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Pruning Compact ConvNets For Efficient Inference | 1 INTRODUCTION . Neural networks frequently suffer from the problem of over-parameterization , such that the model can be compressed by a large factor to drastically reduce memory footprint , computation as well as energy consumption while maintaining similar performance . This is especially pronounced for models for c... | The paper presents an experimental evaluation of simple pruning techniques applied to modern architectures that are designed to be inherently resource-efficient. It is shown that pruning large models in the FBNetV3 family achieves better accuracy-FLOPS trade-offs than smaller models in the FBNetV3 family. It is also sh... | SP:070b00b3bd28545b1bbdf3f6884e748756fb3101 |
Persistent Homology Captures the Generalization of Neural Networks Without A Validation Set | 1 INTRODUCTION . Generalization is what makes a machine learning model useful ; the uncertainty of its behaviour with unseen data is what makes it potentially dangerous . Thus , understanding the generalization error of a model can be considered one of the holy grails of the entire machine learning field . Machine lear... | This paper analyses the training of neural networks from a topological perspective, presenting a pipeline that can measure (pseudo) distances between the network's weights during training. Such information is then employed to study the generalisation error of a neural network. In contrast to existing methods for estim... | SP:0a1ecec6f5447992f3ad0011e1b7efa39da28442 |
Persistent Homology Captures the Generalization of Neural Networks Without A Validation Set | 1 INTRODUCTION . Generalization is what makes a machine learning model useful ; the uncertainty of its behaviour with unseen data is what makes it potentially dangerous . Thus , understanding the generalization error of a model can be considered one of the holy grails of the entire machine learning field . Machine lear... | This paper presents some empirical observations about the relationship of a persistent homology-based measure of learning dynamics and validation set error, during the training of deep neural nets. The paper opens with an introduction on persistent homology, then introduces an approach to study the structure of deep ne... | SP:0a1ecec6f5447992f3ad0011e1b7efa39da28442 |
Neural tangent kernel eigenvalues accurately predict generalization | 1 INTRODUCTION . Understanding and predicting a machine learning model ’ s generalization to unseen data is a central goal of machine learning theory . For a given class of model , one would ideally want a simple picture of a model ’ s inductive bias , identifying the set of functions on which a given model will genera... | This paper provides a novel theoretical account of generalization for kernel regression. To do so, the authors study a matrix built out of the kernel eigensystem evaluated on the training set that they call the "learning transfer matrix," and which relates the decomposition of the true function in the eigenbasis to the... | SP:0b2f9008cd16f792368bbccb68b17d8bf9cf63c5 |
Neural tangent kernel eigenvalues accurately predict generalization | 1 INTRODUCTION . Understanding and predicting a machine learning model ’ s generalization to unseen data is a central goal of machine learning theory . For a given class of model , one would ideally want a simple picture of a model ’ s inductive bias , identifying the set of functions on which a given model will genera... | The paper examines the eigenvalues of a neural network’s “Neural Tangent Kernel” to analyze its generalization performance in the infinite-width regime. It conjectures that the same results will also apply in the finite width regime as well. By analyzing kernel regression and by defining a measure as the “learnability”... | SP:0b2f9008cd16f792368bbccb68b17d8bf9cf63c5 |
MaiT: integrating spatial locality into image transformers with attention masks | 1 INTRODUCTION . Convolutional neural networks ( CNNs ) ( Krizhevsky et al. , 2012 ; He et al. , 2016 ; Tan & Le , 2019 ) have been the de facto model for computer vision ( CV ) tasks , which are inherently equipped with inductive biases such as translation equivariance and locality . Recently , vision transformers ( D... | This paper proposes to bring locality into the attention module of vision transformers. This locality mechanism is brought by the introduced attention masks. Basically, the attention masks are binary and is likely to restrict the attention to the local field of a token. The local attention mask results in a block matri... | SP:7ba7db3bba0bb539fe00165024a483e9f59d5d35 |
MaiT: integrating spatial locality into image transformers with attention masks | 1 INTRODUCTION . Convolutional neural networks ( CNNs ) ( Krizhevsky et al. , 2012 ; He et al. , 2016 ; Tan & Le , 2019 ) have been the de facto model for computer vision ( CV ) tasks , which are inherently equipped with inductive biases such as translation equivariance and locality . Recently , vision transformers ( D... | This paper attempts to improve the classic vision transformers, or specifically the DeiT model by introducing the Masked Attention Head. Instead of focusing on aggregating global information in the original self-attention heads, this paper introduce local information via the proposed Masked Attention Head into self-att... | SP:7ba7db3bba0bb539fe00165024a483e9f59d5d35 |
Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis | 1 INTRODUCTION . Multi-agent reinforcement learning ( MARL ) has achieved great success in various application domains , including control ( 66 ; 10 ; 51 ) , robotics ( 64 ) , wireless sensor networks ( 24 ; 67 ) , intelligent systems ( 71 ) , etc . In MARL , a set of fully decentralized agents interact with a dynamic ... | This paper developed two sample and communication efficient decentralized actor-critic algorithms for multi-agent reinforcement learning. Specifically, the authors proposed decentralized AC and natural AC algorithms that can be private and efficient for different agents to learn. They added noise the local rewards of a... | SP:2e125a1c23bb6dca29df2b8cc9455ce23322c994 |
Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis | 1 INTRODUCTION . Multi-agent reinforcement learning ( MARL ) has achieved great success in various application domains , including control ( 66 ; 10 ; 51 ) , robotics ( 64 ) , wireless sensor networks ( 24 ; 67 ) , intelligent systems ( 71 ) , etc . In MARL , a set of fully decentralized agents interact with a dynamic ... | In this work, the authors propose two decentralized policy gradient-type algorithms for multi-agent reinforcement learning, namely, a decentralized actor-critic algorithm and a decentralized natural actor-critic algorithm. The stochastic updates of both algorithms preserve the agents' privacy information, including the... | SP:2e125a1c23bb6dca29df2b8cc9455ce23322c994 |
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework | 1 INTRODUCTION . Lately , point cloud analysis has emerged as a popular topic in 3D understanding , attracting attention from academia and industry Qi et al . ( 2017a ) ; Shi et al . ( 2019 ) ; Xu et al . ( 2020 ) . Different from 2D images represented by regular dense pixels , point clouds are composed of unordered an... | This paper proposed an alternative point cloud feature extractor, named PointMLP. PointMLP is composed of residual MLPs and geometric affine modules. Classification results on ModelNet40 dataset show the proposed methods achieves slightly better accuracy while using much smaller number of parameters and faster runtime.... | SP:2dea3a92d8827e212ea00095f4f7e5f011538497 |
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework | 1 INTRODUCTION . Lately , point cloud analysis has emerged as a popular topic in 3D understanding , attracting attention from academia and industry Qi et al . ( 2017a ) ; Shi et al . ( 2019 ) ; Xu et al . ( 2020 ) . Different from 2D images represented by regular dense pixels , point clouds are composed of unordered an... | This paper introduces a lightweight and fast neural network architecture processing 3D point cloud. While the idea is quite simple, the proposed architecture outperforms or matches the performance of the previous architectures in terms of both accuracy and also inference time with various tasks including ModelNet40 cla... | SP:2dea3a92d8827e212ea00095f4f7e5f011538497 |
Equivariant Vector Field Network for Many-body System Modeling | 1 INTRODUCTION . Modeling many-body systems has been a long-standing challenge in scientific fields from classical and quantum physics ( Carleo & Troyer , 2017 ; Zhang et al. , 2018 ; Satorras et al. , 2021c ) to structural biology ( Senior et al. , 2020 ; Shi et al. , 2021 ) , due to its high numerical complexity and ... | The authors introduce a model to predict the time evolution of Newton mechanical systems and small molecules. The model takes a graphical representation as input and converts it to an SE(3) and permutation equivariant representation using physical principles (i.e., white-box model). This representation is passed throu... | SP:e02b51aa077852aeddc65bb2217968aca35e9105 |
