paper_name stringlengths 11 170 | text stringlengths 8.07k 307k | summary stringlengths 152 6.16k | paper_id stringlengths 43 43 |
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Semantically-Adaptive Upsampling for Layout-to-Image Translation | 1 INTRODUCTION . The layout-to-image translation task aims to translate input layouts to realistic images ( see Fig . 1 ( a ) ) , which have many real-world applications and draw much attention from the community ( Park et al. , 2019 ; Liu et al. , 2019 ; Jiang et al. , 2020 ; Tang et al. , 2020 ) . For example , Park ... | This paper presents a module named semantically-adaptive upsampling (SA-UpSample) to achieve layout-to-image translation. The proposed method is able to aggregate semantic information in the layout input and adaptively conducts class-specific upsampling in the translation process. Experiments on six datasets demonstrat... | SP:9cc61518a96c3aebe0869a87fc84ea2e13c752e5 |
Unconditional Synthesis of Complex Scenes Using a Semantic Bottleneck | 1 INTRODUCTION . Significant strides have been made on generative models for image synthesis , with a variety of methods based on Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) achieving stateof-the-art performance . At lower resolutions or in specialized domains , GAN-based methods are able to s... | 1. Summary. This paper considers a two-stage procedure of generation complex scenes (cityscapes or living rooms, i.e. without a central object): Firstly, a noise vector is mapped to a discrete semantic map (Gumbel softmax and straight-through estimator are applied). Secondly, the obtained segmentation map is translated... | SP:27acf2abf315fd87b9e382ee3fe28ec903074b9d |
Unconditional Synthesis of Complex Scenes Using a Semantic Bottleneck | 1 INTRODUCTION . Significant strides have been made on generative models for image synthesis , with a variety of methods based on Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) achieving stateof-the-art performance . At lower resolutions or in specialized domains , GAN-based methods are able to s... | This work proposes a novel approach for unconditioned image synthesis of complex scenes by intelligently coupling two major tasks; unconditional label generation and label-conditioned image synthesis. To overcome the limitation of failing to generate high-fidelity complex scenes using current GAN-based approaches, this... | SP:27acf2abf315fd87b9e382ee3fe28ec903074b9d |
Unconditional Synthesis of Complex Scenes Using a Semantic Bottleneck | 1 INTRODUCTION . Significant strides have been made on generative models for image synthesis , with a variety of methods based on Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) achieving stateof-the-art performance . At lower resolutions or in specialized domains , GAN-based methods are able to s... | In this paper, the authors propose a new paradigm for unconditional image synthesis with semantic layouts as the bottleneck. The presented approach is straightforward: we can first sample a semantic layout from a latent variable, and then perform image synthesis from this semantic layout. The proposed method is able to... | SP:27acf2abf315fd87b9e382ee3fe28ec903074b9d |
Trust, but verify: model-based exploration in sparse reward environments | 1 INTRODUCTION . Model-based approach to Reinforcement Learning ( RL ) brings a promise of data efficiency , and with it much greater generality . However , it is still largely an open question of how to make robust model-based RL algorithms . In most cases , the current solutions excel in low sample regime but underpe... | This paper presents a new method which can be combined with graph search algorithms to boost exploration when the uncertainty is high. This new mechanism, called TBV, can override actions given by the model to explore and verify model predictions. It is also shown in the experiments that TBV improves the model performa... | SP:046f850f529bcb0c2376c8597a24108d67c4323a |
Trust, but verify: model-based exploration in sparse reward environments | 1 INTRODUCTION . Model-based approach to Reinforcement Learning ( RL ) brings a promise of data efficiency , and with it much greater generality . However , it is still largely an open question of how to make robust model-based RL algorithms . In most cases , the current solutions excel in low sample regime but underpe... | This paper proposes an approach for encouraging exploration when planning over learned models of discrete reinforcement learning environment. The proposed method involves using an uncertainty-aware model (e.g., an ensemble of neural networks) to predict state-action transitions, together with a graph-based planner oper... | SP:046f850f529bcb0c2376c8597a24108d67c4323a |
Trust, but verify: model-based exploration in sparse reward environments | 1 INTRODUCTION . Model-based approach to Reinforcement Learning ( RL ) brings a promise of data efficiency , and with it much greater generality . However , it is still largely an open question of how to make robust model-based RL algorithms . In most cases , the current solutions excel in low sample regime but underpe... | The authors present a method to guide exploration that prefers to go to areas of the state space for which it is more uncertain. This uncertainty is obtained by measuring the standard deviation of the next state prediction from an ensemble of models. The authors call this the disagreement measure At each step, a search... | SP:046f850f529bcb0c2376c8597a24108d67c4323a |
Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix | 1 INTRODUCTION E Xc YXs Figure 1 : The causal graph in OOD prediction : P ( Y |Xc ) is invariant across domains . The spurious correlation P ( Y |Xs ) may vary . A directed ( bidirected ) edge is a causal relationship ( correlation ) . Strong empirical results have demonstrated the efficacy of deep neural networks ( DN... | In this work, the authors focus on the out-of-distribution generalization problem. The input is dataset from multiple environments and the goal is to learn a model that generalizes well to an unseen test environment. The work is based on recent line of works on invariant risk minimization (IRM) (Arjovsky et al.). The a... | SP:82c52fe144129e913a29248317952ac0fb520ffe |
Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix | 1 INTRODUCTION E Xc YXs Figure 1 : The causal graph in OOD prediction : P ( Y |Xc ) is invariant across domains . The spurious correlation P ( Y |Xs ) may vary . A directed ( bidirected ) edge is a causal relationship ( correlation ) . Strong empirical results have demonstrated the efficacy of deep neural networks ( DN... | This paper attacks the problem of OOD learning from the angle of invariant causal feature learning. The key idea is to capture domain invariant causal features and use the extracted causality relation to convey domain-adaptive classification. In this work, domain invariant causal features are learned by IRM, which impo... | SP:82c52fe144129e913a29248317952ac0fb520ffe |
Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix | 1 INTRODUCTION E Xc YXs Figure 1 : The causal graph in OOD prediction : P ( Y |Xc ) is invariant across domains . The spurious correlation P ( Y |Xs ) may vary . A directed ( bidirected ) edge is a causal relationship ( correlation ) . Strong empirical results have demonstrated the efficacy of deep neural networks ( DN... | .** This paper advances generalizable machine learning via addressing a major limitation of invariant risk minimization (IRM). In particular, the author(s) identified and discussed the issue of strong $\Lambda$ spurious, where spurious features and class labels are strongly correlated due to common cause, causing unpro... | SP:82c52fe144129e913a29248317952ac0fb520ffe |
Continual learning using hash-routed convolutional neural networks | Continual learning could shift the machine learning paradigm from data centric to model centric . A continual learning model needs to scale efficiently to handle semantically different datasets , while avoiding unnecessary growth . We introduce hash-routed convolutional neural networks : a group of convolutional units ... | This paper studies the problem of continual learning and proposes a new learning framework named hash-routed convolutional neural networks (HRN). HRN has a set of convolutional units and hashes similar data to the same unit. With this design, the paper claims three key contributions. (1) HRN provides excellent plastici... | SP:5363b2718ed0168fe5a24e0e6a63c62b20c47c6f |
Continual learning using hash-routed convolutional neural networks | Continual learning could shift the machine learning paradigm from data centric to model centric . A continual learning model needs to scale efficiently to handle semantically different datasets , while avoiding unnecessary growth . We introduce hash-routed convolutional neural networks : a group of convolutional units ... | For a learning model to learn continuously, it needs to handle new datasets without catastrophic forgetting or requiring the model to grow larger. This paper proposes a hash-routed convolution neural network where a different set of convolution filters are used depending on the data. New convolution filters can be adde... | SP:5363b2718ed0168fe5a24e0e6a63c62b20c47c6f |
