paper_name stringlengths 11 170 | text stringlengths 8.07k 307k | summary stringlengths 152 6.16k | paper_id stringlengths 43 43 |
|---|---|---|---|
Does Entity Abstraction Help Generative Transformers Reason? | 1 INTRODUCTION . Transformer language models ( TLMs ; Vaswani et al . 2017 ) have enabled rapid progress in natural language processing ( NLP ) . When pre-trained on large corpora ( such as the web ) to predict the next tokens or a set of masked tokens from an input sequence , TLMs can capture linguistic knowledge ( Pe... | The paper investigates the effect of incorporating entity type abstraction into pre-trained Transformers. To achieve that, the authors have tried five different architectures to build the abstraction aware model. The proposed model is tested on three NLP datasets for reasoning. Empirical results show that entity type a... | SP:944ebd7c4c81d2266fe950501917c948f6925bb2 |
Logic Pre-Training of Language Models | 1 INTRODUCTION . Machine reasoning in natural language understanding ( NLU ) aims to teach machines to understand human languages by building and analyzing the connections between the facts , events , and observations using logical analysis techniques like deduction and induction , which is one of the ultimate goals to... | The goal of the paper is to incorporate logical relations into pre-training of language models to solve the reliance of existing reasoning-enabled language models on external knowledge bases. This is done in a self-supervised way - facts (tuple of 2 arguments and a predicate) are parsed using dependency parsing, and th... | SP:2f65e47bd1d28c29907b5061ab6d3e11f9ff1c4b |
Logic Pre-Training of Language Models | 1 INTRODUCTION . Machine reasoning in natural language understanding ( NLU ) aims to teach machines to understand human languages by building and analyzing the connections between the facts , events , and observations using logical analysis techniques like deduction and induction , which is one of the ultimate goals to... | The paper proposes a new pre-training technique to induce a logical prior in the language model representation. Concretely, they propose pre-training on facts, represented as knowledge base triples (source, sink, relation) (knowledge-base completion) and link prediction, alongside traditional masked language modeling o... | SP:2f65e47bd1d28c29907b5061ab6d3e11f9ff1c4b |
Distributionally Robust Recourse Action | Recourse actions aim to explain a particular algorithmic decision by showing one or multiple ways in which the instance could be modified to receive an alternate outcome . Existing recourse recommendations often assume that the machine learning models do not change over time . However , this assumption does not always ... | The paper provides a framework for recourse (i.e. counterfactual explanations) that is robust to shifts in the model. They formulate the robustified recourse setup as a min-max optimization problem, where the max is over a neighborhood around the distribution over model parameters. The model parameters are drawn from a... | SP:a08c91391aee4c0a73b034f1e56c2852f0babd14 |
Distributionally Robust Recourse Action | Recourse actions aim to explain a particular algorithmic decision by showing one or multiple ways in which the instance could be modified to receive an alternate outcome . Existing recourse recommendations often assume that the machine learning models do not change over time . However , this assumption does not always ... | This paper studies the problem of recourse actions (a.k.a. counterfactual explanations) while considering data distribution shifts or model shifts. The proposed Distributionally Robust Recourse Action (DiRRAc) framework has the ability to generate valid recourse actions when model parameters shift over time. DiRRAc ado... | SP:a08c91391aee4c0a73b034f1e56c2852f0babd14 |
Revisiting Layer-wise Sampling in Fast Training for Graph Convolutional Networks | To accelerate the training of graph convolutional networks ( GCNs ) , many sampling-based methods have been developed for approximating the embedding aggregation . Among them , a layer-wise approach recursively performs importance sampling to select neighbors jointly for existing nodes in each layer . This paper revisi... | This paper studies existing sampling schemes employed in training graph neural network architectures (e.g., FastGCN [Chen et al.] and LADIES [Zon et al.]) that are improvements to graph convolutional networks (GCN) [Kipf & Welling 2017] from a matrix approximation perspective (looking a Frobenius norm). The paper focu... | SP:f4db6a77d6afe5ccbefcdd0f7550dc7669843f58 |
Revisiting Layer-wise Sampling in Fast Training for Graph Convolutional Networks | To accelerate the training of graph convolutional networks ( GCNs ) , many sampling-based methods have been developed for approximating the embedding aggregation . Among them , a layer-wise approach recursively performs importance sampling to select neighbors jointly for existing nodes in each layer . This paper revisi... | This paper revisits layer-wise sampling methods of graph neural networks by addressing issues such as sub-optimal sampling probabilities and the approximation bias due to the usage of sampling without replacement. This paper also presents a new metric to evaluate the performance of the sampling strategy. Through compre... | SP:f4db6a77d6afe5ccbefcdd0f7550dc7669843f58 |
On the interventional consistency of autoencoders | 1 INTRODUCTION . Representation learning is the problem of finding a low dimensional description of the data . The characteristics of a ’ good representation ’ have long since been a matter of debate , often depending on the context in which the representation has to be employed . Multiple works ( cf . Bengio et al . (... | The paper proposes to use interventional consistency to regularise representation learning in VAEs. The idea is well-motivated by the ICM principle and theoretically justified. The paper suggests to use interventional consistency for both training and evaluation of the representation learnt by VAEs. Results show that t... | SP:5ee804c8b2609b7a2adea1b39361b0b55b49b7bd |
On the interventional consistency of autoencoders | 1 INTRODUCTION . Representation learning is the problem of finding a low dimensional description of the data . The characteristics of a ’ good representation ’ have long since been a matter of debate , often depending on the context in which the representation has to be employed . Multiple works ( cf . Bengio et al . (... | This work proposes the notion of "interventional consistency" as a beneficial property learned representations should have and introduces a regularization term to enforce it in autoencoders. Moreover, the authors introduce an "explicit latent causal block" that allows learning a structural causal model (SCM) over the l... | SP:5ee804c8b2609b7a2adea1b39361b0b55b49b7bd |
Experience Replay More When It's a Key Transition in Deep Reinforcement Learning | 1 Introduction . Deep reinforcement learning ( RL ) has shown its promising future for decision-making in various computer games , such as atari ( Mnih et al. , 2013 ; 2015 ) , go ( Schrittwieser et al. , 2020 ) and starcraft ( Vinyals et al. , 2019 ) . However , most successes have been exclusively in simulation due t... | The paper proposes to set up experience replay such that transitions are emphasized based on whether the AN2N algorithm decides to explore more, and how recent the transition was experienced. It is based on observations that the state distribution can dramatically drift over the course of policy improvement, and the in... | SP:fff4e3a93cc5acdc256c9d12c95d23d9ed458a85 |
Experience Replay More When It's a Key Transition in Deep Reinforcement Learning | 1 Introduction . Deep reinforcement learning ( RL ) has shown its promising future for decision-making in various computer games , such as atari ( Mnih et al. , 2013 ; 2015 ) , go ( Schrittwieser et al. , 2020 ) and starcraft ( Vinyals et al. , 2019 ) . However , most successes have been exclusively in simulation due t... | The paper proposes a new method (ERM) to bias the decision over which transitions should be replayed more often. In particular, using ERM, some states are deemed as key and they are replayed more often (following also a recency bias). This new method is then tested in 4 environments on the DeepMind control suite by usi... | SP:fff4e3a93cc5acdc256c9d12c95d23d9ed458a85 |
Abelian Neural Networks | 1 INTRODUCTION . The vector representations of words called word2vec ( Mikolov et al. , 2013a ; b ) trained only on large unlabeled text data are known to capture linear regularities between words . For example , vec ( “ king ” ) −vec ( “ man ” ) +vec ( “ woman ” ) results in the most similar vector to vec ( “ queen ” ... | The authors introduce Abelian group networks (AGN) that explicitly model the operational relation between elements in an Abelian group. The authors prove that the design has the ability to model such relations and present feasible neural network realizations. The authors also prove the ability of AGN to learn represent... | SP:76143383d6721e00317c6a3f0b04929f89c8b982 |