Equivariant Vector Field Network for Many-body System Modeling | 1 INTRODUCTION . Modeling many-body systems has been a long-standing challenge in scientific fields from classical and quantum physics ( Carleo & Troyer , 2017 ; Zhang et al. , 2018 ; Satorras et al. , 2021c ) to structural biology ( Senior et al. , 2020 ; Shi et al. , 2021 ) , due to its high numerical complexity and ... | This work (EVFM) aims to to improve predictions of n-body system dynamics by combining continuous lie symmetries with permutation symmetry. The authors propose to do this by encoding SO(3) invariant representations of each node, followed by the use of a graph transformer and a vectorization block to estimate the vector... | SP:e02b51aa077852aeddc65bb2217968aca35e9105 |
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling | 1 INTRODUCTION . Inferring user intents through user behavior data has been extensively studied in industrial applications , such as recommendation systems , search engines , and online advertising Dupret & Piwowarski ( 2008 ) ; He et al . ( 2014 ) ; Elkahky et al . ( 2015 ) ; Yu et al . ( 2016 ) . One key aspect in th... | This paper deals with the problem of obtaining user representations from behavior sequences and proposes a method introducing the idea of a "bag of features" and multi-head attention. More specifically, a behavior sequence is expressed by a sequence of multi-dimensional vectors and a fixed-length segment of the sequenc... | SP:283b7ebcc54991836055c604e3fae24aac9286b9 |
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling | 1 INTRODUCTION . Inferring user intents through user behavior data has been extensively studied in industrial applications , such as recommendation systems , search engines , and online advertising Dupret & Piwowarski ( 2008 ) ; He et al . ( 2014 ) ; Elkahky et al . ( 2015 ) ; Yu et al . ( 2016 ) . One key aspect in th... | This paper introduces a universal user representation learning approach based on self-supervised learning on long-term user behaviors. The authors first represent user behaviors into sparse vectors, and then encode them into multiple vectors to represent users in different aspects. The authors further study using diffe... | SP:283b7ebcc54991836055c604e3fae24aac9286b9 |
Decomposing Texture and Semantics for Out-of-distribution Detection | 1 INTRODUCTION . The out-of-distribution ( OOD ) detection is the task that recognizes whether the given data comes from the distribution of training samples ( also known as in-distribution ) or not . Any machine learning-based system could receive input samples that have a completely disparate distribution from the tr... | The submission proposes to evaluate OOD detection problems with regards to two aspects — detect distributional shift in texture, or that in object-identity. A Fourier transform is used to identify changes in texture, and a modification of SVDD is used to identify changes in object identity, by building density models o... | SP:3887435d443946bde2ef1f6939bffab745ab69b4 |
Decomposing Texture and Semantics for Out-of-distribution Detection | 1 INTRODUCTION . The out-of-distribution ( OOD ) detection is the task that recognizes whether the given data comes from the distribution of training samples ( also known as in-distribution ) or not . Any machine learning-based system could receive input samples that have a completely disparate distribution from the tr... | The paper proposes an OOD setting emphasizing on texture and semantics. The authors propose an OOD detection method which disentangles texture and semantics. The method achieves SoA performances. | SP:3887435d443946bde2ef1f6939bffab745ab69b4 |
Beyond Quantization: Power aware neural networks | 1 INTRODUCTION . With the ever increasing popularity of deep neural networks ( DNNs ) for tasks like face detection , voice recognition , and image enhancement , power consumption has become one of the major considerations in the design of DNNs for resource-limited end-devices . Over the last several years , a plethora... | The authors observed that the power consumption is dominated by the bit toggling at the input of the accumulator and decreasing the bit-width of only the weights or only the activations has limited benefit to reduce the the power consumed by the multiplier. The paper proposed PANN, which uses tricks such as unsigned ar... | SP:fbefbf441554c459cac88181fbb20d7b6b440006 |
Beyond Quantization: Power aware neural networks | 1 INTRODUCTION . With the ever increasing popularity of deep neural networks ( DNNs ) for tasks like face detection , voice recognition , and image enhancement , power consumption has become one of the major considerations in the design of DNNs for resource-limited end-devices . Over the last several years , a plethora... | The paper argues that power consumption is a major obstacle in deploying DNNs to end devices and that current quantization approaches do not take power consumption directly into account and therefore are not optimal in reducing it. Using an approximate power model based on the average number of bit flips, they make two... | SP:fbefbf441554c459cac88181fbb20d7b6b440006 |
Poisoning and Backdooring Contrastive Learning | 1 INTRODUCTION . Contrastive learning ( Chopra et al. , 2005 ; Hadsell et al. , 2006 ) trains a model that projects a data distribution onto a lower-dimensional embedding space such that similar objects in the origin space are closer together in the embedding space than dissimilar objects ( Chechik et al. , 2010 ; Sohn... | This paper explores data security in multimodal contrastive learning. In particular, it designs an image-text pair generation to poison the dataset, driving the model to misclassify a particular test input or a group of images with a small patch. While the generation method is quite simple, the attack success rate is i... | SP:e4e55a9fb7e1f55fd0a6d13c315f524774b74b5f |
Poisoning and Backdooring Contrastive Learning | 1 INTRODUCTION . Contrastive learning ( Chopra et al. , 2005 ; Hadsell et al. , 2006 ) trains a model that projects a data distribution onto a lower-dimensional embedding space such that similar objects in the origin space are closer together in the embedding space than dissimilar objects ( Chechik et al. , 2010 ; Sohn... | The paper describes poisoning and backdooring attacks on CLIP, a recent method to (pre)-train multimodal networks with a contrastive objective. The setting here is different from a classical supervised BadNet-like setup in that learned embeddings are not necessarily going to be directly mapped to a class of choice. Aut... | SP:e4e55a9fb7e1f55fd0a6d13c315f524774b74b5f |
Discovering Classification Rules for Interpretable Learning with Linear Programming | 1 INTRODUCTION . Medical diagnosis , educational and juridical decisions often have important consequences for the society . Therefore , both the accuracy and interpretability of these decisions are of crucial importance . In particular , these decisions should be understandable by the decision makers . Rule sets consi... | The paper discusses how, from an existing set of rules or a procedure generating rules "on the fly", one can use linear programming to select a subset of rules that would increase "interpretability", where an interpretable system of rules is here understood as a system with few, short rules. The idea is to ensure a g... | SP:3a3e8b97dfde90fa74251f8ff90fc4010c09642b |
Discovering Classification Rules for Interpretable Learning with Linear Programming | 1 INTRODUCTION . Medical diagnosis , educational and juridical decisions often have important consequences for the society . Therefore , both the accuracy and interpretability of these decisions are of crucial importance . In particular , these decisions should be understandable by the decision makers . Rule sets consi... | The paper presents an approach to learning ensembles of classification rules using linear programming. Two variants of the algorithm are considered: one uses a collection of rules extracted from ensembles of decision trees as the starting point, and another one that learns rules from scratch by applying decision tree l... | SP:3a3e8b97dfde90fa74251f8ff90fc4010c09642b |
Revisiting Virtual Nodes in Graph Neural Networks for Link Prediction | 1 INTRODUCTION . Link prediction is an important task to complete graphs that are missing edges in various domains : citation networks ( Kipf & Welling , 2016 ) , social networks ( Adamic & Adar , 2003 ) , medical drug interaction graphs ( Abbas et al. , 2021 ) , or knowledge graphs ( KGs ) ( Ji et al. , 2021 ) . Numer... | This paper investigates using virtual nodes in graph neural networks for link prediction. Specifically, the authors use a graph clustering algorithm to determine groups of nodes in the graph and adopt multiple virtual nodes in the graph for the link prediction senario. They also theoretically investigate the effect of ... | SP:c4f85e58f75ccd367c8907900be68c1ed4b05d4c |
Revisiting Virtual Nodes in Graph Neural Networks for Link Prediction | 1 INTRODUCTION . Link prediction is an important task to complete graphs that are missing edges in various domains : citation networks ( Kipf & Welling , 2016 ) , social networks ( Adamic & Adar , 2003 ) , medical drug interaction graphs ( Abbas et al. , 2021 ) , or knowledge graphs ( KGs ) ( Ji et al. , 2021 ) . Numer... | The authors revisited the commonly used trick of virtual nodes in graph learning. The authors proposed the multiple virtual nodes usage under the link prediction scenario and provided both theoretical and empirical supports for it. For theoretical analysis, the authors consider the influence score for m-regular graph a... | SP:c4f85e58f75ccd367c8907900be68c1ed4b05d4c |
