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Few-Round Learning for Federated Learning | 1 Introduction . Today , valuable data are being collected increasingly at distributed edge nodes such as mobile phones , wearable client devices and smart vehicles/drones . Directly sending these local data to the central server for model training raises significant privacy concerns . To address this issue , an emergi... | This paper studied the combination of federated learning tasks in a meta-learning setting. In particular, with the assistance of the pre-trained meta-model, the new FL model's training can be completed within limited communication rounds. It was inspired by the meta-learning method used in few-shot learning scenario. T... | SP:ecc41670e8132da6dd5fdc3e75405c3060733512 |
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling | 1 INTRODUCTION . Modeling the dynamics of spatio-temporal data generated from networks of edge devices or nodes ( e.g . sensors , wearable devices and the Internet of Things ( IoT ) devices ) is critical for various applications including traffic flow prediction ( Li et al. , 2018 ; Yu et al. , 2018 ) , forecasting ( S... | Graph neural networks and federated learning are both promising directions of works individually. This papers is apparently one of the first few attempts to combine them for spatio-temporal data modeling. The time series data in the local nodes is modelled by an Encoder-Decoder architecture and spatial locality propert... | SP:a7dd38170e565b5450928720a51a50952ce48d86 |
A generalized probability kernel on discrete distributions and its application in two-sample test | We propose a generalized probability kernel ( GPK ) on discrete distributions with finite support . This probability kernel , defined as kernel between distributions instead of samples , generalizes the existing discrepancy statistics such as maximum mean discrepancy ( MMD ) as well as probability product kernels , and... | The paper under review proposes to generalize MMD for discrete random variables whose labels take value in $\mathbb{R}^k$. They propose to estimate these generalized probability kernel distance using empirical estimators. Their properties are studied for two particular examples, namely a kernelized Stein discrenpancy a... | SP:9a099507d376dd1553a8d11b821ce564b8a595ff |
Generalized Gumbel-Softmax Gradient Estimator for Generic Discrete Random Variables | 1 INTRODUCTION . Stochastic computational graphs , including deep generative models such as variational autoencoders , are widely used for representation learning . Optimizing the network parameters through gradient methods requires an estimation of the gradient values , but the stochasticity requires the computation o... | The paper presents a generalization of the Gumbel-Softmax gradient estimator. The original Gumbel-Softmax is usually applied to Bernoulli and categorical random variables. The method proposed in the paper attempts to extend it applicability to other discrete distributions, such as Poisson, multinomial, geometric, among... | SP:5f22f64538ccd28123d51c7f8b16fe056cc5dc0b |
Structure Controllable Text Generation | 1 INTRODUCTION . Natural language is not just a sequence collections of tokens but a structure well-organized sequence expressing understandable information . The structure of language usually obeys a set of grammatical rules , which helps beginners grasp the language with less efforts . Similarly , incorporating the s... | This paper presents a text generation model conditioned on desired structures. The proposed method is essentially a translation model from structure information (represented with multiple sequences of tokens) to a text. This study converts a text into structure information such as part of speech (POS) and participial c... | SP:94c1fa434cf2eb8f4f762cd06cf838b0018c6fa0 |
Non-Linear Rewards For Successor Features | Recently , Reinforcement Learning ( RL ) algorithms have achieved superhuman performance in several challenging domains , such as Atari ( Mnih et al. , 2015 ) , Go ( Silver et al. , 2016 ) , and Starcraft II ( Vinyals et al. , 2019 ) . The main driver of these successes has been the use of deep neural networks , which ... | Successor representations are an old idea that has seem recent interest in the ML community. The idea is conceptually straightforward, by assuming the rewards are linear in some space $r = \vect{\phi}(s, a) \cdot \vect{w}$ then we learn something analogous to an action-value function for the discounted expected feature... | SP:2ffc4cfa0da20b936bb4abee091f2d056dc12dfc |
A Mixture of Variational Autoencoders for Deep Clustering | 1 INTRODUCTION . Clustering is one of the most fundamental techniques used in unsupervised machine learning . It is the process of classifying data into several classes without using any label information . In the past decades , a plethora of clustering methods have been developed and successfully employed in various f... | The paper proposed to cluster the data using k different VAEs’. The method is different from the existing VAE-based deep clustering method (VaDE), which uses only one VAE but employs a Gaussian mixture prior to achieve the clustering goal. The difficulties of the proposed model lie at how to train the model efficiently... | SP:1e11e9ad7288da902ed69a7735d1d89e81692b54 |
BERTology Meets Biology: Interpreting Attention in Protein Language Models | 1 INTRODUCTION . The study of proteins , the fundamental macromolecules governing biology and life itself , has led to remarkable advances in understanding human health and the development of disease therapies . The decreasing cost of sequencing technology has enabled vast databases of naturally occurring proteins ( El... | The authors analyzed how attention and embeddings of Transformers trained on protein sequence correlate with protein properties such as pairwise contacts, binding sites, and post-translational modifications. The paper extends existing papers such as Rives 2020 ‘Biological structure and function emerge…’ by showing that... | SP:b33ac0129381deaa5375b1f6b06b70d58f16a5a9 |