Abelian Neural Networks | 1 INTRODUCTION . The vector representations of words called word2vec ( Mikolov et al. , 2013a ; b ) trained only on large unlabeled text data are known to capture linear regularities between words . For example , vec ( “ king ” ) −vec ( “ man ” ) +vec ( “ woman ” ) results in the most similar vector to vec ( “ queen ” ... | This paper introduces a new kind of neural network for multisets of vector inputs. Whereas DeepSets uses a function h(x, y) = g(f(x) + f(y)), the proposed model uses h(x, y) = f^{-1}(f(x) + f(y)), where f is an invertible neural network. It applies this network to the problem of learning word analogies. | SP:76143383d6721e00317c6a3f0b04929f89c8b982 |
Practical Integration via Separable Bijective Networks | 1 INTRODUCTION . Most supervised learning problems operate by training a model on a finite collection , T , of N ( typically paired ) examples , ( x , y ) . The model is updated by comparing its predicted output to the expected output and performing some flavor of stochastic gradient descent based on the comparison and... | This paper introduces a hybrid model architecture that makes it possible to integrate a separable loss function across a region of input space. Such integrals can be used as regularizers for robustness near the observed data points (local consistency), and out-of-distribution (OOD) detection in neighborhoods away from ... | SP:b2da79a6f231ac2b1aae65062f5eb22bdf97cb2d |
Practical Integration via Separable Bijective Networks | 1 INTRODUCTION . Most supervised learning problems operate by training a model on a finite collection , T , of N ( typically paired ) examples , ( x , y ) . The model is updated by comparing its predicted output to the expected output and performing some flavor of stochastic gradient descent based on the comparison and... | For input-output pair $(x,y)$, the paper undertakes the task of estimating (*) $E_{x \sim p(x)} \Omega(\hat y(x))$. When $x$ is high dimensional, integration is difficult. Standard ML/DL applies MC to estimate this integral based on an iid sample $(x_i,y_i), i=1,\ldots,n$. This paper suggests there is a better way. T... | SP:b2da79a6f231ac2b1aae65062f5eb22bdf97cb2d |
Pretraining for Language Conditioned Imitation with Transformers | 1 INTRODUCTION . Whereas most contemporary reinforcement learning ( RL ) methods require programming a reward function , we are interested in training RL agents which understand language , enabling a flexible and interpretable interface and facilitating human-AI interaction . Natural language often carries tremendous a... | This paper presents a new multimodal benchmark for language conditioned RL settings, where an agent must complete tasks specified by text instructions in the Atari Frostbite environment. Their benchmark provides a dataset of 5M text-labeled transitions for training. Finally, the authors propose a model for language con... | SP:396758010e3aaccd1f0befef2cbad6c2b2d03a06 |
Pretraining for Language Conditioned Imitation with Transformers | 1 INTRODUCTION . Whereas most contemporary reinforcement learning ( RL ) methods require programming a reward function , we are interested in training RL agents which understand language , enabling a flexible and interpretable interface and facilitating human-AI interaction . Natural language often carries tremendous a... | This work proposes a new instruction-following reinforcement learning environment based on the Atari Frostbite environment. They experiment with a modified decision transformer on the environment. The results largely show that performance improves with longer context, more pretraining, and more data. | SP:396758010e3aaccd1f0befef2cbad6c2b2d03a06 |
Personalized Neural Architecture Search for Federated Learning | 1 INTRODUCTION . Federated Learning ( FL ) ( McMahan et al. , 2017 ) is a variant of distributed learning where the objective function can be decomposed into a linear combination of M local objective functions . Each function depends on its private data hosted by a local client and a set of shared parameters w , argmin... | This paper proposes a personalized neural architecture search algorithm (FEDPNAS) for FL. FEDPNAS searches for an architecture with a base component (shared across clients) and a personalized component. It also uses a context-aware operator sampler to learn a sampling distribution for feature maps. It provides a theore... | SP:e16c21331906c3dad479600864c3491d89ab67a5 |
Personalized Neural Architecture Search for Federated Learning | 1 INTRODUCTION . Federated Learning ( FL ) ( McMahan et al. , 2017 ) is a variant of distributed learning where the objective function can be decomposed into a linear combination of M local objective functions . Each function depends on its private data hosted by a local client and a set of shared parameters w , argmin... | The paper proposes a personalized neural architecture search technique for federated learning. The paper incorporates both task-personalization and context-personalization. Experimental results on both CIFAR-10 and MNIST datasets demonstrate the promise of the proposed method. | SP:e16c21331906c3dad479600864c3491d89ab67a5 |
Token Pooling in Vision Transformers | Despite the recent success in many applications , the high computational requirements of vision transformers limit their use in resource-constrained settings . While many existing methods improve the quadratic complexity of attention , in most vision transformers , self-attention is not the major computation bottleneck... | In this paper, the authors propose a novel token-pooling method to reduce redundancies in tokens for recent vision transformers. They analyze the computation cost distribution of vision transformers and the limitations of grid-based & score-based token downsampling methods. They further formulate a reconstruction loss ... | SP:4015789338b07b5dab3e4eaf542d5b0b7aaba267 |
Token Pooling in Vision Transformers | Despite the recent success in many applications , the high computational requirements of vision transformers limit their use in resource-constrained settings . While many existing methods improve the quadratic complexity of attention , in most vision transformers , self-attention is not the major computation bottleneck... | This paper proposes a new token downsampling method for vision transformer, called Token Pooling, to prune redundant tokens efficiently, so as to achieve a better flop-accuracy trade-off. Specifically, token pooling is a nonuniform data-aware downsampling method, which uses cluster algorithms to aggregate information f... | SP:4015789338b07b5dab3e4eaf542d5b0b7aaba267 |
Improving Fairness via Federated Learning | 1 INTRODUCTION . As machine learning is now used to make critical decisions that affect human life , culture , and rights , fair learning has recently received increasing attention . Various fairness notions have been introduced in the past few years ( Dwork et al. , 2012 ; Hardt et al. , 2016 ; Zafar et al. , 2017b ; ... | This paper investigates how one can achieve group fairness under a decentralized setting. The authors develop a theoretical framework for decentralized fair learning algorithms and analyzed the performance of existing approaches including UFL, FFL via FedAvg, and CFL. They provide novel insights showing that UFL<FFL vi... | SP:f234ce685370ba94d828ba354869e6038b643283 |
Improving Fairness via Federated Learning | 1 INTRODUCTION . As machine learning is now used to make critical decisions that affect human life , culture , and rights , fair learning has recently received increasing attention . Various fairness notions have been introduced in the past few years ( Dwork et al. , 2012 ; Hardt et al. , 2016 ; Zafar et al. , 2017b ; ... | The authors propose a novel algorithm to train statistical models respecting fairness criteria like client parity or demographic parity. This problem has been investigated in the literature for the centralized setting and the authors propose to extend FairBatch (FB) to the federated setting. Finally, the authors show e... | SP:f234ce685370ba94d828ba354869e6038b643283 |
L-SR1 Adaptive Regularization by Cubics for Deep Learning | 1 INTRODUCTION . Most deep learning problems involve minimization of the empirical risk of estimation min Θ f ( x ; Θ ) , ( 1 ) where Θ ∈ Rn is the set of weights and f is some scalar-valued loss function . To solve ( 1 ) , various optimization approaches have been implemented , which we describe below . Throughout thi... | The paper describes a limited memory quasi-Newton method based on SR1 updating using a variant on the adaptive regularized cubic (ARC) approach to globalization. The algorithm is applied to training deep neural networks for image classification and autoencoding, and compared to L-BFGS and various SGD variants. The au... | SP:105266467c5692d681190e94f26a01f6cf0705ee |
L-SR1 Adaptive Regularization by Cubics for Deep Learning | 1 INTRODUCTION . Most deep learning problems involve minimization of the empirical risk of estimation min Θ f ( x ; Θ ) , ( 1 ) where Θ ∈ Rn is the set of weights and f is some scalar-valued loss function . To solve ( 1 ) , various optimization approaches have been implemented , which we describe below . Throughout thi... | This paper investigates the application of a certain Quasi-Newton algorithm, the Limited-Memory Symmetric Rank-1 (L-SR1) algorithm, in deep learning problems. The benefit of this technique over similar more widely investigated methods that use a positive definite approximation of the Hessian, such as stochastic L-BFGS,... | SP:105266467c5692d681190e94f26a01f6cf0705ee |