Certified Robustness for Deep Equilibrium Models via Interval Bound Propagation | 1 INTRODUCTION . A recent development in neural network design has been the introduction of implicit layers ( Amos & Kolter , 2017 ; Chen et al. , 2018 ; Agrawal et al. , 2019 ; Bai et al. , 2019 ; 2020 ; El Ghaoui et al. , 2021 ) , where the output is defined implicity as the solution to certain sets of conditions , r... | The paper presents a new class of neural networks called IBP-MonDEQs that are an extension of the recently introduced implicit networks MonDEQs. The authors identify a class of weight matrices of the network that ensures that fixed point of the implicit layers exist with respect to the interval analysis and is unique. ... | SP:868493ffb117e15730ab87159ba934b93b0bf8e4 |
Certified Robustness for Deep Equilibrium Models via Interval Bound Propagation | 1 INTRODUCTION . A recent development in neural network design has been the introduction of implicit layers ( Amos & Kolter , 2017 ; Chen et al. , 2018 ; Agrawal et al. , 2019 ; Bai et al. , 2019 ; 2020 ; El Ghaoui et al. , 2021 ) , where the output is defined implicity as the solution to certain sets of conditions , r... | The authors propose a deep equilibrium (DEQ) layer that provides certifiable robustness via the interval bound propagation technique. This involves augmenting the original fixed point condition considered in DEQs with two additional fixed point conditions, one for each bound. The main contribution is a theoretical resu... | SP:868493ffb117e15730ab87159ba934b93b0bf8e4 |
Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness | 1 INTRODUCTION . With the convolutional neural networks ( CNNs ) becoming ubiquitous , the security and robustness of neural networks is attracting increasing interests . Recent studies find that CNN models are inherently vulnerable to adversarial attacks ( Goodfellow et al. , 2014 ) , which craft imperceptible perturb... | This paper proposed a new way to generate an ensemble of networks against adversarial attacks. Different from other methods, which train different sub-models, the proposed method repeats convolution layers multiple times and controls them with random gates. The experiment demonstrates that it outperforms other ensembl... | SP:1947cb313bad9ec8dfb9e00106e098872c2d07e9 |
Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness | 1 INTRODUCTION . With the convolutional neural networks ( CNNs ) becoming ubiquitous , the security and robustness of neural networks is attracting increasing interests . Recent studies find that CNN models are inherently vulnerable to adversarial attacks ( Goodfellow et al. , 2014 ) , which craft imperceptible perturb... | This paper proposes a robust training and defending method RGN by applying control gates with binary status. During the training, the proposed method generates adversarial examples in a clean-label attack manner and mitigates the adversarial perturbation through training on another path. During the inference, RGN finds... | SP:1947cb313bad9ec8dfb9e00106e098872c2d07e9 |
Training Multi-Layer Over-Parametrized Neural Network in Subquadratic Time | 1 INTRODUCTION . Deep neural networks have been playing a central role in both practical ( such as computer vision ( LeCun et al. , 1998 ; Krizhevsky et al. , 2012 ; Szegedy et al. , 2015 ; He et al. , 2016 ) , natural language processing ( Collobert et al. , 2011 ; Devlin et al. , 2018 ) , automatic driving system , g... | 1. This paper proved that the second-order method can minimize the training loss in linea rate on multi-layer over-parameterized neural networks. This analysis relies on the connection between neural tangent kernel and over-parameterized neural networks. 2. This paper also reduced the per-iteration cost of second-order... | SP:1653be2aeb4a22e0771305d1b18024e3b88c275d |
Training Multi-Layer Over-Parametrized Neural Network in Subquadratic Time | 1 INTRODUCTION . Deep neural networks have been playing a central role in both practical ( such as computer vision ( LeCun et al. , 1998 ; Krizhevsky et al. , 2012 ; Szegedy et al. , 2015 ; He et al. , 2016 ) , natural language processing ( Collobert et al. , 2011 ; Devlin et al. , 2018 ) , automatic driving system , g... | This paper proposes a second-order algorithm for training neural networks, in the L2 regression setting. It provides a theoretical analysis of its complexity in the over-parametrized regime. It does not provide empirical validation of the method, or an implementation. | SP:1653be2aeb4a22e0771305d1b18024e3b88c275d |
Training Multi-Layer Over-Parametrized Neural Network in Subquadratic Time | 1 INTRODUCTION . Deep neural networks have been playing a central role in both practical ( such as computer vision ( LeCun et al. , 1998 ; Krizhevsky et al. , 2012 ; Szegedy et al. , 2015 ; He et al. , 2016 ) , natural language processing ( Collobert et al. , 2011 ; Devlin et al. , 2018 ) , automatic driving system , g... | This paper studies the training algorithms for multi-layer over-parameterized neural networks. In particular, this paper starts from gauss-newton-methods and incorporates tensor-based sketching techniques and preconditioning to improve the per-iteration computational complexity. As a result, the proposed algorithm can ... | SP:1653be2aeb4a22e0771305d1b18024e3b88c275d |
Meta Attention For Off-Policy Actor-Critic | 1 INTRODUCTION . Reinforcement Learning ( RL ) algorithms based on the Actor-Critic framework have achieved considerable success in many areas such as games , robot control , and planning . Compared with onpolicy methods , off-policy methods possess more efficient sampling since they do not require new samples to be co... | This paper proposes an attention-based actor-critic agent. The authors propose to parameterise the actor and the critic with two separate neural networks. Their algorithm operates in a two-stage fashion. In the first stage, the algorithm is a standard actor critic that produces an action probability distribution and a ... | SP:34b501a8bcf5fca41b166b7ed5dac94c108dcb0d |
Meta Attention For Off-Policy Actor-Critic | 1 INTRODUCTION . Reinforcement Learning ( RL ) algorithms based on the Actor-Critic framework have achieved considerable success in many areas such as games , robot control , and planning . Compared with onpolicy methods , off-policy methods possess more efficient sampling since they do not require new samples to be co... | This paper proposes to modify the off-policy actor-critic framework by introducing an attention mechanism in the actor and critic. The attention mechanism is used to adjust the actor features (i.e. intermediate features generated by the actor neural network) for better action selection in the continuous control tasks. ... | SP:34b501a8bcf5fca41b166b7ed5dac94c108dcb0d |
Meta Attention For Off-Policy Actor-Critic | 1 INTRODUCTION . Reinforcement Learning ( RL ) algorithms based on the Actor-Critic framework have achieved considerable success in many areas such as games , robot control , and planning . Compared with onpolicy methods , off-policy methods possess more efficient sampling since they do not require new samples to be co... | The paper introduces attention mechanism into actor-critics method, and formulates RL as a bi-level optimization to learn the (meta) attention parameters. The attention mechanism appears model agnostic and acts on the feature representation from the actor and critic models. Empirically, the proposed model shows improve... | SP:34b501a8bcf5fca41b166b7ed5dac94c108dcb0d |
Should I Run Offline Reinforcement Learning or Behavioral Cloning? | 1 INTRODUCTION . Offline reinforcement learning ( RL ) algorithms aim to leverage large , existing datasets of previously collected data to produce effective policies that generalize across a wide range of scenarios , without the need for costly active data collection . Many recent offline RL algorithms ( Fujimoto et a... | The paper considers a setting where we are given access to a dataset of expert or noisy-expert data collected from some MDP and need to decide whether to use either behavior cloning (BC) or offline RL. It conducts a theoretical analysis in a tabular setting showing that offline RL will recover a better policy than BC w... | SP:e0f3760b57534bf1ea5bdd3135661779d6842510 |
Should I Run Offline Reinforcement Learning or Behavioral Cloning? | 1 INTRODUCTION . Offline reinforcement learning ( RL ) algorithms aim to leverage large , existing datasets of previously collected data to produce effective policies that generalize across a wide range of scenarios , without the need for costly active data collection . Many recent offline RL algorithms ( Fujimoto et a... | Offline RL approaches are of quite great interest because of potentially easier real world applications. But as the paper points out in the extensive related work, there are a lot of conflicting results in the literature when it comes to comparison with plain old behavior cloning. To this end, the paper functions as fa... | SP:e0f3760b57534bf1ea5bdd3135661779d6842510 |
Should I Run Offline Reinforcement Learning or Behavioral Cloning? | 1 INTRODUCTION . Offline reinforcement learning ( RL ) algorithms aim to leverage large , existing datasets of previously collected data to produce effective policies that generalize across a wide range of scenarios , without the need for costly active data collection . Many recent offline RL algorithms ( Fujimoto et a... | The paper provides an attempt to help practitioners answer the question "For what type of environments and datasets should we prefer Offline RL over Behavioural Cloning". To do so, the authors extend the previous work of [1] studying this problem for contextual bandits to MDPs. These theoretical results enable them to ... | SP:e0f3760b57534bf1ea5bdd3135661779d6842510 |