Continual learning using hash-routed convolutional neural networks | Continual learning could shift the machine learning paradigm from data centric to model centric . A continual learning model needs to scale efficiently to handle semantically different datasets , while avoiding unnecessary growth . We introduce hash-routed convolutional neural networks : a group of convolutional units ... | Ok, but not good enough. The authors present Hash-Routed Convolutional Neural Networks (HRNs), intended to enable learning of stable representations for continual learning, i.e. representations that change little for previously-learned tasks as more tasks are learned. The authors benchmark HRNs against several baseline... | SP:5363b2718ed0168fe5a24e0e6a63c62b20c47c6f |
Explicit Pareto Front Optimization for Constrained Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has shown great potential for training policies that optimize a single scalar reward . Recent approaches have exceeded human-level performance on Atari ( Mnih et al. , 2015 ) and Go ( Silver et al. , 2016 ) , and have also achieved impressive results in continuous con... | The paper is interested in reinforcement learning where one needs to satisfy constraints (for instance energy spent) in addition to maximizing rewards. The proposed approach proposes to extend any method able to approximate the Pareto front of optimal policies by also learning portions of the front that satisfy user co... | SP:d484cf1bcfab7795f88ea60a34ee9822be39e886 |
Explicit Pareto Front Optimization for Constrained Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has shown great potential for training policies that optimize a single scalar reward . Recent approaches have exceeded human-level performance on Atari ( Mnih et al. , 2015 ) and Go ( Silver et al. , 2016 ) , and have also achieved impressive results in continuous con... | In Reinforcement Learning (RL) it is common that one searches for policies that optimize the reward and that meet certain constraints. A common approach for handling the constraints is Lagrangian relaxation, i.e., to incorporate the constraints in some way into the objective. In this submission, a different approach is... | SP:d484cf1bcfab7795f88ea60a34ee9822be39e886 |
Explicit Pareto Front Optimization for Constrained Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has shown great potential for training policies that optimize a single scalar reward . Recent approaches have exceeded human-level performance on Atari ( Mnih et al. , 2015 ) and Go ( Silver et al. , 2016 ) , and have also achieved impressive results in continuous con... | This paper introduced a general framework that incorporates multi-object reinforcement learning(MORL) perspective for constrained reinforcement learning to a policy set, called Pareto front, that meets the constrained. The author has instantiated a method based on the previous method MO-MPO. Compared to previous Lagran... | SP:d484cf1bcfab7795f88ea60a34ee9822be39e886 |
Low Complexity Approximate Bayesian Logistic Regression for Sparse Online Learning | 1 INTRODUCTION . We consider online ( Bottou , 1998 ; Shalev-Shwartz et al. , 2011 ) binary logistic regression over a series of rounds t ∈ { 1 , 2 , . . . , T } . At round t , a sparse feature vector xt ∈ [ −1 , 1 ] d with dt d nonzero values , is revealed , and a prediction for the label yt ∈ { −1 , 1 } must be gener... | This paper proposes an algorithm for online logistic regression based on analytical formulas that approximate the Bayesian predictive posterior. These approximations are based on assuming a diagonal covariance Gaussian form for the posterior at each iteration that is optimized to fit the true posterior. Two alternati... | SP:7f9f0d2f54a3f42cceb03d9fce97dd36fd78fb6a |
Low Complexity Approximate Bayesian Logistic Regression for Sparse Online Learning | 1 INTRODUCTION . We consider online ( Bottou , 1998 ; Shalev-Shwartz et al. , 2011 ) binary logistic regression over a series of rounds t ∈ { 1 , 2 , . . . , T } . At round t , a sparse feature vector xt ∈ [ −1 , 1 ] d with dt d nonzero values , is revealed , and a prediction for the label yt ∈ { −1 , 1 } must be gener... | The paper proposes an algorithm for learning the parameters of a logistic regression model in an online setting. The proposed algorithm is based on two approximations: the posterior at iteration t over the model parameters is assumed to be multivariate Gaussian distribution with a diagonal covariance matrix and the log... | SP:7f9f0d2f54a3f42cceb03d9fce97dd36fd78fb6a |
Low Complexity Approximate Bayesian Logistic Regression for Sparse Online Learning | 1 INTRODUCTION . We consider online ( Bottou , 1998 ; Shalev-Shwartz et al. , 2011 ) binary logistic regression over a series of rounds t ∈ { 1 , 2 , . . . , T } . At round t , a sparse feature vector xt ∈ [ −1 , 1 ] d with dt d nonzero values , is revealed , and a prediction for the label yt ∈ { −1 , 1 } must be gener... | The authors propose a low complexity approximation method with closed analytic forms for doing logistic regression in the sparse, online setting. They first introduce the marginalized bayesian gaussian approximation approach, which essentially replaces the sigmoidal with a gaussian. They then give approximate expressio... | SP:7f9f0d2f54a3f42cceb03d9fce97dd36fd78fb6a |
Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications | While deep learning methods have shown great success in medical image analysis , they require a number of medical images to train . Due to data privacy concerns and unavailability of medical annotators , it is oftentimes very difficult to obtain a lot of labeled medical images for model training . In this paper , we st... | The authors propose an algorithm to enlarge the training set for image classification problems in certain medical applications where training data of the target modality is scarce. They do so by training an unpaired image-to-image translator network and an image classifier end-to-end in order to utilize labeled images ... | SP:2c70c132373daf3644f009748f343decdc4886ba |
Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications | While deep learning methods have shown great success in medical image analysis , they require a number of medical images to train . Due to data privacy concerns and unavailability of medical annotators , it is oftentimes very difficult to obtain a lot of labeled medical images for model training . In this paper , we st... | This paper studies the problem of learning a predictor from a specified modality using a dataset where each example has images from only one modality. The proposed approach is to set up a CycleGAN to translate between the modalities and a predictor from the required modality to the prediction target. The authors propos... | SP:2c70c132373daf3644f009748f343decdc4886ba |
Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications | While deep learning methods have shown great success in medical image analysis , they require a number of medical images to train . Due to data privacy concerns and unavailability of medical annotators , it is oftentimes very difficult to obtain a lot of labeled medical images for model training . In this paper , we st... | This paper discusses an approach to augment a medical imaging dataset using images from another modality. The images in the other (i.e. source) modality should have been originally collected & labeled to perform the same discrimination or regression task as the target modality. A network consisting of a prediction netw... | SP:2c70c132373daf3644f009748f343decdc4886ba |
Conformation-Guided Molecular Representation with Hamiltonian Neural Networks | 1 INTRODUCTION . The past several years have seen a prevalence of the intersection between medical chemistry and deep learning . Remarkable progress has been made in various applications on small molecules , ranging from generation ( Jin et al. , 2018 ; You et al. , 2018 ) and property prediction ( Gilmer et al. , 2017... | This paper proposes to use 3d conformations for learning molecular fingerprints by 1) training a generative model to predict the 3d coordinates and 2) use those to train a "fingerprint generator" to obtain fingerprints by learning to predict molecular properties. The paper is well structured and clearly written. The in... | SP:5ae0998f30b7d1308c97b68fc8f840a6de3ffeee |
Conformation-Guided Molecular Representation with Hamiltonian Neural Networks | 1 INTRODUCTION . The past several years have seen a prevalence of the intersection between medical chemistry and deep learning . Remarkable progress has been made in various applications on small molecules , ranging from generation ( Jin et al. , 2018 ; You et al. , 2018 ) and property prediction ( Gilmer et al. , 2017... | The paper proposes a new method for generating fingerprints for small molecules. It is based on two components: a "Hamiltonian Engine" that runs a brief simulation, predicting the structure of the small molecule by minimizing a learned potential energy, and 2) a message passing algorithm that uses the predicted structu... | SP:5ae0998f30b7d1308c97b68fc8f840a6de3ffeee |
Conformation-Guided Molecular Representation with Hamiltonian Neural Networks | 1 INTRODUCTION . The past several years have seen a prevalence of the intersection between medical chemistry and deep learning . Remarkable progress has been made in various applications on small molecules , ranging from generation ( Jin et al. , 2018 ; You et al. , 2018 ) and property prediction ( Gilmer et al. , 2017... | This paper presents a novel neural network module called Hamiltonian Neural Networks to learn representation of molecules. The module consists of two main components: 1) Hamiltonian Engine (HE), and 2) Fingerprint Generator (FG). The HE 1) takes a molecular graph with atom and bond features as inputs, and first generat... | SP:5ae0998f30b7d1308c97b68fc8f840a6de3ffeee |