Learning Rich Nearest Neighbor Representations from Self-supervised Ensembles | 1 INTRODUCTION . The widespread application of pretrained convolutional neural networks in computer vision is one of the most important tools in the field . State-of-the-art on many benchmarks ranging from classification , to object detection , to pose estimation has been set using a pretrained model , such as an Image... | This paper propose a new way to learn self-supervised model ensembling. Their novel approach learns representations via gradient descent directly at inference time after having pretrained feature extractors. And the authors conduct a series of experiments to show the efficacy of their method. | SP:5867aa8bb3eaaf5f916865d662be8667318a514f |
Learning Rich Nearest Neighbor Representations from Self-supervised Ensembles | 1 INTRODUCTION . The widespread application of pretrained convolutional neural networks in computer vision is one of the most important tools in the field . State-of-the-art on many benchmarks ranging from classification , to object detection , to pose estimation has been set using a pretrained model , such as an Image... | The paper proposes a new self-supervised ensembling method to get a better feature representation. Instead of using the conventional averaging or concatenating to ensemble multiple features, this paper proposes a different inference scheme where the backbone is also updated during the test time in a self supervised fas... | SP:5867aa8bb3eaaf5f916865d662be8667318a514f |
Toward Faithful Case-based Reasoning through Learning Prototypes in a Nearest Neighbor-friendly Space. | 1 INTRODUCTION . With the ever-increasing usage of deep learning in real-world situations , interpretable and explainable machine learning has become more and more important . This is because the deeply complex nature and large number of parameters in deep learning models raise concerns about reliability , fairness , t... | The paper proposes a novel combination of prototype learning and deep nearest neighbor learning in order to achieve an embedding of the input data that is more friendly for prototype learning while maintaining the computational efficiency and intepretability of a prototype approach. In more detail, the approach first t... | SP:71be5d3799276330663155b5c9c04b4a8074e800 |
Toward Faithful Case-based Reasoning through Learning Prototypes in a Nearest Neighbor-friendly Space. | 1 INTRODUCTION . With the ever-increasing usage of deep learning in real-world situations , interpretable and explainable machine learning has become more and more important . This is because the deeply complex nature and large number of parameters in deep learning models raise concerns about reliability , fairness , t... | The paper introduces a novel an interpretable neural network for classification tasks that works by assigning input cases to prototypical cases based on their euclidean distance in an embedding space learned by the network. This is achieved by minimizing a loss function consisting of three components: cross entropy los... | SP:71be5d3799276330663155b5c9c04b4a8074e800 |
Manifold Micro-Surgery with Linearly Nearly Euclidean Metrics | 1 INTRODUCTION . In general relativity ( Wald , 2010 ) , a complete Riemannian manifold ( M , g ) endowed with a linearly nearly flat spacetime metric gij is considered for linearized gravity to solve the Newtonian limit . The form of this metric is gij = ηij + γij , where ηij represents a flat Minkowski metric whose b... | The paper concerns the Ricci flow and surgery to handle singularities occuring during the Ricci flow. The authors propose linearly nearly Euclidean metrics that they prove are stable under the Ricii-DeTurck flow. The authors use this to approximate steepest descent gradient flows in information geometry and state that ... | SP:21921e3032fd96cb1ed6c602cfe914ba15097c7f |
Manifold Micro-Surgery with Linearly Nearly Euclidean Metrics | 1 INTRODUCTION . In general relativity ( Wald , 2010 ) , a complete Riemannian manifold ( M , g ) endowed with a linearly nearly flat spacetime metric gij is considered for linearized gravity to solve the Newtonian limit . The form of this metric is gij = ηij + γij , where ηij represents a flat Minkowski metric whose b... | This paper investigates linearly nearly Euclidean metrics on Riemannian manifolds under the Ricci flow. Analysis, focusing on the stability and convergence of the evolution of such metrics, is presented. In addition, the utility of the analysis for measuring the approximation of gradient flow in the context of training... | SP:21921e3032fd96cb1ed6c602cfe914ba15097c7f |
Resolving Training Biases via Influence-based Data Relabeling | 1 INTRODUCTION . Training data plays an inevitably important role in delivering the model ’ s final performance . It has been well recognized that the training bias issue will compromise model performance to a large extent ( Arpit et al. , 2017 ) . Specifically , there are two major scenarios where training biases show... | This paper proposes a data relabeling technique using influence function. The authors first derived the influence function for data relabeling as follows. $$ \eta_{\theta \delta}\left(z\_i, z\_j^{c}\right) \left. \triangleq \frac{d l\_j^c \left(\hat{\theta}\_{\epsilon\_i \delta\_j}\right)}{d \epsilon\_i}\right|_{c\_i=... | SP:508e62e7f8cd002f2ee01c2ba032ce79a1f3a469 |
Resolving Training Biases via Influence-based Data Relabeling | 1 INTRODUCTION . Training data plays an inevitably important role in delivering the model ’ s final performance . It has been well recognized that the training bias issue will compromise model performance to a large extent ( Arpit et al. , 2017 ) . Specifically , there are two major scenarios where training biases show... | The authors present an approach and framework to mitigate training biases by combining influence functions and data relabeling. The idea behind training biases is that part of the data that is used to train the model does not accurately represent the real data distribution seen in the test set. Thus, having a mismatch... | SP:508e62e7f8cd002f2ee01c2ba032ce79a1f3a469 |
Learning Causal Relationships from Conditional Moment Restrictions by Importance Weighting | 1 INTRODUCTION . Consider learning the causal relationship between airline ticket prices and demand . As one might expect , prices and demand rise and fall through the seasons , being affected by other events like vacation periods , which are called confounders and may or may not be observable . Due to confounders , na... | The present paper proposes an importance weighting approach to address the issue of regression under conditional moment restrictions in the context of non-parametric instrumental variables. The authors provide error bounds on the learned structural function. They show that their approach has a convergence rate of $\mat... | SP:0ec9a162aba8bbd0d7d0eb165271c07877ac6452 |
Learning Causal Relationships from Conditional Moment Restrictions by Importance Weighting | 1 INTRODUCTION . Consider learning the causal relationship between airline ticket prices and demand . As one might expect , prices and demand rise and fall through the seasons , being affected by other events like vacation periods , which are called confounders and may or may not be observable . Due to confounders , na... | The paper presents a method for estimating causal effects (or more generally non/parametric functions) under conditional moment restrictions, focusing in this case on nonparametric IVs. The main idea is casting these conditional restrictions to an unconditional version through importance resampling using a conditional ... | SP:0ec9a162aba8bbd0d7d0eb165271c07877ac6452 |
POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems | 1 INTRODUCTION . Neural networks have been increasingly used as the central decision makers in a variety of tasks Mnih et al . ( 2015 ) ; Lillicrap et al . ( 2016 ) ; Pan et al . ( 2018 ) . However , the use of neural-network controllers also gives rise to new challenges on verifying the correctness of the resulting cl... | This paper proposes POLAR, a reachability analysis framework for neural network-controlled systems. POLAR improves the previous Taylor model overapproximation approach in two aspects: 1) use Bernstein polynomial to overapproximates activation function; 2) use symbolic remainders to mitigate the so-called wrapping effec... | SP:abd06d2f55908d07477bc45cfd4f5bd474fb84c8 |
POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems | 1 INTRODUCTION . Neural networks have been increasingly used as the central decision makers in a variety of tasks Mnih et al . ( 2015 ) ; Lillicrap et al . ( 2016 ) ; Pan et al . ( 2018 ) . However , the use of neural-network controllers also gives rise to new challenges on verifying the correctness of the resulting cl... | The authors attack a significant problem or bounded-time reachability analysis of neural-network controlled systems (NNCSs). The method allows for verification of decision-making procedures applied by trained neural networks to systems with the known continuous model (continuous plants). The claimed main novelty of th... | SP:abd06d2f55908d07477bc45cfd4f5bd474fb84c8 |