Backdoor Defense via Decoupling the Training Process | 1 INTRODUCTION . Deep learning , especially deep neural networks ( DNNs ) , has been widely adopted in many realms ( Wang et al. , 2020b ; Li et al. , 2020a ; Wen et al. , 2020 ) for its high effectiveness . In general , the training of DNNs requires a large amount of training samples and computational resources . Acco... | This paper shows that self-supervised, contrastive learning can give a feature extractor that scatters training data points with backdoor triggers in the feature space. With this observation, the authors propose a novel defense method based on contrastive learning and decouple end-to-end training to defend against back... | SP:f6e3b8902793199afc205f8d6df15993eea5b992 |
Backdoor Defense via Decoupling the Training Process | 1 INTRODUCTION . Deep learning , especially deep neural networks ( DNNs ) , has been widely adopted in many realms ( Wang et al. , 2020b ; Li et al. , 2020a ; Wen et al. , 2020 ) for its high effectiveness . In general , the training of DNNs requires a large amount of training samples and computational resources . Acco... | Summary: authors propose a modification to the training procedure to prevent backdoor attacks. Instead of performing supervised training, they suggest first training the model in a self-supervised way, then in a supervised way on fully connected layers. Later they propose to remove low-credible samples and fine-tune th... | SP:f6e3b8902793199afc205f8d6df15993eea5b992 |
Backdoor Defense via Decoupling the Training Process | 1 INTRODUCTION . Deep learning , especially deep neural networks ( DNNs ) , has been widely adopted in many realms ( Wang et al. , 2020b ; Li et al. , 2020a ; Wen et al. , 2020 ) for its high effectiveness . In general , the training of DNNs requires a large amount of training samples and computational resources . Acco... | This paper proposes a decoupling-based backdoor defense (DBD) on poisoning-based backdoor attacks where an adversary can modify the dataset only. Specifically, DBD combines a self-supervised feature extractor and a supervised noise-free classifier, with an additional semi-supervised learning fine-tuning step. The core ... | SP:f6e3b8902793199afc205f8d6df15993eea5b992 |
Learning to Give Checkable Answers with Prover-Verifier Games | 1 INTRODUCTION . The astonishing performance of today ’ s dominant learning paradigm – optimizing powerful differentiable function approximators to minimize suitable loss functions – often comes at the cost of poor robustness and reliability . It is common for powerful deep learning systems to be vulnerable to adversar... | The authors propose a framework for training networks, such that justification of the answers can automatically emerge from it. Specifically, they propose a training framework where a prover's objective is to persuade the verifier network while the verifier network's objective is answering correctly based on both the o... | SP:07478ff01da03c456b44a04f251e59daa364677a |
Learning to Give Checkable Answers with Prover-Verifier Games | 1 INTRODUCTION . The astonishing performance of today ’ s dominant learning paradigm – optimizing powerful differentiable function approximators to minimize suitable loss functions – often comes at the cost of poor robustness and reliability . It is common for powerful deep learning systems to be vulnerable to adversar... | This paper proposes a new learning methodology for training neural networks based on Prover-Verifier Games (PVGs), which are inspired by interactive proof systems (IPS). PVG consists of two learners, which work in both collaborative and adversarial manners, and the hope is that two learners together could achieve more ... | SP:07478ff01da03c456b44a04f251e59daa364677a |
Learning to Give Checkable Answers with Prover-Verifier Games | 1 INTRODUCTION . The astonishing performance of today ’ s dominant learning paradigm – optimizing powerful differentiable function approximators to minimize suitable loss functions – often comes at the cost of poor robustness and reliability . It is common for powerful deep learning systems to be vulnerable to adversar... | The paper explores the idea of learning a game-theory-inspired prover-verifier system to augment neural networks with verifiable predictions. In this paper, the authors set up a differentiable prover-verifier game and establish conditions on the formulation of the game to ensure the learned verifier satisfies the sou... | SP:07478ff01da03c456b44a04f251e59daa364677a |
Explore and Control with Adversarial Surprise | 1 INTRODUCTION . Reinforcement learning methods have attained impressive results across a number of domains ( e.g. , Berner et al . ( 2019 ) ; Kober et al . ( 2013 ) ; Levine et al . ( 2016 ) ; Vinyals et al . ( 2019 ) ) . However , current RL methods typically require a large number of samples for each new task ( Dann... | This paper introduces a method, called Adversarial Surprise (AS), for unsupervised reinforcement learning. AS employs a two-player, adversarial, sequential procedure in which an Explore player tries to maximize the approximate entropy of the observations, whereas a Control player tries to minimize this same entropy. Th... | SP:5cfd3e8b1aeca40406af37d08743ce8dc2f9c8fe |
Explore and Control with Adversarial Surprise | 1 INTRODUCTION . Reinforcement learning methods have attained impressive results across a number of domains ( e.g. , Berner et al . ( 2019 ) ; Kober et al . ( 2013 ) ; Levine et al . ( 2016 ) ; Vinyals et al . ( 2019 ) ) . However , current RL methods typically require a large number of samples for each new task ( Dann... | This paper introduces Adversarial Surprise, a new approach for unsupervised reinforcement learning in stochastic BMDPs, where the goal is to explore an environment without rewards. The algorithm uses a single agent with two policies, an Explorer and a Controller, which switch during an episode with opposite rewards: to... | SP:5cfd3e8b1aeca40406af37d08743ce8dc2f9c8fe |
Explore and Control with Adversarial Surprise | 1 INTRODUCTION . Reinforcement learning methods have attained impressive results across a number of domains ( e.g. , Berner et al . ( 2019 ) ; Kober et al . ( 2013 ) ; Levine et al . ( 2016 ) ; Vinyals et al . ( 2019 ) ) . However , current RL methods typically require a large number of samples for each new task ( Dann... | This paper proposes Adversarial Surprise (AS), a method for unsupervised training of RL agents based on the competition of two policies dubbed Explore and Control. These two policies control compete to maximize/minimize surprise, respectively, by taking turns to control a shared body. Authors show that under some condi... | SP:5cfd3e8b1aeca40406af37d08743ce8dc2f9c8fe |
DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons | The joint task of bug localization and program repair is an integral part of the software development process . In this work we present DeepDebug , an approach to automated debugging using large , pretrained transformers . We begin by training a bug-creation model on reversed commit data for the purpose of generating s... | This paper proposes an approach to fixing synthetic python bugs by using the backtranslation model. The idea is to employ four different types of datasets and employ BART to learn from them. They employ backtranslation in evaluating the trained model and outperform the previous baseline of over 50%. They further evalua... | SP:bfc9fdbd6152659e6d7975261a2adf9f918be84a |
DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons | The joint task of bug localization and program repair is an integral part of the software development process . In this work we present DeepDebug , an approach to automated debugging using large , pretrained transformers . We begin by training a bug-creation model on reversed commit data for the purpose of generating s... | Authors propose couple approaches to train models for automatically debugging Python programs. Authors create bug generation model that generates training set with automatically added bugs. The bugs are used in code repositories with tests to obtain stack traces of errors. These stack traces are used to train debugging... | SP:bfc9fdbd6152659e6d7975261a2adf9f918be84a |
DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons | The joint task of bug localization and program repair is an integral part of the software development process . In this work we present DeepDebug , an approach to automated debugging using large , pretrained transformers . We begin by training a bug-creation model on reversed commit data for the purpose of generating s... | This manuscript describes DeepDebug a transformer-based mode that performs code repair on Python methods. Specifically, the model is pretrained on 200k Python code repositories and fine-tuned on a number of datasets consisting of bug fix commits and augmentation through synthetic bugs. The model takes Python methods wi... | SP:bfc9fdbd6152659e6d7975261a2adf9f918be84a |