Revisiting BFfloat16 Training | State-of-the-art generic low-precision training algorithms use a mix of 16-bit and 32-bit precision , creating the folklore that 16-bit precision alone is not enough to maximize model accuracy . As a result , deep learning accelerators are forced to support both 16-bit and 32-bit compute units which is more costly than... | This work reinvigorates half precision training as an alternative to either full single precision or mixed half and single precision. The authors demonstrate that the nearest rounding is the culprit for the worse performance of half precision training compared to single precision, due to cancelling small updates. They ... | SP:eee5cfc44781aebad99973ffaaaa0a5c5acecef8 |
Revisiting BFfloat16 Training | State-of-the-art generic low-precision training algorithms use a mix of 16-bit and 32-bit precision , creating the folklore that 16-bit precision alone is not enough to maximize model accuracy . As a result , deep learning accelerators are forced to support both 16-bit and 32-bit compute units which is more costly than... | This paper explores the possibilities of reducing the precision of the weight update operation (i.e. AXPY ops) from 32 bit to 16 bit in today’s BFloat16 training framework. To enable 16 bit update, the authors proposed two techniques, i.e. stochastic rounding and Kahan summation. The authors use a simple least-squares ... | SP:eee5cfc44781aebad99973ffaaaa0a5c5acecef8 |
Revisiting BFfloat16 Training | State-of-the-art generic low-precision training algorithms use a mix of 16-bit and 32-bit precision , creating the folklore that 16-bit precision alone is not enough to maximize model accuracy . As a result , deep learning accelerators are forced to support both 16-bit and 32-bit compute units which is more costly than... | I think the use of QPyTorch for the experiments here invalidates the results since the intermediate matrix multiplies are done in single precision (FP32), and so are more optimistic than a pure 16-bit implementation. (This is both according to the authors Sec 4, experiment setup; and according to the QPyTorch paper arx... | SP:eee5cfc44781aebad99973ffaaaa0a5c5acecef8 |
Accurate and fast detection of copy number variations from short-read whole-genome sequencing with deep convolutional neural network | 1 INTRODUCTION . A copy number variant ( CNV ) is a genetic mutation where a stretch of DNA is completely lost or repeated more than once compared to a reference genome . CNV sizes range from 50 bases to 3 million bases or more with two major types : duplication if the DNA sequence is repeated and deletion if a DNA seq... | The authors proposed CNV-Net, a deep learning-based approach for copy number variation identification. They encoded mapped DNA sequences into a pileup image that captures reference sequence, sequencing coverage, and mapped reads. Then, they used CNNs to classify it into deletions, duplications, or non-breakpoints. They... | SP:93cd389d63117a27a7f152c06bfb32a123e73377 |
Accurate and fast detection of copy number variations from short-read whole-genome sequencing with deep convolutional neural network | 1 INTRODUCTION . A copy number variant ( CNV ) is a genetic mutation where a stretch of DNA is completely lost or repeated more than once compared to a reference genome . CNV sizes range from 50 bases to 3 million bases or more with two major types : duplication if the DNA sequence is repeated and deletion if a DNA seq... | They authors describe a method to detect structural variation from aligned sequencing reads in a genome browser view. Their model encodes this genome browser view into an RGB image and applies a deep convolutional neural network to classify variant type (or no variant). They make use of curated variant annotations to t... | SP:93cd389d63117a27a7f152c06bfb32a123e73377 |
Accurate and fast detection of copy number variations from short-read whole-genome sequencing with deep convolutional neural network | 1 INTRODUCTION . A copy number variant ( CNV ) is a genetic mutation where a stretch of DNA is completely lost or repeated more than once compared to a reference genome . CNV sizes range from 50 bases to 3 million bases or more with two major types : duplication if the DNA sequence is repeated and deletion if a DNA seq... | The authors in the paper describe a deep learning approach to detect copy number variants (CNVs) from DNA sequencing data, CNV-Net. It described the approach by transforming the pileups into images and pass them through a CNN. This strategy has been proposed four or five years back to do SNPs and indels calling (DeepV... | SP:93cd389d63117a27a7f152c06bfb32a123e73377 |
Contrastive Learning with Hard Negative Samples | 1 INTRODUCTION . Owing to their empirical success , contrastive learning methods ( Chopra et al. , 2005 ; Hadsell et al. , 2006 ) have become one of the most popular self-supervised approaches for learning representations ( Oord et al. , 2018 ; Tian et al. , 2019 ; Chen et al. , 2020a ) . In computer vision , unsupervi... | This paper investigated how to sample informative/hard negative examples for self-supervised contrastive learning without label information. To tackle this challenge, this paper proposed an efficient tunable sampling distribution to select negative samples that are similar to the query when the true label or similarity... | SP:e79a01ae3e15c0ac52ca8c603c9d131ad14bda52 |
Contrastive Learning with Hard Negative Samples | 1 INTRODUCTION . Owing to their empirical success , contrastive learning methods ( Chopra et al. , 2005 ; Hadsell et al. , 2006 ) have become one of the most popular self-supervised approaches for learning representations ( Oord et al. , 2018 ; Tian et al. , 2019 ; Chen et al. , 2020a ) . In computer vision , unsupervi... | The paper proposes a novel noise contrastive estimation (NCE) objective that incorporates hard-negative samples without similarity supervision, e.g., assuming unsupervised learning. To this end, it modifies the denominator of the original NCE by (a) re-weighting the negative samples based on the euclidean distance from... | SP:e79a01ae3e15c0ac52ca8c603c9d131ad14bda52 |
Contrastive Learning with Hard Negative Samples | 1 INTRODUCTION . Owing to their empirical success , contrastive learning methods ( Chopra et al. , 2005 ; Hadsell et al. , 2006 ) have become one of the most popular self-supervised approaches for learning representations ( Oord et al. , 2018 ; Tian et al. , 2019 ; Chen et al. , 2020a ) . In computer vision , unsupervi... | In this paper, the authors mainly study how to sample good/informative negative examples for contrastive learning. The key challenge is the unsupervision in contrastive methods. They propose a new unsupervised method to select the hard-negative samples with user control. The experimental results on three modalities (i... | SP:e79a01ae3e15c0ac52ca8c603c9d131ad14bda52 |
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate | 1 INTRODUCTION . Stochastic gradient descent ( SGD ) and its variants play a key role in training deep learning models . From the optimization perspective , SGD is favorable in many aspects , e.g. , scalability for large-scale models ( He et al. , 2016 ) , parallelizability with big training data ( Goyal et al. , 2017 ... | In this paper, an implicit bias of SGD and GD in terms of the direction of convergence points is studied. This study shows that, in a setting of linear regression, SGD and GD converge to different directions, which are determined by the largest/smallest eigenvectors of a data matrix when the learning rate is moderately... | SP:b953f98385a35b4d1a1c7bd73f9eb64c470a4b80 |
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate | 1 INTRODUCTION . Stochastic gradient descent ( SGD ) and its variants play a key role in training deep learning models . From the optimization perspective , SGD is favorable in many aspects , e.g. , scalability for large-scale models ( He et al. , 2016 ) , parallelizability with big training data ( Goyal et al. , 2017 ... | In the paper the authors analyzed the convergence dynamics toward a minimum of gradient descent (GD) and stochastic gradient descent (SGD). The algorithms are considered to start in the basin of attraction of a minimum, and using discrete steps they approach the bottom. The main result of the paper concerns the fact th... | SP:b953f98385a35b4d1a1c7bd73f9eb64c470a4b80 |
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate | 1 INTRODUCTION . Stochastic gradient descent ( SGD ) and its variants play a key role in training deep learning models . From the optimization perspective , SGD is favorable in many aspects , e.g. , scalability for large-scale models ( He et al. , 2016 ) , parallelizability with big training data ( Goyal et al. , 2017 ... | This paper analyzes the differences in the convergence of SGD and GD when using "small" and "moderate" learning rates to shed light on why SGD with "moderate" and annealing learning rates perform well in practice. Focusing on an overparametrized linear regression problem, the paper claims that SGD with a "moderate" lea... | SP:b953f98385a35b4d1a1c7bd73f9eb64c470a4b80 |