A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) dedicates to solve the sequential decision making problem , where an agent is interacting with an unknown environment to find the best policy that maximizes the expected cumulative rewards ( Sutton & Barto , 2018 ) . It is known that the tabular algorithms direct controlli... | This paper studies the single reward episodic MDP problem with the model-based TS algorithm. Assuming the given model class satisfies realizability, regularization property, bounded Eluder dimension, low covering number, and the true dynamics is the stochastic control model under gaussian noise transition (eq 5), the a... | SP:8afa601f36fdecbcca39ca4b514c896cc73893cf |
A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) dedicates to solve the sequential decision making problem , where an agent is interacting with an unknown environment to find the best policy that maximizes the expected cumulative rewards ( Sutton & Barto , 2018 ) . It is known that the tabular algorithms direct controlli... | This paper studies the problem of learning representations for RL. On the theory side, the paper considers the setting where the state-transition is a nonlinear function of the past state-action plus additive noise, and develops a no-regret algorithm. On the empirical side, the paper shows that an adaptation of this al... | SP:8afa601f36fdecbcca39ca4b514c896cc73893cf |
MetaMorph: Learning Universal Controllers with Transformers | Multiple domains like vision , natural language , and audio are witnessing tremen-1 dous progress by leveraging Transformers for large scale pre-training followed by2 task specific fine tuning . In contrast , in robotics we primarily train a single robot3 for a single task . However , modular robot systems now allow fo... | This paper presents MetaMorph, a Transformer based universal controller to learn behaviors across different robot morphologies. Their Transformer module takes a sequence of tokens as input, corresponding to the number of modules in the robot. Each input token comprises the proprioceptive and morphology information for ... | SP:516b27b0867e288ba30da79602356fe581d59d8a |
MetaMorph: Learning Universal Controllers with Transformers | Multiple domains like vision , natural language , and audio are witnessing tremen-1 dous progress by leveraging Transformers for large scale pre-training followed by2 task specific fine tuning . In contrast , in robotics we primarily train a single robot3 for a single task . However , modular robot systems now allow fo... | This paper proposes a Transformer-based universal policy to control modular robots. Being the space of modular robots combinatorial, it is (almost) impossible to train specific policy for each sample of the "robot distribution". Therefore, the paper proposes a transformer-based architecture design with explicit informa... | SP:516b27b0867e288ba30da79602356fe581d59d8a |
Predictive Maintenance for Optical Networks in Robust Collaborative Learning | 1 INTRODUCTION . Optical fiber networks compose the core of telecommunication infrastructure today due to their high capacity of data transmission . Optical networks rely on fully functional hardware components that run under optimal conditions . In order to reduce the risk of unplanned network interruption and service... | This paper uses federated machine learning for predictive maintenance on optical networks. Federated learning provides a number of advantages, including security, privacy, and accuracy. The accuracy claims are motivated by having a broader set of failure examples to draw from, addressing a known issue in predictive mai... | SP:03927e9924258d065475e45ebeedc315e8f7e325 |
Predictive Maintenance for Optical Networks in Robust Collaborative Learning | 1 INTRODUCTION . Optical fiber networks compose the core of telecommunication infrastructure today due to their high capacity of data transmission . Optical networks rely on fully functional hardware components that run under optimal conditions . In order to reduce the risk of unplanned network interruption and service... | This paper designs a maintenance prediction framework for key optical network components based on federated learning. The designed framework can resist malicious environments and several kinds of attacks. The paper uses simulation data to verify that the proposed method has good predictive ability and can withstand the... | SP:03927e9924258d065475e45ebeedc315e8f7e325 |
Finetuned Language Models are Zero-Shot Learners | We take a 137B parameter pretrained language model and instruction tune it on over 60 NLP datasets verbalized via natural language instruction templates . We evaluate this instruction-tuned model , which we call FLAN , on unseen task types . FLAN substantially improves the performance of its unmodified counterpart and ... | The paper explores a simple and effective method to improve zero-shot performance of pretrained language models. Authors take a 137B parameter pretrained model and finetune it on multiple tasks verbalized via natural language instruction templates. As the result, the instruction-tuned model performs well on un-seen tas... | SP:60232b35685b12a1aa583e9b2ef650eafb7bfcc0 |
Finetuned Language Models are Zero-Shot Learners | We take a 137B parameter pretrained language model and instruction tune it on over 60 NLP datasets verbalized via natural language instruction templates . We evaluate this instruction-tuned model , which we call FLAN , on unseen task types . FLAN substantially improves the performance of its unmodified counterpart and ... | The paper proposes a simple method, "instruction-tuning", to improve the zero-shot learning capability of large language model, which 1) annotates prompts for a wide range of tasks and then 2) fine-tunes the model to "answer/respond to" those prompt. The empirical results are impressive: after instruction-tuning, the 0... | SP:60232b35685b12a1aa583e9b2ef650eafb7bfcc0 |
Inverse Contextual Bandits: Learning How Behavior Evolves over Time | 1 INTRODUCTION . Modeling decision-making policies is a central concern in computational and behavioral science , with key applications in healthcare [ 1 ] , economics [ 2 ] , and cognition [ 3 ] . The business of policy learning is to determine an agent ’ s decision-making policy given observations of their behavior .... | This paper studies an inverse (linear) contextual bandits (ICB) problem, where, given a $T$-round realization of a bandit policy’s actions and observed rewards, the goal is to design an algorithm to estimate the underlying environment parameter, along with the “belief trajectory” of the bandit policy. A particular emph... | SP:4304fa522634ec30bcf3862d4ae463baeed51711 |
Inverse Contextual Bandits: Learning How Behavior Evolves over Time | 1 INTRODUCTION . Modeling decision-making policies is a central concern in computational and behavioral science , with key applications in healthcare [ 1 ] , economics [ 2 ] , and cognition [ 3 ] . The business of policy learning is to determine an agent ’ s decision-making policy given observations of their behavior .... | This paper addresses the problem of Inverse Contextual Bandit. They raise an important question: given demonstrated behavior from an agent, how has the agent’s knowledge been evolving over time? Formally, Given a contextual bandit problem $(X, A, R, T )$, where $R, T$ are unknown to the agent. Given an observational ... | SP:4304fa522634ec30bcf3862d4ae463baeed51711 |
Auto-Encoding Inverse Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) provides a powerful framework for automating decision making . However , RL still requires significantly engineered reward functions for good practical performance . To make RL more applicable in the real-world , it is important to learn a reward function from expert demon... | This paper proposes to use auto-encoder for inverse reinforcement learning. The main goal of using the auto-encoder, is to use it as the reward function, which takes the auto-encoder reconstruction error to provide reward signals for the agent. The authors claim that such approach provides more informative signal than ... | SP:189665e20164d1d5e70e4185afca18b2d0f8bdb9 |
Auto-Encoding Inverse Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) provides a powerful framework for automating decision making . However , RL still requires significantly engineered reward functions for good practical performance . To make RL more applicable in the real-world , it is important to learn a reward function from expert demon... | This paper proposes the Auto-Encoding Inverse Reinforcement Learning (AEIRL) method for better imitation learning, especially in the presence of noisy expert demonstrations. The paper’s key insight is that auto-encoders can eliminate the effects of noise from expert demonstrations and provide a more stable reward signa... | SP:189665e20164d1d5e70e4185afca18b2d0f8bdb9 |
Soteria: In search of efficient neural networks for private inference | 1 INTRODUCTION . Machine learning models are susceptible to several security and privacy attacks throughout their training and inference pipelines . Defending each of these threats require different types of security mechanisms . One important requirement is that the sensitive input data as well as the trained model pa... | The paper develops a method for private neural-network-inference via utilization of Yao's garbled circuits (GC) protocol. In order to keep the computation complexity manageable - the main practical hurdle for GC - the paper proposes utilization of a neural network architecture search, coupled with restricting weights ... | SP:8542f8a0aa1e9305f5e0424b325f8c7cba563f24 |