Noise-Contrastive Variational Information Bottleneck Networks | 1 INTRODUCTION Deep neural networks ( DNNs ) have become the standard tool for challenging classification tasks , e.g . image classification or semantic segmentation , due to their excellent predictive accuracy . However , predictions of DNNs often tend to be overconfident , leading to miscalibration ( Guo et al. , 201... | This work suggests a *noise-contrastive loss* for variational information bottleneck networks to resolve the poor performance at separating correct and incorrect predictions in regularization methods. Standard regularization methods suffer *separability problems*: models indiscriminately regularize confidence to improv... | SP:8e2015afabb59d791d51809a003407a5142aeefd |
Noise-Contrastive Variational Information Bottleneck Networks | 1 INTRODUCTION Deep neural networks ( DNNs ) have become the standard tool for challenging classification tasks , e.g . image classification or semantic segmentation , due to their excellent predictive accuracy . However , predictions of DNNs often tend to be overconfident , leading to miscalibration ( Guo et al. , 201... | This paper attempts to improve uncertainty estimates for neural network-based classification. To do so, they combine various techniques: the information bottleneck, a variational distribution over neural net weights, a noise contrastive loss term, and an L2-normalisation step for the last part of encoder. They present ... | SP:8e2015afabb59d791d51809a003407a5142aeefd |
Noise-Contrastive Variational Information Bottleneck Networks | 1 INTRODUCTION Deep neural networks ( DNNs ) have become the standard tool for challenging classification tasks , e.g . image classification or semantic segmentation , due to their excellent predictive accuracy . However , predictions of DNNs often tend to be overconfident , leading to miscalibration ( Guo et al. , 201... | This paper first empirically observes that output regularization methods for reducing model overconfidence improves calibration but make correct and incorrect predictions indistinguishable based on predictive uncertainty. To tackle the issue, it proposes a noise-contrastive loss and some architectural refinements base... | SP:8e2015afabb59d791d51809a003407a5142aeefd |
Understanding approximate and unrolled dictionary learning for pattern recovery | Dictionary learning consists of finding a sparse representation from noisy data and is a common way to encode data-driven prior knowledge on signals . Alternating minimization ( AM ) is standard for the underlying optimization , where gradient descent steps alternate with sparse coding procedures . The major drawback o... | The authors theoretically study the performance of dictionary learning using "unrolling" based methods. As opposed to alternating minimization (AM) which switches back and forth between dictionary estimation and sparse recovery, the paper writes down the target dictionary as the solution to a bi-level optimization, whe... | SP:ccc0c1a5a3f474edc40fbc97237c2c15d156e4a4 |
Understanding approximate and unrolled dictionary learning for pattern recovery | Dictionary learning consists of finding a sparse representation from noisy data and is a common way to encode data-driven prior knowledge on signals . Alternating minimization ( AM ) is standard for the underlying optimization , where gradient descent steps alternate with sparse coding procedures . The major drawback o... | The paper studies dictionary learning where assumes that data can be represented as a linear combination of a few atoms of a matrix called dictionary. Traditionally, one way to approach the problem is to set up a min-min (bi-convex) optimization problem known as lasso or basis pursuit and solve it through alternating m... | SP:ccc0c1a5a3f474edc40fbc97237c2c15d156e4a4 |
Understanding approximate and unrolled dictionary learning for pattern recovery | Dictionary learning consists of finding a sparse representation from noisy data and is a common way to encode data-driven prior knowledge on signals . Alternating minimization ( AM ) is standard for the underlying optimization , where gradient descent steps alternate with sparse coding procedures . The major drawback o... | The authors investigate the asymptotic behavior of unrolling applied to dictionary learning. The applicability of unrolling to dictionary learning results from the circumstance that dictionary learning can be reformulated in terms of bilevel optimization, where the lower-level (or inner) problem is a sparse coding prob... | SP:ccc0c1a5a3f474edc40fbc97237c2c15d156e4a4 |
Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting | 1 INTRODUCTION . In the most simple case , time series forecasting deals with a scalar time-varying signal and aims to predict or forecast its values in the near future ; for example , countless applications in finance , healthcare , production automatization , etc . ( Carta et al. , 2021 ; Cao et al. , 2018 ; Sagheer ... | Recent works such as the Informer have used efficient attention mechanisms and shown significant performance improvements in the long sequence time-series forecasting problems. However, the authors argued that using only the coarsest past representations for the decoder could be a major limitation. In this paper, the a... | SP:7ce909645e709416d43fa0e795f6cf2831a0757e |
Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting | 1 INTRODUCTION . In the most simple case , time series forecasting deals with a scalar time-varying signal and aims to predict or forecast its values in the near future ; for example , countless applications in finance , healthcare , production automatization , etc . ( Carta et al. , 2021 ; Cao et al. , 2018 ; Sagheer ... | This paper presents Yformer to perform long sequence time series forecasting. The key idea is to employ skip connection to improve the prediction resolution and stabilize the encoder and decoder by reconstructing the recent past. The experiment results on two datasets showed the effectiveness of the proposed method. | SP:7ce909645e709416d43fa0e795f6cf2831a0757e |
Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting | 1 INTRODUCTION . In the most simple case , time series forecasting deals with a scalar time-varying signal and aims to predict or forecast its values in the near future ; for example , countless applications in finance , healthcare , production automatization , etc . ( Carta et al. , 2021 ; Cao et al. , 2018 ; Sagheer ... | The authors propose a new Transformer-based architecture for long-sequence temporal forecasting (LSTF) utilising ProbSparse attention mechanisms to efficiently capture long-term dependencies with L log(L) complexity. The Yformer builds on the Informer architecture with 3 key innovations: 1. Using distinct encoders to ... | SP:7ce909645e709416d43fa0e795f6cf2831a0757e |
Capturing Structural Locality in Non-parametric Language Models | Structural locality is a ubiquitous feature of real-world datasets , wherein data points are organized into local hierarchies . Some examples include topical clusters in text or project hierarchies in source code repositories . In this paper , we explore utilizing this structural locality within non-parametric language... | This work concerns itself about utilizing structural locality inherent in real-world datasets in improving the effectiveness of non-parametric language models. It makes a claim that a) structural locality is not implicitly fully captured by the distance metric used in non-parametric language models and further that b) ... | SP:6bbcd3b93df28e77efc11cae45e2c7bfe5f2b398 |
Capturing Structural Locality in Non-parametric Language Models | Structural locality is a ubiquitous feature of real-world datasets , wherein data points are organized into local hierarchies . Some examples include topical clusters in text or project hierarchies in source code repositories . In this paper , we explore utilizing this structural locality within non-parametric language... | The paper is about modelling structural locality in non-parametric language models. The key hypothesis is in modelling not only the co-occurrence characteristics but also structural characteristics such as locality. The paper explains the key claims via case studies conducted on source code data and Wikipedia datasets.... | SP:6bbcd3b93df28e77efc11cae45e2c7bfe5f2b398 |
Capturing Structural Locality in Non-parametric Language Models | Structural locality is a ubiquitous feature of real-world datasets , wherein data points are organized into local hierarchies . Some examples include topical clusters in text or project hierarchies in source code repositories . In this paper , we explore utilizing this structural locality within non-parametric language... | The authors propose an approach to complement context by adding 'locality' information in examples present in external stores of non-parametric language models. The locality information captures the hierarchical structure, and deems two contexts more similar (or having less distance) if they share common hierarchical s... | SP:6bbcd3b93df28e77efc11cae45e2c7bfe5f2b398 |
Task-Agnostic Graph Neural Explanations | 1 INTRODUCTION . Graph neural networks ( GNNs ) ( Kipf & Welling , 2017 ; Veličković et al. , 2018 ; Xu et al. , 2019 ) have achieved remarkable success in learning from real-world graph-structured data due to their unique ability to capture both feature-wise and topological information . Extending their success , GN... | The authors propose TAGE, a task-agnostic explanation method for explaining GNNs. TAGE explains GNN embedding models without downstream tasks and allows the explanation of multi-task models. This paper maximizes the mutual information of masked graph embedding and masked subgraph embedding as the objective function. Th... | SP:fb23ef05b515557e71411a840c28e1dd4d39d5cb |