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning | 1 INTRODUCTION . One of the most remarkable aspects of human intelligence is the ability to quickly adapt to new tasks and environments . From a young age , children are able to acquire new skills and solve new tasks through imitation and instruction ( Council et al. , 2000 ; Meltzoff , 1988 ; Hunt , 1965 ) . The key i... | In this paper, the authors present a system that exploits both natural-language instructions, and demonstrations, to learn how to perform multi-subtask tasks in a Minecraft-like environment. The system has two objectives: First, given a state and objective, learn to generate a natural language description of the high-... | SP:f2ba107be2c91442139b0e67d5612571a330c901 |
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning | 1 INTRODUCTION . One of the most remarkable aspects of human intelligence is the ability to quickly adapt to new tasks and environments . From a young age , children are able to acquire new skills and solve new tasks through imitation and instruction ( Council et al. , 2000 ; Meltzoff , 1988 ; Hunt , 1965 ) . The key i... | This paper proposes to use natural language to aid reinforcement learning by generating instructions for sub-goals that allow the agent to complete tasks with delayed rewards. The authors first design a multi-task crafting environment and collect step-by-step human demonstrations along with sub-goal instructions using ... | SP:f2ba107be2c91442139b0e67d5612571a330c901 |
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning | 1 INTRODUCTION . One of the most remarkable aspects of human intelligence is the ability to quickly adapt to new tasks and environments . From a young age , children are able to acquire new skills and solve new tasks through imitation and instruction ( Council et al. , 2000 ; Meltzoff , 1988 ; Hunt , 1965 ) . The key i... | This paper studies the problem of generating natural language instructions to guide a policy to generalize to new environments where reward function is not given but a small set of demonstrations can be provided. A natural language generation (NLG) LSTM network and an initial policy network is trained from a labeled da... | SP:f2ba107be2c91442139b0e67d5612571a330c901 |
Gradient-based tuning of Hamiltonian Monte Carlo hyperparameters | 1 INTRODUCTION . Hamiltonian Monte Carlo ( HMC ) is a popular sampling based method for performing accurate inference on complex distributions that we may only know up to a normalization constant ( Neal , 2011 ) . Unfortunately , HMC can be slow to run in practice as we need to allow time for the simulation to ‘ burn-i... | This paper proposes a variational inference based framework to tune some of the hyper-parameters of HMC algorithms automatically. The authors drop the entropy term from regular ELBO formulation, which facilitates a gradient-based approach. However, dropping this term requires extra care for which authors offer an autom... | SP:8a2a34d242071dfbb207d394fec3c87517f19cc2 |
Gradient-based tuning of Hamiltonian Monte Carlo hyperparameters | 1 INTRODUCTION . Hamiltonian Monte Carlo ( HMC ) is a popular sampling based method for performing accurate inference on complex distributions that we may only know up to a normalization constant ( Neal , 2011 ) . Unfortunately , HMC can be slow to run in practice as we need to allow time for the simulation to ‘ burn-i... | The paper introduces a gradient-based approach for tuning the step-size and the diagonal mass matrix of HMC together with the parameters of an initial distribution for the Markov chain. They suggest different objectives amenable for SGD: maximize the expected target log-density of the final state of the chain, but also... | SP:8a2a34d242071dfbb207d394fec3c87517f19cc2 |
Gradient-based tuning of Hamiltonian Monte Carlo hyperparameters | 1 INTRODUCTION . Hamiltonian Monte Carlo ( HMC ) is a popular sampling based method for performing accurate inference on complex distributions that we may only know up to a normalization constant ( Neal , 2011 ) . Unfortunately , HMC can be slow to run in practice as we need to allow time for the simulation to ‘ burn-i... | The paper proposes a method to optimize the parameters of the Hybrid Monte Carlo (HMC) algorithm (the step size and the diagonal of momentum's covariance matrix). In order to do that, the authors consider the distribution of samples q_T() obtained after T iterations of the algorithm (T accept/reject steps) starting fro... | SP:8a2a34d242071dfbb207d394fec3c87517f19cc2 |
Knowledge distillation via softmax regression representation learning | 1 INTRODUCTION . Recently , there has been a great amount of research effort to make Convolutional Neural Networks ( CNNs ) lightweight so that they can be deployed in devices with limited resources . To this end , several approaches for model compression have been proposed , including network pruning ( Han et al. , 20... | This paper proposes a new formulation of knowledge distillation (KD) for model compression. Different from the classic formulation that matches the logits between student and teacher models, this paper suggests to match the output features of the penultimate layers between student and teacher models, based on L2 distan... | SP:4ad6e101af93b83ea1704de6137d2ddd52f6f5ae |
Knowledge distillation via softmax regression representation learning | 1 INTRODUCTION . Recently , there has been a great amount of research effort to make Convolutional Neural Networks ( CNNs ) lightweight so that they can be deployed in devices with limited resources . To this end , several approaches for model compression have been proposed , including network pruning ( Han et al. , 20... | This paper proposes a new knowledge distillation method by enhancing the student network's representation learning. The proposed framework is very simple: use the teacher's final fully-connected layer (or, 'projection matrix $W$' in the paper) to obtain both the teacher's and the student's logit output and optimize the... | SP:4ad6e101af93b83ea1704de6137d2ddd52f6f5ae |
Knowledge distillation via softmax regression representation learning | 1 INTRODUCTION . Recently , there has been a great amount of research effort to make Convolutional Neural Networks ( CNNs ) lightweight so that they can be deployed in devices with limited resources . To this end , several approaches for model compression have been proposed , including network pruning ( Han et al. , 20... | This paper proposed a novel method for knowledge distillation. The idea is to utilize the teacher’s pre-trained classifier to train the student’s penultimate layer feature by adopting two losses: (a) the Feature Matching loss LFM and (b) the Softmax Regression loss LSR. The latter is designed to take into account the c... | SP:4ad6e101af93b83ea1704de6137d2ddd52f6f5ae |
Improved Uncertainty Post-Calibration via Rank Preserving Transforms | 1 INTRODUCTION . Modern machine learning models such as deep neural networks have achieved high performance on many challenging tasks , and have been put into production that impacts billions of people ( LeCun et al. , 2015 ) . It is increasingly critical that the outputs of these models are comprehensible and safe to ... | This paper proposes a post-calibration technique that is meant to be more powerful than temperature scaling without introducing overfitting. The authors argue that previous attempts to generalize temperature scaling have a tendency to overfit not because of the additional parameters, but rather because they are not ran... | SP:6dfa69b550d13cf14a3f1492245913b5895cb44b |
Improved Uncertainty Post-Calibration via Rank Preserving Transforms | 1 INTRODUCTION . Modern machine learning models such as deep neural networks have achieved high performance on many challenging tasks , and have been put into production that impacts billions of people ( LeCun et al. , 2015 ) . It is increasingly critical that the outputs of these models are comprehensible and safe to ... | This work proposes a method for calibrating outputs of deep neural networks using higher capacity learning than previously possible. This is accomplished by a “Neural Rank Preserving Transform” (NRPT) which is applied post-logit to the network outputs. The learned NRPT transform is monotonic, it preserves the rank of t... | SP:6dfa69b550d13cf14a3f1492245913b5895cb44b |
Improved Uncertainty Post-Calibration via Rank Preserving Transforms | 1 INTRODUCTION . Modern machine learning models such as deep neural networks have achieved high performance on many challenging tasks , and have been put into production that impacts billions of people ( LeCun et al. , 2015 ) . It is increasingly critical that the outputs of these models are comprehensible and safe to ... | This paper introduces a new post-processing calibration method that can keep classification ranking, like the simple (local) temperature scaling methods, but has larger capacity, comparable to matrix scaling. Essentially it tries to achieve the best of both approaches by avoiding the underfitting and overfitting issue... | SP:6dfa69b550d13cf14a3f1492245913b5895cb44b |
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations | 1 INTRODUCTION . Neural network is currently the state-of-the-art machine learning model in a variety of tasks , including computer vision , natural language processing , and game-playing , to name a few . In particular , feed-forward neural networks consists of layers of trainable model weights and activation function... | The paper investigates the effects of weight perturbations on the output margin for multiclass classifcation problems. The paper shows that robustness to weight perturbations can be bounded using the (1,\infty)-norm of the weight matrices. The paper then suggests that a low (1,\infty)-norm of the weight matrices leads ... | SP:d4689f4057f55060106e5a21480c88330ee0e446 |