Soteria: In search of efficient neural networks for private inference | 1 INTRODUCTION . Machine learning models are susceptible to several security and privacy attacks throughout their training and inference pipelines . Defending each of these threats require different types of security mechanisms . One important requirement is that the sensitive input data as well as the trained model pa... | The paper aims at developing an efficient neural network architecture with reduced computational cost under a cryptographic primitive where data on the client side and model on the server side are kept confidential. The motivation is that the existing works have focused on developing cryptographic techniques in a fixed... | SP:8542f8a0aa1e9305f5e0424b325f8c7cba563f24 |
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection | 1 INTRODUCTION . Reverse engineering is the process of analyzing a given binary program without its source code . It routinely requires experienced analysts and demands a huge amount of manual effort . This process is essential in many critical security problems , such as malware analysis , vulnerability discovery , an... | ==+== A. Paper summary This paper proposes a new model architecture to encode assembly code. By using a new reconstruction loss with the new architecture, the paper achieves better fine-tuning results on downstream binary code similarity detection tasks. | SP:db45d6d64491b9bf610e493af2247d9f4d578d68 |
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection | 1 INTRODUCTION . Reverse engineering is the process of analyzing a given binary program without its source code . It routinely requires experienced analysts and demands a huge amount of manual effort . This process is essential in many critical security problems , such as malware analysis , vulnerability discovery , an... | This paper proposes an enhanced transformer for dealing with the generation of assembly code. This is achieved by feeding the model with one instruction at the time (as BERT did), and hence each of these is divided into sub-words in a position-preserving way. The authors shows the efficacy of GenTAL against many other ... | SP:db45d6d64491b9bf610e493af2247d9f4d578d68 |
An Application of Pseudo-log-likelihoods to Natural Language Scoring | Language models built using semi-supervised machine learning on large corpora of natural language have very quickly enveloped the fields of natural language generation and understanding . In this paper we apply a zero-shot approach independently developed by a number of researchers now gaining recognition as a signific... | The paper makes multiple independent contributions, including: 1. The addition of a new adversarial common-sense reasoning dataset dubbed “Winogradversarial” 2. The explicit gathering together of multiple research threads all exploring the use of pretrained language models for zero-shot prediction on language-understa... | SP:64da35ba47ea28883a1828aa80926f57b037332f |
An Application of Pseudo-log-likelihoods to Natural Language Scoring | Language models built using semi-supervised machine learning on large corpora of natural language have very quickly enveloped the fields of natural language generation and understanding . In this paper we apply a zero-shot approach independently developed by a number of researchers now gaining recognition as a signific... | I believe that the main contribution is that the paper shows that albert-xxlarge-v2 is the best zero-shot model on the commonsense datasets (out of multiple models available in huggingface that are being evaluated). However, the authors also seem to argue against finetuning as a general approach for commonsense reasoni... | SP:64da35ba47ea28883a1828aa80926f57b037332f |
Contrastive Representation Learning for 3D Protein Structures | 1 INTRODUCTION . In recent years , learning on 3D protein structures has gained a lot of attention in the fields of protein modeling and structural bioinformatics . These neural network architectures process the 3D position of the atoms and/or amino acids in 3D space in order to make predictions of unprecedented perfor... | This paper studies unsupervised contrastive learning on protein structures, using sub-structure sampling as the data transformation strategy in contrastive learning. The protein structure representation from contrastive learning is then evaluated for three tasks: fold classification, enzyme classification, and protein ... | SP:5f8c45f7d133f3ec6e0280fb596a3242e2f48733 |
Contrastive Representation Learning for 3D Protein Structures | 1 INTRODUCTION . In recent years , learning on 3D protein structures has gained a lot of attention in the fields of protein modeling and structural bioinformatics . These neural network architectures process the 3D position of the atoms and/or amino acids in 3D space in order to make predictions of unprecedented perfor... | This paper presents an unsupervised deep learning approach for learning representations of 3D protein structures. They use an objective function motivated by the recent contrastive learning approaches (from computer vision). They show the utility of their model in two downstream applications: protein fold classificatio... | SP:5f8c45f7d133f3ec6e0280fb596a3242e2f48733 |
Kokoyi: Executable LaTeX for End-to-end Deep Learning | 1 INTRODUCTION . The success of deep learning is a tale of two ends . At one end is model development , which leverages the language of mathematics to define models . At the other end is model implementation , which relies on programming languages such as Python and CUDA to unleash the power of big data and efficient c... | The paper presents a LaTeX-based language and compiler called kokoyi to write math-based models and compile them to actual code (such as PyTorch). The authors present an approach to support optimizations such as auto-batching during this compilation process which significantly reduces user burden. The authors presented... | SP:e5bb31f2b7a8d8c2c605b71fc625890bb4f5a388 |
Kokoyi: Executable LaTeX for End-to-end Deep Learning | 1 INTRODUCTION . The success of deep learning is a tale of two ends . At one end is model development , which leverages the language of mathematics to define models . At the other end is model implementation , which relies on programming languages such as Python and CUDA to unleash the power of big data and efficient c... | This paper proposes Kokoyi, which can automatically translate mathematics into Python implementations. The proposed tool consists of kokoyi-lang, a programming language with the syntax of LATEX and the semantics of deep learning mathematics, and kokoyi-lang, a compiler and runtime supporting advanced optimizations. Kok... | SP:e5bb31f2b7a8d8c2c605b71fc625890bb4f5a388 |
Contrastive Embeddings for Neural Architectures | The performance of algorithms for neural architecture search strongly depends on the parametrization of the search space . We use contrastive learning to identify networks across different initializations based on their data Jacobians and their number of parameters , and automatically produce the first architecture emb... | The paper proposes a new technique to generate embeddings agnostic of the search space. This can be achieved by first computing the data jacobian of the network with respect to datapoints sampled from different neighbourhoods. This jacobian matrix is then input to a contrastive network which produces architecture emb... | SP:eef7b413ff0071fd73687ae4ccc3bc753df12742 |
Contrastive Embeddings for Neural Architectures | The performance of algorithms for neural architecture search strongly depends on the parametrization of the search space . We use contrastive learning to identify networks across different initializations based on their data Jacobians and their number of parameters , and automatically produce the first architecture emb... | The paper proposes a self-supervised embedding learning method to learn embeddings of various-sized neural network architectures. Each network is first represented as a low rank projection of a Jacobian matrix, where the rows are Jacobians (output-averaged if multivariate) evaluated at various inputs at random initiali... | SP:eef7b413ff0071fd73687ae4ccc3bc753df12742 |
Lagrangian Method for Episodic Learning | 1 INTRODUCTION . Episodic learning Terry ( 2017 ) is a general learning paradigm in which the agent learns based on data collected from a sequence of episodes of environmental interactions . Each episode consists of a finite number of decision steps , and the goal is to maximize an episode-wise performance . With one-s... | In this work, the authors consider nonlinear Q-form Lagrangian function and show corresponding strong duality property. The main contributions are 1) the new proof of showing the duality gap as zero from a minmax perspective; 2) the generality of the theory with applications to machine translation tasks. A imitation le... | SP:89a7b0e7a6b9dc575b6686ac9bad37853b82c321 |