Task-Agnostic Graph Neural Explanations | 1 INTRODUCTION . Graph neural networks ( GNNs ) ( Kipf & Welling , 2017 ; Veličković et al. , 2018 ; Xu et al. , 2019 ) have achieved remarkable success in learning from real-world graph-structured data due to their unique ability to capture both feature-wise and topological information . Extending their success , GN... | This paper is motivated by the fact that existing task-specific explainers are too expensive to be applied to generating explanations for a model trained for multi-tasks. They decompose the typical end-to-end learning-based GNN explainer into two parts: the embedding explainer $\mathcal{T}_{\mathcal{E}}$ and the downst... | SP:fb23ef05b515557e71411a840c28e1dd4d39d5cb |
Task-Agnostic Graph Neural Explanations | 1 INTRODUCTION . Graph neural networks ( GNNs ) ( Kipf & Welling , 2017 ; Veličković et al. , 2018 ; Xu et al. , 2019 ) have achieved remarkable success in learning from real-world graph-structured data due to their unique ability to capture both feature-wise and topological information . Extending their success , GN... | In this manuscript, a new explainer for GNNs is proposed. The newly proposed method aims to provide a task-agnostic explanation for the embedding GNNs rather than a specific downstream task. The motivation of the proposed method is that the modern GNNs are typically trained in a two-stage manner, where the embedding GN... | SP:fb23ef05b515557e71411a840c28e1dd4d39d5cb |
IGLU: Efficient GCN Training via Lazy Updates | 1 INTRODUCTION . The Graph Convolution Network ( GCN ) model has received much attention as an effective graph representation learning technique . It can exploit network topology while embedding data points enabling superior performance in several applications such as node classification on graphs ( Kipf & Welling , 20... | This paper studies tries to tackle the scalability challenge of training GNNs on large graphs. The authors propose IGLU, an architecture-agnostic method. IGLU caches intermediate computations and uses a lazy update strategy. Convergence analysis on IGLU is provided and empirical results show that IGLU has better perfor... | SP:59d57b9cb0842c860bfb06c7c0ddf7065fc384a0 |
IGLU: Efficient GCN Training via Lazy Updates | 1 INTRODUCTION . The Graph Convolution Network ( GCN ) model has received much attention as an effective graph representation learning technique . It can exploit network topology while embedding data points enabling superior performance in several applications such as node classification on graphs ( Kipf & Welling , 20... | The work proposes IGLU, an algorithm to scale-up GNNs using stale computations instead of traditional neighborhood sampling. IGLU has bounded bias if the loss and activation functions are smooth. Results on large-scale benchmarks show IGLU achieves SOTA and scales better than previous methods. | SP:59d57b9cb0842c860bfb06c7c0ddf7065fc384a0 |
IGLU: Efficient GCN Training via Lazy Updates | 1 INTRODUCTION . The Graph Convolution Network ( GCN ) model has received much attention as an effective graph representation learning technique . It can exploit network topology while embedding data points enabling superior performance in several applications such as node classification on graphs ( Kipf & Welling , 20... | This paper introduces a new method, IGLU, that caches intermediate computations at various GCN layers. This enables IGLU to perform lazy updates that do not require updating a large number of node embeddings during descent and offers much faster convergence without significantly biasing the gradients. Overall, this pa... | SP:59d57b9cb0842c860bfb06c7c0ddf7065fc384a0 |
Multi-objective Optimization by Learning Space Partition | 1 INTRODUCTION . Multi-objective optimization ( MOO ) has been extensively used in many practical scenarios involving trade-offs between multiple objectives . For example , in automobile design ( Chang , 2015 ) , we must maximize the performance of the engine while simultaneously minimizing emissions and fuel consumpti... | This work proposes a novel learning-based method called LaMOO to partition the search space for the multi-objective optimization problem. With the learned partition, the computational budget can be allocated to the small promising regions (e.g., the region close to the Pareto frontier) rather than the whole search spa... | SP:4085a28c03be5973332b8801f216918b78677483 |
Multi-objective Optimization by Learning Space Partition | 1 INTRODUCTION . Multi-objective optimization ( MOO ) has been extensively used in many practical scenarios involving trade-offs between multiple objectives . For example , in automobile design ( Chang , 2015 ) , we must maximize the performance of the engine while simultaneously minimizing emissions and fuel consumpti... | The paper develops an enhancement to multiobjective solvers so to find better Pareto solutions. The idea is to learn a proxy of the distance of samples to the Pareto frontier, and leverage such information to split the search space via a tree structure, Samples can then be extracted from promising nodes of the tree fro... | SP:4085a28c03be5973332b8801f216918b78677483 |
Multi-objective Optimization by Learning Space Partition | 1 INTRODUCTION . Multi-objective optimization ( MOO ) has been extensively used in many practical scenarios involving trade-offs between multiple objectives . For example , in automobile design ( Chang , 2015 ) , we must maximize the performance of the engine while simultaneously minimizing emissions and fuel consumpti... | This paper presents a learning space partitions-based multi-objective optimization framework by using Monte Carlo tree search and an innovatively proposed metric, i.e., dominance number. Solid theoretical analysis on single-objective optimization (SOO) and some observation on multi-objective optimization (MOO) for the ... | SP:4085a28c03be5973332b8801f216918b78677483 |
Multi-Task Neural Processes | Neural processes have recently emerged as a class of powerful neural latent variable models that combine the strengths of neural networks and stochastic processes . As they can encode contextual data in the network ’ s function space , they offer a new way to model task relatedness in multi-task learning . To study its... | This paper presents a multi-task neural processes approach in which function priors are derived in a hierarchical Bayesian inference framework to incorporate the shared knowledge into its context of the prediction function. Authors introduced a higher-level latent variable derived from the context data of related tasks... | SP:d2b56456ad62914d187bac8c78ffa995c32fa83b |
Multi-Task Neural Processes | Neural processes have recently emerged as a class of powerful neural latent variable models that combine the strengths of neural networks and stochastic processes . As they can encode contextual data in the network ’ s function space , they offer a new way to model task relatedness in multi-task learning . To study its... | The paper proposes a multi-task learning model with neural processes. The model is based on a hierarchical construction whereby each task is conditioned on global and local information. The paper derives a “hierarchical” ELBO for this model that is evaluated with MC sampling. Experiments are presented to validate the p... | SP:d2b56456ad62914d187bac8c78ffa995c32fa83b |
Multi-Task Neural Processes | Neural processes have recently emerged as a class of powerful neural latent variable models that combine the strengths of neural networks and stochastic processes . As they can encode contextual data in the network ’ s function space , they offer a new way to model task relatedness in multi-task learning . To study its... | This paper proposes a new multi-task neural process based on the classical neural process. The idea is to introduce additional global variables to share knowledge from different tasks. That is to say, a hierarchical bayesian model is constructed to link single-task neural processes together. The new model is able to ha... | SP:d2b56456ad62914d187bac8c78ffa995c32fa83b |
Learning Neural Implicit Functions as Object Representations for Robotic Manipulation | 1 INTRODUCTION . Intelligent agents should be able to interact with objects in the environment , such as grasping and placing an object , or more general tool-use , to achieve a certain goal . In robotics , such instances are formalized as manipulation planning , a type of a motion planning problem that solves not only... | The method proposes an implicit-field-function-based representation, which can be directly inferred from the camera images and be used for robot manipulation. The proposed method infers the object representation by querying about the implicit features of some pre-defined key points. The architecture projects the 3D que... | SP:f26e9ce12b57afb93c423645933e30d4b2dec3ef |