Optimization Planning for 3D ConvNets | 1 INTRODUCTION . The recent advances in 3D Convolutional Neural Networks ( 3D ConvNets ) have successfully pushed the limits and improved the state-of-the-art of video recognition . For instance , an ensemble of LGD3D networks ( Qiu et al. , 2019 ) achieves 17.88 % in terms of average error in trimmed video classificat... | The paper proposes a novel way to automatically tune 3D ConvNet hyper-parameters (learning rate, input clip length, sampling way). This is achieved by decomposing the optimization path into several states and the state transition is triggered when the knee-point on the performance-epoch curve is met. Extensive experime... | SP:128ae7bc4a53360e9492783e7430da8c778f3d66 |
Score-based Causal Discovery from Heterogeneous Data | 1 INTRODUCTION . Discovering causal relations among variables is a fundamental problem in various fields such as economics , biology , drug testing , and commercial decision making . Because conducting randomized controlled trials is usually expensive or even infeasible , discovering causal relations from observational... | This paper proposes strategies for learning the structure of multiple sets of data observed over a common set of variables which may exhibit distribution shift. The authors address this problem by augmenting the dataset with an indicator variable which indicates membership to dataset. After augmenting the dataset stan... | SP:9e4232a23a81fe31b824208547760a9906a05a4a |
Reinforcement Learning with Random Delays | 1 INTRODUCTION This article is concerned with the Reinforcement Learning ( RL ) scenario depicted in Figure 1 , which is commonly encountered in real-world applications ( Mahmood et al. , 2018 ; Fuchs et al. , 2020 ; Hwangbo et al. , 2017 ) . Oftentimes , actions generated by the agent are not immediately applied in th... | The paper introduces an algorithm for the case where actions have delayed effects in RL, and specifically in the case where the delay is random. A resampling approach is applied to off policy buffered data in order to align it with the current policy and this approach is integrated into a SAC architecture, creating th... | SP:37e441bbd53413fb7ae61d146145795c481a2bf0 |
Fine-grained Synthesis of Unrestricted Adversarial Examples | 1 INTRODUCTION . Adversarial examples , inputs resembling real samples but maliciously crafted to mislead machine learning models , have been studied extensively in the last few years . Most of the existing papers , however , focus on normconstrained attacks and defenses , in which the adversarial input lies in an -nei... | This paper proposes a mechanism to generate adversarial examples by applying latent variables level manipulation, based on the styleGAN framework. Unlike previous works mostly focused on image level perturbations and geometry transformations, this work tends to control higher level latent sampling such as style, so as ... | SP:6fc9ae204ba7ca8db33d3ce39362ab05d36eec97 |
Signed Graph Diffusion Network | 1 INTRODUCTION . Given a signed social graph , how can we learn appropriate node representations to infer the signs of missing edges ? Signed social graphs model trust relationships between people with positive ( trust ) and negative ( distrust ) edges . Many online social services such as Epinions ( Guha et al. , 2004... | In this paper, the author studied the problem of node embedding in signed networks. The authors proposed SGDNet which combines the idea of diffusion/random work in signed networks and Residual connection in GCN. The network is trained directly with classification loss on edge sign prediction. The authors carried out ex... | SP:597472fc14f399625474d13df3453d6377a6c465 |
Predicting the impact of dataset composition on model performance | 1 INTRODUCTION . The success of large scale machine learning systems depends critically on the quantity and quality of data used during training , and we can not expect these systems to succeed if there is not enough training data or if that data does not cover all the phenomena contained in the test distribution ( Ben... | This work studies the problem of predicting model performance with more training data when the data are collected from different sources. The predictor is a function of the number training examples, and the ratio of examples from each source. The predictor needs to be built from a small number of training examples the ... | SP:bbd6a6fcf9731e02fdf3e45c4eb4156be2c38d33 |
Learning Mesh-Based Simulation with Graph Networks | 1 INTRODUCTION . State-of-the art modeling of complex physical systems , such as deforming surfaces and volumes , often employs mesh representations to solve the underlying partial differential equations ( PDEs ) . Mesh-based finite element simulations underpin popular methods in structural mechanics [ 31 , 48 ] , aero... | This paper presents a graph-network-based architecture for learning to perform mesh-based simulations, which can be run more efficiently than the full, "ground-truth" simulations. The experiments demonstrate that the proposed method is able to learn to simulate a wide range of different physical scenarios. Moreover, th... | SP:a52f70b4b90309b1553f59e8730e6378ad57b684 |
Differentially Private Synthetic Data: Applied Evaluations and Enhancements | 1 INTRODUCTION . Maintaining an individual ’ s privacy is a major concern when collecting sensitive information from groups or organizations . A formalization of privacy , known as differential privacy , has become the gold standard with which to protect information from malicious agents ( Dwork , TAMC 2008 ) . Differe... | The proposed method works as follows. Given samples are partitioned into two parts; one is for classifier training and the other is for data synthesizer training. Both are trained in a differentially private manner. After training, the DP synthesizer generates samples and the DP classifier labels them so that the resul... | SP:a8ae05c783e0c619cb859c4ad6da479529bf7af4 |