Lagrangian Method for Episodic Learning | 1 INTRODUCTION . Episodic learning Terry ( 2017 ) is a general learning paradigm in which the agent learns based on data collected from a sequence of episodes of environmental interactions . Each episode consists of a finite number of decision steps , and the goal is to maximize an episode-wise performance . With one-s... | This paper studies the Q learning in episodic learning from a Lagrangian formulation of the Q-form Bellman optimality equation. On the theory side, this paper studies the (1) fixed point of the bellman optimality operator when the discounting factor for non-terminal states are 1; (2) strong duality of the considered ... | SP:89a7b0e7a6b9dc575b6686ac9bad37853b82c321 |
Neural Manifold Clustering and Embedding | Given a union of non-linear manifolds , non-linear subspace clustering or manifold clustering aims to cluster data points based on manifold structures and also learn to parameterize each manifold as a linear subspace in a feature space . Deep neural networks have the potential to achieve this goal under highly non-line... | Authors developed a method and training procedure for manifold clustering problems. The proposed solution inspired from information theoretic methods namely maximum rate reduction. Authors also support their claims with empirical results. | SP:d6c1148b4f3044ea3ff8a2dc07981a539fd9ee34 |
Neural Manifold Clustering and Embedding | Given a union of non-linear manifolds , non-linear subspace clustering or manifold clustering aims to cluster data points based on manifold structures and also learn to parameterize each manifold as a linear subspace in a feature space . Deep neural networks have the potential to achieve this goal under highly non-line... | This work proposed a general manifold clustering algorithm called Neural Manifold Clustering and Embedding (NMCE), which utilize Maximum Coding Rate Reduction (MCR2 ) as the objective function and data augmentation to enforce constrains. In the implementation stage, given that even the toy experiment is difficult to... | SP:d6c1148b4f3044ea3ff8a2dc07981a539fd9ee34 |
Graph Tree Neural Networks | 1 INTRODUCTION . In deep learning , various architectures have been designed such as CNN , RNN , and GNN ( LeCun et al. , 1989 ; Hopfield , 1982 ; Scarselli et al. , 2008 ; Wu et al. , 2020 ) . These networks show good performance in various fields such as image , text , and sound etc . However , most of the existing s... | The paper introduces a new neural network architecture called a "Graph Tree Neural Network". This architecture is inspired by several properties of the human brain: 1. it's multi-modal; 2. it allows for cross-input reasoning; 3. it can adapt its internal structure to the current input; 4. it can adapt the amount of com... | SP:bfc029f8ae6271d156f507cf3886bd4079a91d55 |
Graph Tree Neural Networks | 1 INTRODUCTION . In deep learning , various architectures have been designed such as CNN , RNN , and GNN ( LeCun et al. , 1989 ; Hopfield , 1982 ; Scarselli et al. , 2008 ; Wu et al. , 2020 ) . These networks show good performance in various fields such as image , text , and sound etc . However , most of the existing s... | In this paper, the authors propose Graph Tree Neural Network (GTNN) a new learning model that is structured as a graph tree (i.e., a tree with links between siblings) where each node has to process the data coming from its children. GTNN can take as input data of various types where each node will be processing the dat... | SP:bfc029f8ae6271d156f507cf3886bd4079a91d55 |
Object Pursuit: Building a Space of Objects via Discriminative Weight Generation | 1 INTRODUCTION . What are human infants and toddlers learning while they are manipulating a discovered object ? And , how do such continual interaction and learning experiences , i.e. , objects are discovered and learned one by one , help develop the capability to understand the scenes that consist of individual object... | This paper presents a new framework to learn object-centric representations. The model is composed of a segmentation network and a hypernetwork. The hypernetwork takes the latent representation of a certain object as input and predicts the weights for the segmentation network. The latent representation and the hypernet... | SP:9aea99f6d15886147e932ee5de55dc24a00cbb67 |
Object Pursuit: Building a Space of Objects via Discriminative Weight Generation | 1 INTRODUCTION . What are human infants and toddlers learning while they are manipulating a discovered object ? And , how do such continual interaction and learning experiences , i.e. , objects are discovered and learned one by one , help develop the capability to understand the scenes that consist of individual object... | This paper proposed a framework to continuously learn object-centric representations and formulate the problem by projecting the object representation space to the hypernetwork parameters for the segmentation task. The data are sampled from marginals where only one instance mask is collected in each scene. The represen... | SP:9aea99f6d15886147e932ee5de55dc24a00cbb67 |
Connecting Graph Convolution and Graph PCA | 1 INTRODUCTION . Graph neural networks ( GNNs ) are neural networks designed for the graph domain . Since the breakthrough of GCN ( Kipf & Welling , 2017 ) , which notably improved performance on the semisupervised node classification problem , many GNN variants have been proposed ; including GAT ( Veličković et al. ... | This paper relates GCN to PCA from the perspective of optimization. The authors propose Graph PCA that is a general form of GCN. They further introduce a regularization term that enforces nodes with same labels close to each other. | SP:b31551ab379915f28477cb1f49699cb811a91d29 |
Connecting Graph Convolution and Graph PCA | 1 INTRODUCTION . Graph neural networks ( GNNs ) are neural networks designed for the graph domain . Since the breakthrough of GCN ( Kipf & Welling , 2017 ) , which notably improved performance on the semisupervised node classification problem , many GNN variants have been proposed ; including GAT ( Veličković et al. ... | This manuscript looks at the classic graph-regularized PCA (GPCA) and try to build the connection with GPCA and the state-of-the-art GCN, and finally proposes a new deep graph network GPCANet. The authors make a number of clear contributions as listed in the paper: 1) they build the connection between GPCA and GCN, 2)... | SP:b31551ab379915f28477cb1f49699cb811a91d29 |
Maximizing Ensemble Diversity in Deep Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) agents trained with high capacity function approximators such a deep neural networks have shown to solve complex sequential decision-making problems , including the board games of Chess , GO and Shogi ( Silver et al. , 2016 ; 2017 ; 2018 ) , achieving super-human performan... | MED-RL This paper studies foster diversity in ensemble of DRL networks by regularization methods. The paper is an empirical one and compared five ensemble methods with and without their diversity algorithm in six Mujoco and six Atari games, and showed some results. The algorithm proposed is a modification of MaxMinDQN... | SP:8268301968d5bb106483c7603117df2335d64610 |
Maximizing Ensemble Diversity in Deep Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) agents trained with high capacity function approximators such a deep neural networks have shown to solve complex sequential decision-making problems , including the board games of Chess , GO and Shogi ( Silver et al. , 2016 ; 2017 ; 2018 ) , achieving super-human performan... | The paper considers a problem of ensemble-based deep RL methods that ensembles of critic networks converge to the same point in the representation space. To address it, the paper proposes a regularization technique that forces representations of a critic network to be dissimilar from those of other critic networks in t... | SP:8268301968d5bb106483c7603117df2335d64610 |
First-Order Optimization Inspired from Finite-Time Convergent Flows | 1 INTRODUCTION . Consider the unconstrained minimization problem for a given cost function f : Rn → R. When f is sufficiently regular , the standard algorithm in continuous time ( dynamical system ) is given by ẋ = FGF ( x ) , −∇f ( x ) ( 1 ) with ẋ , ddtx ( t ) , known as the gradient flow ( GF ) . Generalizing GF ,... | This paper studies the convergence rates of the two first-order methods, named $q$-RGF and $q$-SGF. These are constructed by forward Euler discretizing the $q$-rescaled gradient flow ($q$-RGF) [Wibisono et al., 2016] and $q$-signed GF ($q$-SGF) [Romero-Benosman, 2020], respectively. These gradient flows are shown to co... | SP:62d9ff0ab002c3a5d47b3cfcf2a645336d3aad2d |
First-Order Optimization Inspired from Finite-Time Convergent Flows | 1 INTRODUCTION . Consider the unconstrained minimization problem for a given cost function f : Rn → R. When f is sufficiently regular , the standard algorithm in continuous time ( dynamical system ) is given by ẋ = FGF ( x ) , −∇f ( x ) ( 1 ) with ẋ , ddtx ( t ) , known as the gradient flow ( GF ) . Generalizing GF ,... | This paper considers the analysis of two discrete time schemes derived from gradient flows named q-RGF and q-SGF. The topic fits more generally into continuous time perspectives for optimization and the relations between ODE theory and optimization. This is an interesting direction with many interesting promises. | SP:62d9ff0ab002c3a5d47b3cfcf2a645336d3aad2d |