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations | 1 INTRODUCTION . Neural network is currently the state-of-the-art machine learning model in a variety of tasks , including computer vision , natural language processing , and game-playing , to name a few . In particular , feed-forward neural networks consists of layers of trainable model weights and activation function... | The paper discusses learning neural network models under weight parameter perturbations. In particular the paper motivates the use of a new loss function (equation 9) based on the analysis of neural network robustness (Section 3.2, 3.3) and generalization properties (Section 3.4) to perturbations of the weight paramete... | SP:d4689f4057f55060106e5a21480c88330ee0e446 |
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations | 1 INTRODUCTION . Neural network is currently the state-of-the-art machine learning model in a variety of tasks , including computer vision , natural language processing , and game-playing , to name a few . In particular , feed-forward neural networks consists of layers of trainable model weights and activation function... | In this work, the authors theoretically analyze the robustness against weight perturbations in neural networks. Upper bounds of the pairwise class margin for single-layer, all-layer, and selected-layer perturbation are established. Based on the analysis, the authors propose novel robust surrogate loss functions for 0-1... | SP:d4689f4057f55060106e5a21480c88330ee0e446 |
Probabilistic Meta-Learning for Bayesian Optimization | Transfer and meta-learning algorithms leverage evaluations on related tasks in order to significantly speed up learning or optimization on a new problem . For applications that depend on uncertainty estimates , e.g. , in Bayesian optimization , recent probabilistic approaches have shown good performance at test time , ... | In the paper, a probabilistic method is proposed for Bayesian optimization transfer learning (or meta-learning). A latent representation is modeled separately for each task. The surrogate used in BO will be approximated by a neural network with its input being the test point and the latent representation of the task. T... | SP:967ddfd5afb989c6446dfba55c51d6815b208f94 |
Probabilistic Meta-Learning for Bayesian Optimization | Transfer and meta-learning algorithms leverage evaluations on related tasks in order to significantly speed up learning or optimization on a new problem . For applications that depend on uncertainty estimates , e.g. , in Bayesian optimization , recent probabilistic approaches have shown good performance at test time , ... | The paper proposes BaNNER, a new transfer learning approach to accelerate hyperparameter optimization based on related tasks. This is based on a generative model learning the data and task distributions. A number of experiments against standard BO and random search, as well as against ABLR, the most closely related tra... | SP:967ddfd5afb989c6446dfba55c51d6815b208f94 |
Probabilistic Meta-Learning for Bayesian Optimization | Transfer and meta-learning algorithms leverage evaluations on related tasks in order to significantly speed up learning or optimization on a new problem . For applications that depend on uncertainty estimates , e.g. , in Bayesian optimization , recent probabilistic approaches have shown good performance at test time , ... | In this paper, the authors introduce a technique for multitask Bayesian optimization based on meta learning. In general, there are two broad approaches to multi task learning in Bayesian optimization: sharing information between tasks by modelling the correlation between tasks (e.g., Swersky et al 2013), and sharing in... | SP:967ddfd5afb989c6446dfba55c51d6815b208f94 |
Forward Prediction for Physical Reasoning | 1 INTRODUCTION . When presented with a picture of a Rube Goldberg machine , we can predict how the machine works . We do so by using our intuitive understanding of concepts such as force , mass , energy , collisions , etc. , to imagine how the machine state would evolve once released . This ability allows us to solve r... | This paper investigates the performance of several state-of-the-art forward-prediction models in the complex physical-reasoning tasks of the PHYRE benchmark. The authors have provided thorough evaluations of the models by ablating on different ways of representing the state (object-based or pixel-based), forms of model... | SP:0fbc071cfafc8162c5a47c06af4234df08b388f8 |
Forward Prediction for Physical Reasoning | 1 INTRODUCTION . When presented with a picture of a Rube Goldberg machine , we can predict how the machine works . We do so by using our intuitive understanding of concepts such as force , mass , energy , collisions , etc. , to imagine how the machine state would evolve once released . This ability allows us to solve r... | This paper discusses the importance of forward prediction in physical reasoning, and particularly in the PHYRE benchmark: a dataset of physical tasks where the agent is asked to place a ball of a chosen radius in a 2d environment. The authors build a classifier that, given an initial state, predicts the probability of ... | SP:0fbc071cfafc8162c5a47c06af4234df08b388f8 |
Forward Prediction for Physical Reasoning | 1 INTRODUCTION . When presented with a picture of a Rube Goldberg machine , we can predict how the machine works . We do so by using our intuitive understanding of concepts such as force , mass , energy , collisions , etc. , to imagine how the machine state would evolve once released . This ability allows us to solve r... | This work investigates how a forward prediction model helps the physical reasoning task. The authors propose two variants of forward prediction model, i.e., object-based and pixel-based. The authors also design a classification model, taking predicted results as inputs, to evaluate the efficiency of the prediction mode... | SP:0fbc071cfafc8162c5a47c06af4234df08b388f8 |
Learning to Represent Action Values as a Hypergraph on the Action Vertices | 1 INTRODUCTION . Representation learning methods have helped shape recent progress in RL by enabling a capacity for learning good representations of state . This is in spite of the fact that , traditionally , representation learning was less often explored in the RL context . As such , the de facto representation learn... | This work focuses on learning action representations for problems involving high-dimensional action spaces. The aim is to build a flexible and general methodology for learning representations of multidimensional actions that can be combined with existing architectures (which mostly focus on learning state representatio... | SP:12cb2675bf50cbb33187941a81e0f25af862b756 |
Learning to Represent Action Values as a Hypergraph on the Action Vertices | 1 INTRODUCTION . Representation learning methods have helped shape recent progress in RL by enabling a capacity for learning good representations of state . This is in spite of the fact that , traditionally , representation learning was less often explored in the RL context . As such , the de facto representation learn... | The paper considers the problem of representation learning of actions, i.e., learning a decomposition of action-value function in multidimensional action spaces using hypergraphs. The key idea is to represent the actions as a hypergraph and learn a representation for each hyperedge in an arbitrary hypergraph. Consequen... | SP:12cb2675bf50cbb33187941a81e0f25af862b756 |
Learning to Represent Action Values as a Hypergraph on the Action Vertices | 1 INTRODUCTION . Representation learning methods have helped shape recent progress in RL by enabling a capacity for learning good representations of state . This is in spite of the fact that , traditionally , representation learning was less often explored in the RL context . As such , the de facto representation learn... | This paper incorporates a concept called hypergraph network into reinforcement learning. The idea of hypergraph is to extend edge to hyperedge where a set of vertices can be considered at the same time. This seems natural for scenarios like continuous action control with multi-dimension action space. From experimental ... | SP:12cb2675bf50cbb33187941a81e0f25af862b756 |
Truly Deterministic Policy Optimization | In this paper , we present a policy gradient method that avoids exploratory noise injection and performs policy search over the deterministic landscape . By avoiding noise injection all sources of estimation variance can be eliminated in systems with deterministic dynamics ( up to the initial state distribution ) . Sin... | The paper proposes a deterministic policy gradient method using the Wasserstein distance to quantify the difference of deterministic policies. It also introduces a new deterministic estimator for the policy gradient with theoretical justification. The paper uses a similar path as TRPO to first introduce a surrogate obj... | SP:1676af08ba2395fab80517e4ebc780a221431592 |
