab3e843017e91576c97c1c85c359f4 |
To Understand Representation of Layer-aware Sequence Encoders as Multi-order-graph | In this paper , we propose a unified explanation of representation for layer-aware neural sequence encoders , which regards the representation as a revisited multigraph called multi-order-graph ( MoG ) , so that model encoding can be viewed as a processing to capture all subgraphs in MoG . The relationship reflected by... | The paper proposes a new multigraph architecture called Multi-Order-Graph to explain the representation generation process in neural sequence encoders (Self-Attention or SAN based models). The main contribution of this MoG is the introduction of n-order dependency which can model not only relationships between words bu... | SP:548aec7a3eab3e843017e91576c97c1c85c359f4 |
Average-case Acceleration for Bilinear Games and Normal Matrices | 1 INTRODUCTION . The traditional analysis of optimization algorithms is a worst-case analysis ( Nemirovski , 1995 ; Nesterov , 2004 ) . This type of analysis provides a complexity bound for any input from a function class , no matter how unlikely . However , since hard-to-solve inputs might rarely occur in practice , t... | In this submission, first-order methods for solving smooth games are studied in the average case. In particular, first-order methods are derived and studied that are average-case optimal for certain optimization problems. In particular average-optimal first-order methods for solving zero-sum minimax games are presented... | SP:ff34a84b45570d684598dda4a9cd63be2a459e51 |
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