Learning a subspace of policies for online adaptation in Reinforcement Learning | 1 INTRODUCTION . In recent years , Deep Reinforcement Learning ( RL ) has succeeded at solving complex tasks , from defeating humans in board games ( Silver et al. , 2017 ) to complex control problems ( Peng et al. , 2017 ; Schulman et al. , 2017 ) . It relies on different learning algorithms ( e.g. , A2C - ( Mnih et a... | The paper studies how to discover a subset of policies that will become useful for fast adaptation of future tasks. The subspace is defined as the convex hull of some anchor policies which are discovered, and are encouraged to perform well with respect to the extrinsic reward while being as different as possible from e... | SP:de2ca9e0d296137c5ddf68db89b5d0e6b1342031 |
Learning a subspace of policies for online adaptation in Reinforcement Learning | 1 INTRODUCTION . In recent years , Deep Reinforcement Learning ( RL ) has succeeded at solving complex tasks , from defeating humans in board games ( Silver et al. , 2017 ) to complex control problems ( Peng et al. , 2017 ; Schulman et al. , 2017 ) . It relies on different learning algorithms ( e.g. , A2C - ( Mnih et a... | The authors propose a method for rapid adaptation from a single training task to an unseen test-task in reinforcement learning. The method optimizes to find a convex subspace, specifically a line, of parameters that minimize the objective in expectation over a uniform distribution. For adaptation, the authors propose t... | SP:de2ca9e0d296137c5ddf68db89b5d0e6b1342031 |
AutoOED: Automated Optimal Experimental Design Platform with Data- and Time-Efficient Multi-Objective Optimization | 1 INTRODUCTION . Optimal Experimental Design ( OED ) problems in science and engineering often require satisfying several conflicting objectives simultaneously . These problems aim to solve a multi-objective optimization system and discover a set of optimal solutions , called Pareto optimal . Furthermore , the objectiv... | The paper presents a package for black-box optimization and is specifically designed for the optimization of experimental designs. To this end, the authors build upon multi-objective Bayesian Optimization which allows to obtain good points within a few function evaluations and also enables to obtain a Pareto-Front of n... | SP:3f482ef803d5de09018a4b1f8e120320ef1622d0 |
AutoOED: Automated Optimal Experimental Design Platform with Data- and Time-Efficient Multi-Objective Optimization | 1 INTRODUCTION . Optimal Experimental Design ( OED ) problems in science and engineering often require satisfying several conflicting objectives simultaneously . These problems aim to solve a multi-objective optimization system and discover a set of optimal solutions , called Pareto optimal . Furthermore , the objectiv... | In this paper, the authors present AutoOED, an open-source platform for efficiently optimizing multiobjective problems (MO) with a restricted budget of experiments. The platform automatically guides the design of experiments to be evaluated. AutoOED is built upon multi-objective Bayesian optimization (MOBO). To accele... | SP:3f482ef803d5de09018a4b1f8e120320ef1622d0 |
Policy improvement by planning with Gumbel | 1 INTRODUCTION . In 2018 , AlphaZero ( Silver et al. , 2018 ) demonstrated a single algorithm achieving state-of-the-art results on Go , chess , and Shogi . The community reacted quickly . Leela Chess Zero ( Linscott et al. , 2018 ) was created to reproduce AlphaZero results on chess , winning Top Chess Engine Champion... | The paper proposes a number of principled algorithmic modifications to state-of-the-art planning algorithms (AlphaZero, MuZero) for improving performance in settings with many actions and a relatively small computation and / or sample budget. The main contributions are algorithmic and empirical. The key ideas include t... | SP:10eb3473230595eec1d5056bdc904d1852f791a0 |
Policy improvement by planning with Gumbel | 1 INTRODUCTION . In 2018 , AlphaZero ( Silver et al. , 2018 ) demonstrated a single algorithm achieving state-of-the-art results on Go , chess , and Shogi . The community reacted quickly . Leela Chess Zero ( Linscott et al. , 2018 ) was created to reproduce AlphaZero results on chess , winning Top Chess Engine Champion... | This paper considers MCTS with learned search guidance, as in AlphaZero, MuZero, etc. The work proposes several adjustments to the prior works, particularly regarding the way in which actions are selected at the root and non-root nodes, at training and during evaluation; and also the way in which the policy is updated ... | SP:10eb3473230595eec1d5056bdc904d1852f791a0 |
FoveaTer: Foveated Transformer for Image Classification | 1 INTRODUCTION . Many mammals , including humans , have evolved a locus ( the fovea ) in the visual sensory array with increased spatial fidelity and use head and eye movements ( Land , 2012 ; Marshall et al. , 2014 ) to orient such locus to regions and objects of interest . The system design allows visual-sensing orga... | This paper proposes a called Foveater which uses a foveated module to extract the information from the feature map with different levels of details and different locations. This proposed method has the architecture that makes sense for the image classification task. However, I found it is difficult to find significant ... | SP:4913d3bf3911917a1fd6752a3321beffc2804c7c |
FoveaTer: Foveated Transformer for Image Classification | 1 INTRODUCTION . Many mammals , including humans , have evolved a locus ( the fovea ) in the visual sensory array with increased spatial fidelity and use head and eye movements ( Land , 2012 ; Marshall et al. , 2014 ) to orient such locus to regions and objects of interest . The system design allows visual-sensing orga... | The paper shows a method of Foveated Transformer motivated by a human foveal vision where the spatial resolution varies depending on the focused point. Namely, the method mainly consists of the spatial attention model that controls the gaze point and spatial-varying convolutional filter (dense pooling in center and spa... | SP:4913d3bf3911917a1fd6752a3321beffc2804c7c |
CareGraph: A Graph-based Recommender System for Diabetes Self-Care | 1 INTRODUCTION . The recent global pandemic has brought with it a permanent shift away from traditional in-person health consultations , towards large digital telehealth platforms that support remote consults . This research is performed in the context of one such platform that provides a mobile application to help peo... | This paper introduces a knowledge-graph enhanced recommendation method for healthcare platforms. The authors use user profile information to select entity neighbors for user modeling. Experiments on a nudge CTR prediction dataset show some improvements brought by the proposed method. | SP:140a3d3a8f884cfb4ef8e6542812947017a0888c |
CareGraph: A Graph-based Recommender System for Diabetes Self-Care | 1 INTRODUCTION . The recent global pandemic has brought with it a permanent shift away from traditional in-person health consultations , towards large digital telehealth platforms that support remote consults . This research is performed in the context of one such platform that provides a mobile application to help peo... | This paper proposes a graph-based recommender system for diabetes self-care management. The proposed method bases on the knowledge graph embeddings techniques. The proposed method shows better performance compared with two baselines on metric AUC. | SP:140a3d3a8f884cfb4ef8e6542812947017a0888c |
How to deal with missing data in supervised deep learning? | 1 INTRODUCTION Missing data affect data analysis across a wide range of domains and the sources of missing values span an equally wide range . Recently , deep latent variable models ( DLVMs , Kingma & Welling , 2014 ; Rezende et al. , 2014 ) have been applied to missing data problems in an unsupervised setting ( e.g . ... | The paper handles the issue of missing values in supervised deep learning settings. The fig.1 describes their method aptly. Their method (supMIWAE) is a combination of a VAE with a neural network classifier. Given the task to predict p(Y|x_{obs}, x_{miss}), the authors view it as a joint model of covariates and outcome... | SP:cd8eaee441e33312233c6d6d41142be9e6b59b9d |
How to deal with missing data in supervised deep learning? | 1 INTRODUCTION Missing data affect data analysis across a wide range of domains and the sources of missing values span an equally wide range . Recently , deep latent variable models ( DLVMs , Kingma & Welling , 2014 ; Rezende et al. , 2014 ) have been applied to missing data problems in an unsupervised setting ( e.g . ... | This paper approaches the problem of supervised learning with missing data. The authors propose a probabilistic approach by jointly modeling the observed data, missing data and outcomes. The main contribution of this model is that they rely on deep generative models which seems to be an improvement from previous method... | SP:cd8eaee441e33312233c6d6d41142be9e6b59b9d |
Graph Barlow Twins: A self-supervised representation learning framework for graphs | 1 INTRODUCTION . Graph representation learning has been intensively studied for the last few years , having proposed various architectures and layers , like GCN ( Kipf & Welling , 2017 ) , GAT ( Veličković et al. , 2018 ) , GraphSAGE ( Hamilton et al. , 2017 ) etc . A substantial part of these methods was introduced ... | The paper applies the recently proposed self-supervised learning method Barlow-Twins to graph structured data. For constructing the augmented version of a graph, previous methods such as edge-dropping or feature masking are used. The paper conducts experimental evaluation on datasets of various scales on both transduct... | SP:54c599a6476212857ac5d5871c361e31a78b7100 |