Truly Deterministic Policy Optimization | In this paper , we present a policy gradient method that avoids exploratory noise injection and performs policy search over the deterministic landscape . By avoiding noise injection all sources of estimation variance can be eliminated in systems with deterministic dynamics ( up to the initial state distribution ) . Sin... | This paper introduces a policy gradient method based on deterministic policies and deterministic gradient estimates. By assuming such a deterministic setting, the authors show that the proposed technique can estimate gradients on long-horizon tasks without the need to inject noise into the system for exploration. The a... | SP:1676af08ba2395fab80517e4ebc780a221431592 |
Truly Deterministic Policy Optimization | In this paper , we present a policy gradient method that avoids exploratory noise injection and performs policy search over the deterministic landscape . By avoiding noise injection all sources of estimation variance can be eliminated in systems with deterministic dynamics ( up to the initial state distribution ) . Sin... | This paper proposes a new deterministic policy gradient method (TDPO). The main idea and its derivations are based on the use of a deterministic Vine (DeVine) gradient estimator and the Wasserstein metric. The paper shows that a closed-form computation of Wasserstein distance can be derived without any approximation on... | SP:1676af08ba2395fab80517e4ebc780a221431592 |
Effective Subspace Indexing via Interpolation on Stiefel and Grassmann manifolds | 1 INTRODUCTION . Subspace selection algorithms have been successful in many application problems related to dimension reduction ( Zhou et al . ( 2010 ) , Bian & Tao ( 2011 ) , Si et al . ( 2010 ) , Zhang et al . ( 2009 ) ) , with applications including , e.g. , human face recognition ( Fu & Huang ( 2008 ) ) , speech an... | This paper proposes a subspace indexing model with interpolation (SIM-I) for dimension reduction. To capture the global nonlinearity and the local variation of data, SIM-I split the global space into a collection of local disjoint partitions by a kd-tree style scheme and build a subspace indexing model (e.g., LPP) for ... | SP:f14bb4150e95fa83a686a9a4b879cda9fc40a7bf |
Effective Subspace Indexing via Interpolation on Stiefel and Grassmann manifolds | 1 INTRODUCTION . Subspace selection algorithms have been successful in many application problems related to dimension reduction ( Zhou et al . ( 2010 ) , Bian & Tao ( 2011 ) , Si et al . ( 2010 ) , Zhang et al . ( 2009 ) ) , with applications including , e.g. , human face recognition ( Fu & Huang ( 2008 ) ) , speech an... | This paper presents an approach to low-dimensional embedding of data, with an emphasis on image datasets. The algorithm proceeds by first finding several local subspaces that fit the data and then tying these subspaces together using the center of mass calculation on the Stiefel/Grassmann manifold. The method appears t... | SP:f14bb4150e95fa83a686a9a4b879cda9fc40a7bf |
Effective Subspace Indexing via Interpolation on Stiefel and Grassmann manifolds | 1 INTRODUCTION . Subspace selection algorithms have been successful in many application problems related to dimension reduction ( Zhou et al . ( 2010 ) , Bian & Tao ( 2011 ) , Si et al . ( 2010 ) , Zhang et al . ( 2009 ) ) , with applications including , e.g. , human face recognition ( Fu & Huang ( 2008 ) ) , speech an... | The authors propose an approach towards computing globally non-linear, low-dimensional embeddings of high dimensional data. In particular, they consider a sub-space indexing model with interpolation (called SIM-I) which consists of two steps. First, a locally linear model of the data is generated (using kD-trees and P... | SP:f14bb4150e95fa83a686a9a4b879cda9fc40a7bf |
Compositional Video Synthesis with Action Graphs | 1 INTRODUCTION . Learning to generate visual content is a fundamental task in computer vision , with numerous applications from sim-to-real training of autonomous agents , to creating visuals for games and movies . While the quality of generating still images has leaped forward recently ( Karras et al. , 2020 ; Brock e... | This paper proposes a generative method (AG2Vid) that generates video conditioned by the first frame, first layout and an action graph. An action graph is defined such that nodes represent objects in the scene and edges represent actions. To capture the temporal dynamics, each pairwise connection is enriched with a tim... | SP:b7192893870580c3821c5baebb643e53ed0ed2cf |
Compositional Video Synthesis with Action Graphs | 1 INTRODUCTION . Learning to generate visual content is a fundamental task in computer vision , with numerous applications from sim-to-real training of autonomous agents , to creating visuals for games and movies . While the quality of generating still images has leaped forward recently ( Karras et al. , 2020 ; Brock e... | The paper proposes a hierarchical approach to video synthesis based on Action Graph. Action Graph is a graph representation to describe the dynamics of individual objects. Based on this, the authors proposes an action scheduling mechanism to track the progress of action and then generate the scene layout at each timest... | SP:b7192893870580c3821c5baebb643e53ed0ed2cf |
Compositional Video Synthesis with Action Graphs | 1 INTRODUCTION . Learning to generate visual content is a fundamental task in computer vision , with numerous applications from sim-to-real training of autonomous agents , to creating visuals for games and movies . While the quality of generating still images has leaped forward recently ( Karras et al. , 2020 ; Brock e... | This paper proposes a model for video generation which disentangles the object layout prediction, frame-by-frame, from the actual pixelwise frame generation. A so-called Action Graph (AG) is used as specification of the video to be generated, rather than a sentence. Action graphs model objects as nodes and actions as c... | SP:b7192893870580c3821c5baebb643e53ed0ed2cf |
Concentric Spherical GNN for 3D Representation Learning | Learning 3D representations of point clouds that generalize well to arbitrary orientations is a challenge of practical importance in problems ranging from computer vision to molecular modeling . The proposed approach is based on a concentric spherical representation of 3D space , formed by nesting spatially-sampled sph... | This paper presents a novel multi-sphere icosahedral discretization for representation of 3D data. Given meshes or point cloud, the authors map them to multiple layers spheres and apply graph conv on the spheres to learn rotation-invariant features. In the final stage, all the layers are merged via a radial pool operat... | SP:797f40f8ab27a0ce589b18ec143e398b6b44a460 |
Concentric Spherical GNN for 3D Representation Learning | Learning 3D representations of point clouds that generalize well to arbitrary orientations is a challenge of practical importance in problems ranging from computer vision to molecular modeling . The proposed approach is based on a concentric spherical representation of 3D space , formed by nesting spatially-sampled sph... | In this paper, a multi-resolution convolutional architecture is proposed to learn from concentric feature maps. Different from single sphere representation, both graph convolutions and radial convolutions are employed to extract the intra-sphere and inter-sphere information. Benefit form the radial discretization, the ... | SP:797f40f8ab27a0ce589b18ec143e398b6b44a460 |
Concentric Spherical GNN for 3D Representation Learning | Learning 3D representations of point clouds that generalize well to arbitrary orientations is a challenge of practical importance in problems ranging from computer vision to molecular modeling . The proposed approach is based on a concentric spherical representation of 3D space , formed by nesting spatially-sampled sph... | This paper addresses an important problem in 3D representation learning, which is how to design a robust convolution neural network for arbitrarily oriented inputs. They propose to use multi-sphere icosahedral discretization to representation 3D data at first. And then, several alternative convolution operations are us... | SP:797f40f8ab27a0ce589b18ec143e398b6b44a460 |
UMEC: Unified model and embedding compression for efficient recommendation systems | 1 INTRODUCTION . As the core component of a recommendation system ( RS ) , recommendation models ( RM ) based on ranking neural networks are widely adopted in general content recommendation and retrieval applications . In general , an effective recommendation model consists of two components : a feature embedding sub-m... | The paper proposes a new unified optimization framework to solve the RM compression problem. It jointly compresses the prediction network and the feature embedding, and automatically optimizes their complexity budgets under the total resource constraint. In this way, it avoids treating selection of input feature and co... | SP:3a60ea9460ca9592892c6d4795d065e3b4be13de |
UMEC: Unified model and embedding compression for efficient recommendation systems | 1 INTRODUCTION . As the core component of a recommendation system ( RS ) , recommendation models ( RM ) based on ranking neural networks are widely adopted in general content recommendation and retrieval applications . In general , an effective recommendation model consists of two components : a feature embedding sub-m... | This paper proposes a framework of a unified recommendation system, which balances the compression degree of the model and the accuracy of the model by compressing the embedding layer model, while optimizing feature selection and neural network compression. This paper proposes a resource-constrained method to simultane... | SP:3a60ea9460ca9592892c6d4795d065e3b4be13de |