Learning Neural Implicit Functions as Object Representations for Robotic Manipulation | 1 INTRODUCTION . Intelligent agents should be able to interact with objects in the environment , such as grasping and placing an object , or more general tool-use , to achieve a certain goal . In robotics , such instances are formalized as manipulation planning , a type of a motion planning problem that solves not only... | This paper proposes a method that integrates neural implicit functions (NIFs) with planning methods for robot manipulation. A neural implicit function that represents geometry with SDFs, grasp scores, and hanging scores is learned. This is then integrated with a planner based on Logic-Geometric Planning (LGP). Experime... | SP:f26e9ce12b57afb93c423645933e30d4b2dec3ef |
Learning Neural Implicit Functions as Object Representations for Robotic Manipulation | 1 INTRODUCTION . Intelligent agents should be able to interact with objects in the environment , such as grasping and placing an object , or more general tool-use , to achieve a certain goal . In robotics , such instances are formalized as manipulation planning , a type of a motion planning problem that solves not only... | This paper studies the problem of learning representations for robotic manipulation tasks. The authors developed a method that represents objects as neural implicit functions. To method is trained in a data-driven fashion that learns the training pipeline from camera images to interaction features end-to-end. At each t... | SP:f26e9ce12b57afb93c423645933e30d4b2dec3ef |
Scalable Hierarchical Embeddings of Complex Networks | Graph representation learning has become important in order to understand and predict intrinsic structures in complex networks . A variety of embedding methods has in recent years been developed including the Latent Distance Modeling ( LDM ) approach . A major challenge is scaling network embedding approaches to very l... | The authors of this paper propose using Latent Distance Modeling (LDM) for embedding networks. LDM is an old model and simply relies on estimating the probability of an edge based on the distance of the respective embeddings of the two endpoints (+ some fixed-effect terms that capture the degree nonhomogeneity. The pro... | SP:5909c76cef387e0e626c8ea0dd165a7b32d896ac |
Scalable Hierarchical Embeddings of Complex Networks | Graph representation learning has become important in order to understand and predict intrinsic structures in complex networks . A variety of embedding methods has in recent years been developed including the Latent Distance Modeling ( LDM ) approach . A major challenge is scaling network embedding approaches to very l... | The focus of the paper is in suggesting a node embedding method that uses hierarchy to ensure scalability. The proposed method, “Scalable Hierarchical Latent Distance Model” ( SH-LDM), aims to reconcile embedding and hierarchical network representations. This method is based on the following components: - __Foundationa... | SP:5909c76cef387e0e626c8ea0dd165a7b32d896ac |
Scalable Hierarchical Embeddings of Complex Networks | Graph representation learning has become important in order to understand and predict intrinsic structures in complex networks . A variety of embedding methods has in recent years been developed including the Latent Distance Modeling ( LDM ) approach . A major challenge is scaling network embedding approaches to very l... | The paper studies the node-level representation learning problem. It proposes SH-LDM, which combines the embedding and hierarchical representations for scalable graph representation learning. The hierarchical structure in SH-LDM reduces the time and space complexity of the LDM to linearithmic in terms of the number of ... | SP:5909c76cef387e0e626c8ea0dd165a7b32d896ac |
Empirical Study of the Decision Region and Robustness in Deep Neural Networks | 1 INTRODUCTION . With the steep improvement of the performance of Deep Neural Networks ( DNNs ) , their applications are expanding to the real world , such as autonomous driving and healthcare ( LeCun et al. , 2015 ; Miotto et al. , 2018 ; Huang & Chen , 2020 ) . For real world application , it may be necessary to choo... | The paper proposes a new metric, the size of the populated region set (PRS), as an explanation for models with similar clean accuracies reaching very different accuracies under adversarial attacks. PRS is the set of decision regions that have training examples in them. After introducing and defining populated regions s... | SP:3b16daad8d1675a80905dd147cade67558c50fd2 |
Empirical Study of the Decision Region and Robustness in Deep Neural Networks | 1 INTRODUCTION . With the steep improvement of the performance of Deep Neural Networks ( DNNs ) , their applications are expanding to the real world , such as autonomous driving and healthcare ( LeCun et al. , 2015 ; Miotto et al. , 2018 ; Huang & Chen , 2020 ) . For real world application , it may be necessary to choo... | This work empirically studies for deep networks the relationship between (1) model robustness and (2) the decision surface. A novel metric is proposed, the Populated Region Set (PRS) metric, essentially the number of regions in decision space which have at least one training sample. The authors claim the metric has a "... | SP:3b16daad8d1675a80905dd147cade67558c50fd2 |
Empirical Study of the Decision Region and Robustness in Deep Neural Networks | 1 INTRODUCTION . With the steep improvement of the performance of Deep Neural Networks ( DNNs ) , their applications are expanding to the real world , such as autonomous driving and healthcare ( LeCun et al. , 2015 ; Miotto et al. , 2018 ; Huang & Chen , 2020 ) . For real world application , it may be necessary to choo... | This paper aims to understand the robustness of DNNs from the perspective of decision regions. Towards that, the authors introduce a new metric, the so-called Populated Region Set (PRS) whose ratio is later used to investigate the robustness of a selection of DNNs empirically. Based on the respective empirical evidence... | SP:3b16daad8d1675a80905dd147cade67558c50fd2 |
CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models | 1 INTRODUCTION . Grounding natural language in fine-grained image regions is essential for a broad variety of visionlanguage tasks , such as robotic navigation ( Tellex et al. , 2011 ; Anderson et al. , 2018b ) , visual question answering ( Antol et al. , 2015 ; Anderson et al. , 2018a ) , visual dialogue ( Das et al. ... | This paper proposed CPT, colorful prompt tuning for visual grounding tasks using the pre-trained V+L model. By adding color-based co-referential markers in both image and text, CPT makes visual ground as a fill-in-the-blank problem and mitigates the gap between pre-training and fine-tuning. The experiments are conducte... | SP:558d63e97493a29608da9b7700ef586c0047d592 |
CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models | 1 INTRODUCTION . Grounding natural language in fine-grained image regions is essential for a broad variety of visionlanguage tasks , such as robotic navigation ( Tellex et al. , 2011 ; Anderson et al. , 2018b ) , visual question answering ( Antol et al. , 2015 ; Anderson et al. , 2018a ) , visual dialogue ( Das et al. ... | This paper proposes a colorful prompt tuning (CPT) method for tuning pretrained vision-language models. CPT reformulates visual grounding into a fill-in-the-blank problem with color-based coreferential markers in image and text. The grounding to the target image region is achieved by recovering the corresponding color ... | SP:558d63e97493a29608da9b7700ef586c0047d592 |
CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models | 1 INTRODUCTION . Grounding natural language in fine-grained image regions is essential for a broad variety of visionlanguage tasks , such as robotic navigation ( Tellex et al. , 2011 ; Anderson et al. , 2018b ) , visual question answering ( Antol et al. , 2015 ; Anderson et al. , 2018a ) , visual dialogue ( Das et al. ... | This paper proposes a novel paradigm named Cross-Modal Prompting Tuning (CPT) that reformulates visual grounding into a fill-in-the-blank problem. Specifically, CPT applies a unique colorful mask to each visual region in the input image and then utilizes a pre-defined template to wrap the input text, where the modal ne... | SP:558d63e97493a29608da9b7700ef586c0047d592 |
AdaAug: Learning Class- and Instance-adaptive Data Augmentation Policies | 1 INTRODUCTION Data augmentation is a common way to enhance the robustness of deep learning models by augmenting the datasets used for model training . Applying popular data augmentation operations such as randomized cropping , horizontal flipping , and color shifting to image data has become a standard procedure in mo... | This paper introduces a data augmentation method AdaAug that learns adaptive augmentation policies in a class-dependent and potentially instance-dependent manner to improve the generalisation capability of deep learning models. Concretely, it proposes an efficient exploition-exploration workflow to search for an augmen... | SP:6f8a327a9f14875aad8b76d34889c9c91d04a444 |
AdaAug: Learning Class- and Instance-adaptive Data Augmentation Policies | 1 INTRODUCTION Data augmentation is a common way to enhance the robustness of deep learning models by augmenting the datasets used for model training . Applying popular data augmentation operations such as randomized cropping , horizontal flipping , and color shifting to image data has become a standard procedure in mo... | - This paper proposes AdaAug, an Automated Data Augmentation (AutoDA) method to learn a class/instance-dependent augmentation policy efficiently. The key ideas of AdaAug are two-fold. First, it uses a hidden feature of the original input to adapt the augmentation for each instance. Second, it alternates exploit-and-exp... | SP:6f8a327a9f14875aad8b76d34889c9c91d04a444 |