Graph Barlow Twins: A self-supervised representation learning framework for graphs | 1 INTRODUCTION . Graph representation learning has been intensively studied for the last few years , having proposed various architectures and layers , like GCN ( Kipf & Welling , 2017 ) , GAT ( Veličković et al. , 2018 ) , GraphSAGE ( Hamilton et al. , 2017 ) etc . A substantial part of these methods was introduced ... | This paper proposed a self-supervised learning framework for graph representation learning based on a cross-correlation-based loss function. In the proposed framework, two views of the input graph obtained by augmentation methods are passed through the same encoder to compute two embedding matrices, then Barlow Twins l... | SP:54c599a6476212857ac5d5871c361e31a78b7100 |
Logarithmic landscape and power-law escape rate of SGD | Stochastic gradient descent ( SGD ) undergoes complicated multiplicative noise for the mean-square loss . We use this property of the SGD noise to derive a stochastic differential equation ( SDE ) with simpler additive noise by performing a random time change . In the SDE , the loss gradient is replaced by the logarith... | This paper considers the rate at which the SGD iterations will escape the valley around a local minimum. Under some approximation assumptions, this paper shows that the SGD noise covariance is highly structured as it aligns with the Hessian at the local minimum in the immediate vicinity. By considering the Ito SDE with... | SP:c08b370b56eba4fb58a1ea1cba0f45c9cd3143e7 |
Logarithmic landscape and power-law escape rate of SGD | Stochastic gradient descent ( SGD ) undergoes complicated multiplicative noise for the mean-square loss . We use this property of the SGD noise to derive a stochastic differential equation ( SDE ) with simpler additive noise by performing a random time change . In the SDE , the loss gradient is replaced by the logarith... | This paper studies the behavior of SGD around the minimum. Unlike many other works that simply treat SGD noise as a fixed noise, the authors characterizes the location-dependence of SGD noise, which gives drastically different escaping behavior. By some simplification of the noise covariance matrix, the authors are abl... | SP:c08b370b56eba4fb58a1ea1cba0f45c9cd3143e7 |
Interventional Black-Box Explanations | 1 INTRODUCTION . The design of deep neural networks ( DNNs ) is built on complex structure of neurons , layers and operations ( e.g. , convolutions , non-linearity and back-propagation ) . These biologically-inspired designs are able to evolve by their own from training data . Their high dimensional parameter space all... | The authors aim to give post hoc explanations of Neural Network classifier decisions. They do so by finding causal relationships between model parameters and classifier outputs. To this end model parameters are set to zero and the change in the models prediction is calculated. If the change in prediction exceeds a thre... | SP:92c0086763b9510afcb490beb863cbd8a5e550d3 |
Interventional Black-Box Explanations | 1 INTRODUCTION . The design of deep neural networks ( DNNs ) is built on complex structure of neurons , layers and operations ( e.g. , convolutions , non-linearity and back-propagation ) . These biologically-inspired designs are able to evolve by their own from training data . Their high dimensional parameter space all... | This paper proposed a causal-driven method aiming to resolve the black-box issue of DNNs. The proposed interventional black-box explanations method tends to be model-agnostic and can apply a variety of DNN models. In the experiments, the authors examined the proposed method on two well-known DNN architectures LeNet and... | SP:92c0086763b9510afcb490beb863cbd8a5e550d3 |
Efficient Self-supervised Vision Transformers for Representation Learning | This paper investigates two techniques for developing efficient self-supervised vision transformers ( EsViT ) for visual representation learning . First , we show through a comprehensive empirical study that multi-stage architectures with sparse self-attentions can significantly reduce modeling complexity but with a co... | The paper investigates how to use self-supervised learning for multi-stage visual transformer models. Previous works have shown that SSL can learn image correspondences and lead to performant pre-trained models, while the multi-stage models can reduce the computation cost dramatically. This work tries to merge these tw... | SP:13071dbb937ba7f7c7cbeade305f6d59635dabdf |
Efficient Self-supervised Vision Transformers for Representation Learning | This paper investigates two techniques for developing efficient self-supervised vision transformers ( EsViT ) for visual representation learning . First , we show through a comprehensive empirical study that multi-stage architectures with sparse self-attentions can significantly reduce modeling complexity but with a co... | This paper develops an efficient self-supervised vision transformer for learning visual representations. It introduces a multi-stage architecture with sparse attentions to reduce computation complexity and proposes a new pretraining task of region matching to capture fine-grained region dependencies. The results on the... | SP:13071dbb937ba7f7c7cbeade305f6d59635dabdf |
DAIR: Disentangled Attention Intrinsic Regularization for Safe and Efficient Bimanual Manipulation | 1 INTRODUCTION . Consider the bimanual robot manipulation tasks such as rearranging multiple objects to their target locations in Figure 1 ( a ) . This complex and compositional task is very challenging as the agents will first need to reduce it to several sub-tasks ( pushing or grasping each object ) , and then the tw... | This paper proposes an implicit regularization for bimanual manipulation that enforces two robot arms to focus on different regions, which prevents both arms from performing on the same object at the same time. The proposed method realizes this idea by computing attention between robots and objects and then constrainin... | SP:7a40b403f19e24b0004ecc7a0bf1d9ba8ceae1d5 |
DAIR: Disentangled Attention Intrinsic Regularization for Safe and Efficient Bimanual Manipulation | 1 INTRODUCTION . Consider the bimanual robot manipulation tasks such as rearranging multiple objects to their target locations in Figure 1 ( a ) . This complex and compositional task is very challenging as the agents will first need to reduce it to several sub-tasks ( pushing or grasping each object ) , and then the tw... | This paper proposes an attention-based solution to dual-arm robot manipulation from sparse rewards that relies on a novel idea for intrinsic regularisation. The proposed regularisation term encourages each robotic arm to focus on separate subtasks and objects. The proposed approach aims to reduce the problem of extract... | SP:7a40b403f19e24b0004ecc7a0bf1d9ba8ceae1d5 |
IntSGD: Adaptive Floatless Compression of Stochastic Gradients | 1 INTRODUCTION . Many recent breakthroughs in machine learning were made possible due to the introduction of large , sophisticated and high capacity supervised models whose training requires days or even weeks of computation ( Hinton et al. , 2015 ; He et al. , 2016 ; Huang et al. , 2017 ; Devlin et al. , 2018 ) . Howe... | The paper introduces a randomized compress-to-integer operator with a shared scaling factor for use in data communication in distributed SGD. The resulting algorithm is provably convergent, matches the behavior of SGD up to constant factors, and works well with the all-reduce primitive. The authors claim three main c... | SP:ff0683b5929993e2f909081930bc30353a7a4d55 |
IntSGD: Adaptive Floatless Compression of Stochastic Gradients | 1 INTRODUCTION . Many recent breakthroughs in machine learning were made possible due to the introduction of large , sophisticated and high capacity supervised models whose training requires days or even weeks of computation ( Hinton et al. , 2015 ; He et al. , 2016 ; Huang et al. , 2017 ; Devlin et al. , 2018 ) . Howe... | The authors propose a quantized parallel SGD where the gradient coordinates are rounded after scaling: $Q_\alpha(g) = $ round$(\alpha g )/\alpha.$ The scaling factor $\alpha$ determines the quantization error: higher the $\alpha$, lower the quantization error. The authors propose a clever $\alpha$ shared across workers... | SP:ff0683b5929993e2f909081930bc30353a7a4d55 |
VAE Approximation Error: ELBO and Exponential Families | 1 INTRODUCTION . Variational autoencoders proposed by Kingma & Welling ( 2014 ) strive at learning complex data distributions pd ( x ) , x ∈ X in a generative way . They introduce latent variables z ∈ Z and model the joint distribution as pθ ( x | z ) p ( z ) , where p ( z ) is a simple distribution that is usually ass... | Summary. This paper presents an analysis of the approximation error of VAE models when the encoder and decoder are from exponential families. They show that when the model is consistent (i.e., the encoder is able to match the posterior), the encoder and decoder distributions are exponential family distributions that ar... | SP:2d237edc34601e158d7ed48ecc72bc873ae5f4dd |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.