UMEC: Unified model and embedding compression for efficient recommendation systems | 1 INTRODUCTION . As the core component of a recommendation system ( RS ) , recommendation models ( RM ) based on ranking neural networks are widely adopted in general content recommendation and retrieval applications . In general , an effective recommendation model consists of two components : a feature embedding sub-m... | This paper studies the compression of recommendation models (RMs). That is new and relatively less studied in the model compression field, but of great practical value. The main unique challenge of RM compression lies in the entanglement of compressing both the network parameters and the feature embedding inputs, and t... | SP:3a60ea9460ca9592892c6d4795d065e3b4be13de |
A Gradient-based Kernel Approach for Efficient Network Architecture Search | 1 INTRODUCTION . Understanding and improving the optimization of deep networks has been an active field of artificial intelligence . One of the mysteries in deep learning is why extremely deep neural networks are hard to train . Currently , the vanishing and exploding gradient problem is widely believed to be the main ... | This paper pointed out that both gradient correlations and gradient values have strong impacts on model training, which is an insightful finding. Based on this finding, they explore a simple yet effective network architecture search (NAS) approach. The new approach replaces the expensive “train-then-test” evaluation pa... | SP:a099a8bec6b5bf6a292104066f32f5152ac2bc1c |
A Gradient-based Kernel Approach for Efficient Network Architecture Search | 1 INTRODUCTION . Understanding and improving the optimization of deep networks has been an active field of artificial intelligence . One of the mysteries in deep learning is why extremely deep neural networks are hard to train . Currently , the vanishing and exploding gradient problem is widely believed to be the main ... | This paper defines a new scoring function for efficiently selecting between different network architectures, without requiring the expensive training of the models. This scoring function is based on the expected dot-products between the stochastic gradient vectors corresponding to different pairs of training examples.... | SP:a099a8bec6b5bf6a292104066f32f5152ac2bc1c |
A Gradient-based Kernel Approach for Efficient Network Architecture Search | 1 INTRODUCTION . Understanding and improving the optimization of deep networks has been an active field of artificial intelligence . One of the mysteries in deep learning is why extremely deep neural networks are hard to train . Currently , the vanishing and exploding gradient problem is widely believed to be the main ... | This paper consists of two parts, where it first studies the difficulty of neural network training, then proposes an efficient criterion for neural architecture search (NAS). In the first part, the authors propose two criteria, the average gradient absolute values and the average gradient correlations (across pairs of ... | SP:a099a8bec6b5bf6a292104066f32f5152ac2bc1c |
Wasserstein-2 Generative Networks | 1 INTRODUCTION . Generative learning framework has become widespread over the last couple of years tentatively starting with the introduction of generative adversarial networks ( GANs ) by Goodfellow et al . ( 2014 ) . The framework aims to define a stochastic procedure to sample from a given complex probability distri... | This paper proposes Wasserstein-2 Generative Networks (W2GNs) which is an optimal transport framework for learning generative models. Unlike minimax problems of Wasserstein GANs, the proposed approach which is based on minimizing the 2-Wasserstein distance reduces to a single-level optimization problem. The paper numer... | SP:2600690b40d3f6b9c4e237c6a11685dc7912b466 |
Wasserstein-2 Generative Networks | 1 INTRODUCTION . Generative learning framework has become widespread over the last couple of years tentatively starting with the introduction of generative adversarial networks ( GANs ) by Goodfellow et al . ( 2014 ) . The framework aims to define a stochastic procedure to sample from a given complex probability distri... | The paper proposes a new method for learning an optimal pushforward (for the quadratic cost) from a distribution to another distribution based on samples of both distributions. The optimal transport problem is first written equivalently as a minimax problem over set of convex functions, as in Makkuva et al. 19. Then, t... | SP:2600690b40d3f6b9c4e237c6a11685dc7912b466 |
Wasserstein-2 Generative Networks | 1 INTRODUCTION . Generative learning framework has become widespread over the last couple of years tentatively starting with the introduction of generative adversarial networks ( GANs ) by Goodfellow et al . ( 2014 ) . The framework aims to define a stochastic procedure to sample from a given complex probability distri... | From my perspective, this is a much needed and love-to-see work for the line of neural generative modeling. Previous approaches were dominated by GAN based approaches which require solving a minimax optimization which has technical hurdles in practice. This paper reviews the literature of OT theory, and provides a comp... | SP:2600690b40d3f6b9c4e237c6a11685dc7912b466 |
Efficient Certified Defenses Against Patch Attacks on Image Classifiers | Adversarial patches pose a realistic threat model for physical world attacks on autonomous systems via their perception component . Autonomous systems in safety-critical domains such as automated driving should thus contain a fail-safe fallback component that combines certifiable robustness against patches with efficie... | This paper presents a provable defense method called BAGCERT against patch attacks which uses an invariant of BagNet for certification. By using the network with small receptive fields, this paper first analyzes the worst-case classification. The basic certification process is created by using a novel aggregation funct... | SP:1a201622459f5aac2d8372f9c90d53c42b91cd90 |
Efficient Certified Defenses Against Patch Attacks on Image Classifiers | Adversarial patches pose a realistic threat model for physical world attacks on autonomous systems via their perception component . Autonomous systems in safety-critical domains such as automated driving should thus contain a fail-safe fallback component that combines certifiable robustness against patches with efficie... | This paper considers a problem of the defense against adversarial patch insertion attacks for image classification. Namely, it considers rectangular adversarial patches of fixed sizes and aspect ratios inserted in arbitrary locations of input images and requires from the desired model to obtain good classification perf... | SP:1a201622459f5aac2d8372f9c90d53c42b91cd90 |
Efficient Certified Defenses Against Patch Attacks on Image Classifiers | Adversarial patches pose a realistic threat model for physical world attacks on autonomous systems via their perception component . Autonomous systems in safety-critical domains such as automated driving should thus contain a fail-safe fallback component that combines certifiable robustness against patches with efficie... | This paper deals with obtaining verified bounds on the accuracy of a model under attack restricted to patch modification (only a small, localized group of pixels can be modified). As opposed to previous methods (Chiang et al, 2020) which simply applied existing verification methods to the problem by enumerating possibl... | SP:1a201622459f5aac2d8372f9c90d53c42b91cd90 |
Neighborhood-Aware Neural Architecture Search | 1 INTRODUCTION . The process of automatic neural architecture design , also called neural architecture search ( NAS ) , is a promising technology to improve performance and efficiency for deep learning applications ( Zoph & Le , 2017 ; Zoph et al. , 2018 ; Liu et al. , 2019 ) . NAS methods typically minimize the valida... | The authors proposed neighborhood-aware neural architecture search, where during the evaluation phase during search, the neighborhood of an architecture is considered. Specifically, when an architecture $\alpha$ is picked, its neighbors $\mathcal{N}(\alpha)$ all contribute to the performance validation. This is built u... | SP:9f950c0b9c87ef9e2fbbe79e4a2c4d6a8fd1b041 |
Neighborhood-Aware Neural Architecture Search | 1 INTRODUCTION . The process of automatic neural architecture design , also called neural architecture search ( NAS ) , is a promising technology to improve performance and efficiency for deep learning applications ( Zoph & Le , 2017 ; Zoph et al. , 2018 ; Liu et al. , 2019 ) . NAS methods typically minimize the valida... | This paper proposes a neighbor-aware method in the neural architecture search (NAS). The paper states that by optimizing a neighbor of the neural network, it can search the result in a flat-minima, which is more stable than the sharp minima. The experiment results in further support that the proposed NA-RS and NA-DARTS... | SP:9f950c0b9c87ef9e2fbbe79e4a2c4d6a8fd1b041 |
Neighborhood-Aware Neural Architecture Search | 1 INTRODUCTION . The process of automatic neural architecture design , also called neural architecture search ( NAS ) , is a promising technology to improve performance and efficiency for deep learning applications ( Zoph & Le , 2017 ; Zoph et al. , 2018 ; Liu et al. , 2019 ) . NAS methods typically minimize the valida... | This paper introduces a searching framework of neural architectures search by modifying the objective function to optimize the aggregated performance over the neighborhood of an architecture. From the observation that flat-minima architecture $\alpha$ generalizes better than sharp-minima architecture (Zela et al. (ICLR... | SP:9f950c0b9c87ef9e2fbbe79e4a2c4d6a8fd1b041 |