AdaAug: Learning Class- and Instance-adaptive Data Augmentation Policies | 1 INTRODUCTION Data augmentation is a common way to enhance the robustness of deep learning models by augmenting the datasets used for model training . Applying popular data augmentation operations such as randomized cropping , horizontal flipping , and color shifting to image data has become a standard procedure in mo... | This paper illustrates an adaptive based data augmentation method named as AdaAug that searches adaptive augmentation policies in a class-dependent and potentially instance-dependent manner to improve the generalisation capability of deep learning models. This paper proposes an efficient exploition-exploration workflow... | SP:6f8a327a9f14875aad8b76d34889c9c91d04a444 |
Unsupervised Object Learning via Common Fate | 1 INTRODUCTION . Machine learning excels if sufficient training data is available that is representative of the task at hand . In recent years , this i.i.d . data paradigm has been shown not only to apply for pattern recognition problems , but also for generative modeling ( Goodfellow et al. , 2014 ) . In practice , th... | This paper provides a multi-stage solution to unsupervised, frame-wise segmentation in videos. The common fate heuristic is used to provide initial object detections and segmentations. Then VAE based model is used to refine the initial results. A new simulated dataset is proposed. Results show that the proposed method... | SP:ad56e4793625bbd44652ebff8b82d56b9efbd612 |
Unsupervised Object Learning via Common Fate | 1 INTRODUCTION . Machine learning excels if sufficient training data is available that is representative of the task at hand . In recent years , this i.i.d . data paradigm has been shown not only to apply for pattern recognition problems , but also for generative modeling ( Goodfellow et al. , 2014 ) . In practice , th... | The paper introduces an object-centric generative model for visual scenes. The model decouples the problem into three tasks: 1) modelling the 2D appearance and shape of individual objects with a variational auto encoder; 2) same thing for background; 3) sampling the position, size and appearance of individual objects ... | SP:ad56e4793625bbd44652ebff8b82d56b9efbd612 |
Unsupervised Object Learning via Common Fate | 1 INTRODUCTION . Machine learning excels if sufficient training data is available that is representative of the task at hand . In recent years , this i.i.d . data paradigm has been shown not only to apply for pattern recognition problems , but also for generative modeling ( Goodfellow et al. , 2014 ) . In practice , th... | This work proposes an object-centric generative model. It consists of motion segmentation, object model, background model, and scene model. The object model is trained to reconstruct an object as if it is not occluded. The authors have introduced a new dataset, called Fishbowl, which provides inmodal and amodal segment... | SP:ad56e4793625bbd44652ebff8b82d56b9efbd612 |
Equivariant Transformers for Neural Network based Molecular Potentials | 1 INTRODUCTION . Quantum mechanics are essential for the computational analysis and design of molecules and materials . However , the complete solution of the Schrödinger equation is analytically and computationally not practical , which initiated the study of approximations in the past decades ( Szabo & Ostlund , 199... | The authors introduce a novel architecture for ML force fields, the Equivariant transformer (ET). It is based on the Transformer approach and can be used to predict energies (and forces) and other molecular properties (e.g., QM targets). The performance on standard benchmarks such as QM9 and MD17 is impressive. The aut... | SP:7ba40525c4aeb9f391027539b4019374231ddfdb |
Equivariant Transformers for Neural Network based Molecular Potentials | 1 INTRODUCTION . Quantum mechanics are essential for the computational analysis and design of molecules and materials . However , the complete solution of the Schrödinger equation is analytically and computationally not practical , which initiated the study of approximations in the past decades ( Szabo & Ostlund , 199... | The paper presents an equivariant transformer model for predicting quantum mechanical properties from an atomic graph. The model obtains SOTA or near-SOTA results on three popular datasets while maintaining good computational efficiency. The primary novelty in their method is a new way to compute the attention score us... | SP:7ba40525c4aeb9f391027539b4019374231ddfdb |
Equivariant Transformers for Neural Network based Molecular Potentials | 1 INTRODUCTION . Quantum mechanics are essential for the computational analysis and design of molecules and materials . However , the complete solution of the Schrödinger equation is analytically and computationally not practical , which initiated the study of approximations in the past decades ( Szabo & Ostlund , 199... | This paper proposed equivariant transformers --- a neural network based algorithm to predict properties of molecules. The architecture is built upon the traditional transformer architecture, combined with some modifications specific to molecular property prediction tasks, such as exponential normal radial basis functio... | SP:7ba40525c4aeb9f391027539b4019374231ddfdb |
Distributed Skellam Mechanism: a Novel Approach to Federated Learning with Differential Privacy | 1 INTRODUCTION . Deep neural networks , especially large-scale ones such as GPT-3 ( Brown et al. , 2020 ) , are known for their excellent memorization capabilities ( Song et al. , 2017 ; Feldman , 2020 ; Zhang et al. , 2021 ) . However , it is rather difficult to control what exactly the neural net memorizes , and unin... | ### update after the discussions I am satisfied with the responses from the authors' and the updated paper. I therefore recommend accepting the paper. --- The paper considers the problem of distributed differentially private (DP) learning using black-box secure multi-party computation (MPC) for aggregating gradients... | SP:b42d2b125c1877e8fe644c0ed5ed77ea64602430 |
Distributed Skellam Mechanism: a Novel Approach to Federated Learning with Differential Privacy | 1 INTRODUCTION . Deep neural networks , especially large-scale ones such as GPT-3 ( Brown et al. , 2020 ) , are known for their excellent memorization capabilities ( Song et al. , 2017 ; Feldman , 2020 ; Zhang et al. , 2021 ) . However , it is rather difficult to control what exactly the neural net memorizes , and unin... | This paper studies federated learning under the distributed DP framework [KLS 2021] and proposes the distributed Skellam mechanism (DSM). Compared to the existing approach [KLS 2021] that uses distributed discrete Gaussian (DDG) noise, DSM perturbs each local gradient with independent Skellam noise. This gives the adva... | SP:b42d2b125c1877e8fe644c0ed5ed77ea64602430 |
Distributed Skellam Mechanism: a Novel Approach to Federated Learning with Differential Privacy | 1 INTRODUCTION . Deep neural networks , especially large-scale ones such as GPT-3 ( Brown et al. , 2020 ) , are known for their excellent memorization capabilities ( Song et al. , 2017 ; Feldman , 2020 ; Zhang et al. , 2021 ) . However , it is rather difficult to control what exactly the neural net memorizes , and unin... | This paper presents a mechanism based on Skellam distribution (called Distributed Skellam Mechanism (DSM)) to prevent privacy leakage for federated learning. It provides analysis of privacy guarantee in the decentralized setting. Specifically, DSM is shown to be both RDP and (\epsilon, \delta)-DP. Also, DSM is applied ... | SP:b42d2b125c1877e8fe644c0ed5ed77ea64602430 |
Distribution Matching in Deep Generative Models with Kernel Transfer Operators | 1 Introduction . Generative modeling , in its unconditional form , refers to the problem of estimating the data generating distribution : given i.i.d . samples X with an unknown distribution PX , a generative model seeks to find a parametric distribution that closely resembles PX . In modern deep generative models , we... | The authors propose a new type of generative model. The new scheme is based on a kernel transfer operator that leads to a cheap method for distribution matching. The authors rely on rigorous theory on RKHS and propose a framework for transferring a prior distribution linearly (in RKHS) to the data distribution. The aut... | SP:25694c2c1a190114854a49d57a3abdd336c88b2f |
Distribution Matching in Deep Generative Models with Kernel Transfer Operators | 1 Introduction . Generative modeling , in its unconditional form , refers to the problem of estimating the data generating distribution : given i.i.d . samples X with an unknown distribution PX , a generative model seeks to find a parametric distribution that closely resembles PX . In modern deep generative models , we... | The paper presents a novel (or an unusual type of) generative modeling approach that is kernel-based and non-parametric. The basic idea is using an operator that maps from the RKHS of Z to the RKHS of X, so that data generation can be done by mapping the prior distribution $p_{prior}(z)$ to the RKHS of Z, applying the ... | SP:25694c2c1a190114854a49d57a3abdd336c88b2f |
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