Fully Convolutional Approach for Simulating Wave Dynamics | We investigate the performance of fully convolutional networks to predict the motion and interaction of surface waves in open and closed complex geometries . We focus on a U-Net type architecture and assess its ability to capture and extrapolate wave propagation in time as well as the reflection , interference and diff... | The authors use a U-Net architecture network to predict the motion and interaction of surface waves in an open and closed complex. The network trained on data with a simple box and right-angled corner geometries and generalizes well to other complex geometric configurations. The neural network-based method runs much fa... | SP:e434279e7be862814a73cf3116ddbb4f1b6a1283 |
Fully Convolutional Approach for Simulating Wave Dynamics | We investigate the performance of fully convolutional networks to predict the motion and interaction of surface waves in open and closed complex geometries . We focus on a U-Net type architecture and assess its ability to capture and extrapolate wave propagation in time as well as the reflection , interference and diff... | The paper applies a fully convolutional U-Net model for next step prediction of the height field for 2d wave dynamics. On the domains tested, the predictions remain accurate for 20 time-steps and the method seems to provide considerable speed-ups compared to a state-of-the-art spectral/hp element numerical solver. Some... | SP:e434279e7be862814a73cf3116ddbb4f1b6a1283 |
Fully Convolutional Approach for Simulating Wave Dynamics | We investigate the performance of fully convolutional networks to predict the motion and interaction of surface waves in open and closed complex geometries . We focus on a U-Net type architecture and assess its ability to capture and extrapolate wave propagation in time as well as the reflection , interference and diff... | In this paper, a methodology for simulating wave dynamics is presented based on convolutional neural networks. A standard analytic wave dynamics solver was used to generate a large dataset of 2D wave simulations. A deep net based on U-Net was trained to predict the next state of the wave field given the five previous s... | SP:e434279e7be862814a73cf3116ddbb4f1b6a1283 |
Image Modeling with Deep Convolutional Gaussian Mixture Models | 1 INTRODUCTION . This conceptual work is in the context of probabilistic image modeling , whose main objectives are density estimation and image generation ( sampling ) . Since images usually do not precisely follow a Gaussian mixture distribution , such a treatment is inherently approximative in nature . This implies ... | In this manuscript the authors present a variant of stacked Gaussian mixture models they propose for modeling images called Deep Convolutional Gaussian Mixture Model. This model may contain analogues of convolutional layers and nonlinearities between the stacked Gaussian mixture models. This model can then be trained u... | SP:9a657957e2dff8496f11c119ee364f0aa2cc1c21 |
Image Modeling with Deep Convolutional Gaussian Mixture Models | 1 INTRODUCTION . This conceptual work is in the context of probabilistic image modeling , whose main objectives are density estimation and image generation ( sampling ) . Since images usually do not precisely follow a Gaussian mixture distribution , such a treatment is inherently approximative in nature . This implies ... | This paper defines encoding and decoding procedures which use transformations inspired by Gaussian mixture models (GMMs). The decoding procedure further involves "sharpening" steps. A heuristic for training the parameters shared by the encoder and decoder is proposed which optimizes the likelihoods of GMMs defined on v... | SP:9a657957e2dff8496f11c119ee364f0aa2cc1c21 |
Image Modeling with Deep Convolutional Gaussian Mixture Models | 1 INTRODUCTION . This conceptual work is in the context of probabilistic image modeling , whose main objectives are density estimation and image generation ( sampling ) . Since images usually do not precisely follow a Gaussian mixture distribution , such a treatment is inherently approximative in nature . This implies ... | The paper proposes a model that combines hierarchical Gaussian Mixture Models with a convolutional architecture, supporting both estimation and sampling. The model is trained end-to-end via SGD and is composed of 3 types of layers: standard convolutional and max-pooling layers and a newly proposed GMM layer. The latter... | SP:9a657957e2dff8496f11c119ee364f0aa2cc1c21 |
World Model as a Graph: Learning Latent Landmarks for Planning | 1 INTRODUCTION . An intelligent agent should be able to solve difficult problems by breaking them down into sequences of simpler problems . Classically , planning algorithms have been the tool of choice for endowing AI agents with the ability to reason over complex long-horizon problems ( Doran & Michie , 1966 ; Hart e... | This paper approaches long horizon planning by learning a sparse graphical representation. The proposed algorithm, L3P, proceeds by learning a latent space which enforces a distance measure, where this distance is learned to mimic the number of steps between states via a goal conditioned Q-function. A clustering algori... | SP:fc6a56751f44bda7afbc532fa41167fce6ac286d |
World Model as a Graph: Learning Latent Landmarks for Planning | 1 INTRODUCTION . An intelligent agent should be able to solve difficult problems by breaking them down into sequences of simpler problems . Classically , planning algorithms have been the tool of choice for endowing AI agents with the ability to reason over complex long-horizon problems ( Doran & Michie , 1966 ; Hart e... | This paper presents a method for learning a sparse set of latent subgoal states during training. Using a goal-conditioned policy and the latent states, a simple planning algorithm that performs soft value iteration between the subgoal states is proposed to facilitate within-dataset generalization. The proposed method o... | SP:fc6a56751f44bda7afbc532fa41167fce6ac286d |
World Model as a Graph: Learning Latent Landmarks for Planning | 1 INTRODUCTION . An intelligent agent should be able to solve difficult problems by breaking them down into sequences of simpler problems . Classically , planning algorithms have been the tool of choice for endowing AI agents with the ability to reason over complex long-horizon problems ( Doran & Michie , 1966 ; Hart e... | This paper proposes an approach for automatically learning state abstraction on a RL problem, which can then be used for temporally extended planning using a search algorithm. The main contribution is introducing the concept of latent *landmarks*, a clustering of low dimensional state embeddings. Landmarks are defined ... | SP:fc6a56751f44bda7afbc532fa41167fce6ac286d |
Just How Toxic is Data Poisoning? A Benchmark for Backdoor and Data Poisoning Attacks | 1 INTRODUCTION . Data poisoning is a security threat to machine learning systems in which an attacker controls the behavior of a system by manipulating its training data . This class of threats is particularly germane to deep learning systems because they require large amounts of data to train and are therefore often t... | The authors study a number of existing data poisoning attacks, ablating different design choices of these attacks and evaluating them on a common benchmark. They find that many of these attacks are quite brittle to changes in their original experimental evaluation and fail to generalize to more realistic/challenging sc... | SP:b6258f8002510a53ee5a6ee63f8a35ac7e3b076b |
Just How Toxic is Data Poisoning? A Benchmark for Backdoor and Data Poisoning Attacks | 1 INTRODUCTION . Data poisoning is a security threat to machine learning systems in which an attacker controls the behavior of a system by manipulating its training data . This class of threats is particularly germane to deep learning systems because they require large amounts of data to train and are therefore often t... | This paper discusses many issues in the data poisoning literature. They call into question the real world applicability of data poisoning attacks and discuss the short coming in the literature. They present several issues in the comparison of such attacks. Notably they show that many attributes of a data poisoning atta... | SP:b6258f8002510a53ee5a6ee63f8a35ac7e3b076b |
Just How Toxic is Data Poisoning? A Benchmark for Backdoor and Data Poisoning Attacks | 1 INTRODUCTION . Data poisoning is a security threat to machine learning systems in which an attacker controls the behavior of a system by manipulating its training data . This class of threats is particularly germane to deep learning systems because they require large amounts of data to train and are therefore often t... | This paper studies 4 different previously proposed (clean label and targeted) poisoning attacks and compares them in a systematic way. The authors argue that the experimental setup in these papers are significantly different and comparing them with each other is hard. This incentivizes them to use a benchmark to test a... | SP:b6258f8002510a53ee5a6ee63f8a35ac7e3b076b |
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