Recall Loss for Imbalanced Image Classification and Semantic Segmentation | 1 INTRODUCTION . Dataset imbalance is an important problem for many computer vision tasks such as semantic segmentation and image classification . In semantic segmentation , imbalance occurs as a result of natural occurrence and varying sizes of different classes . For example , in an outdoor driving segmentation datas... | A novel recall loss (RecallCE) that considers dynamically-changing class recalls is proposed in this paper to mitigate class imbalance in long-tailed recognition problems. The class recalls are estimated using either the current batch statistics or an exponential moving average, depending on the number of class (or cla... | SP:959ed37c07a831c71c5dd586a5940313e62b7018 |
Mind the Pad -- CNNs Can Develop Blind Spots | 1 MOTIVATION Convolutional neural networks ( CNNs ) serve as feature extractors for a wide variety of machinelearning tasks . Little attention has been paid to the spatial distribution of activation in the feature maps a CNN computes . Our interest in analyzing this distribution is triggered by mysterious failure cases... | The paper studies the effect of padding on artefacts in CNN feature maps and performance on image classification and object detection. It convincingly makes the case that these artefacts have a significant detrimental effect on task performance, e.g. leading to blind spots / missed detections of small objects near the ... | SP:1ccd6cfc6dce5a3f4b0c65dd1625f71ac3225c2d |
"Hey, that's not an ODE'": Faster ODE Adjoints with 12 Lines of Code | 1 INTRODUCTION . We begin by recalling the usual set-up for neural differential equations . 1.1 NEURAL ORDINARY DIFFERENTIAL EQUATIONS . The general approach of neural ordinary differential equations ( E , 2017 ; Chen et al. , 2018 ) is to use ODEs as a learnable component of a differentiable framework . Typically the ... | The paper proposes a modification for the adjoint method, such that to improve the training efficiency of neural ODEs. The proposed idea is that the solution of some terms in the adjoint method can be less accurate, because these are not ODEs but simple integrals, and hence, the error does not propagate. Thus, the solv... | SP:4a4c6ede9645c5b814a84fbd9e91472f0888621e |
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models | 1 INTRODUCTION . Recently , research related to learning on graph structural data has gained considerable attention in machine learning community . Graph neural networks ( Gori et al. , 2005 ; Hamilton et al. , 2017 ; Veličković et al. , 2018 ) , particularly graph convolutional networks ( Kipf & Welling , 2017 ; Def... | By integrating Adaboosting and a fully connected layer, this paper provides a new graph neural network structure. The objective of this paper is to design a deeper graph models in an efficient way for better performance. The computational efficiency and performance of the proposed algorithm are evaluated using the task... | SP:43b0b8d8e0c30180cb627ef62898028f5e7dfec8 |
Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization | We propose a simple , general and effective technique , Reward Randomization for discovering diverse strategic policies in complex multi-agent games . Combining reward randomization and policy gradient , we derive a new algorithm , RewardRandomized Policy Gradient ( RPG ) . RPG is able to discover multiple distinctive ... | This paper considers the problem of finding a nash equilibrium in two player games where each of the algorithm runs an RL algorithm. In this paper they ask the question -- which nash equilibria does the dynamics converge to in this two player game (where each player optimizes based on a policy gradient algorithm). They... | SP:04a93ed7a7bef0c8f8c99a1fa381cc920fbd2002 |
Predicting Video with VQVAE | 1 INTRODUCTION . When it comes to real-world image data , deep generative models have made substantial progress . With advances in computational efficiency and improvements in architectures , it is now feasible to generate high resolution , realistic images from vast and highly diverse datasets ( Brock et al. , 2019 ; ... | The authors propose to use a VQVAE-2 setup for video prediction. In particular, they propose a hierarchical discrete latent variable model that compresses videos into a latent space. An autoregressive model is then used to model dynamics in this latent space, which has reduced dimensionality, and can be used together w... | SP:24344b20e162a68ed6631aa050c2c09a8f91d5ac |
Initialization and Regularization of Factorized Neural Layers | 1 INTRODUCTION . Most neural network layers consist of matrix-parameterized functions followed by simple operations such as activation or normalization . These layers are the main sources of model expressivity , but also the biggest contributors to computation and memory cost ; thus modifying these layers to improve co... | This paper studies initialization and regularization in factorized neural networks (reparameterize a weight matrix by the product of several weight matrices). The authors proposed spectral initialization, that is to initialize the factorized matrices using the SVD of the un-factorized matrix. The authors also proposed ... | SP:070b8df785712a7741fa4a986ef99f3c47f52b1a |
Perturbation Type Categorization for Multiple $\ell_p$ Bounded Adversarial Robustness | 1 INTRODUCTION . There has been a long line of work studying the vulnerabilities of machine learning models to small changes in the input data . In particular , most existing works focus on ` p bounded perturbations ( Szegedy et al. , 2013 ; Goodfellow et al. , 2015 ) . While majority of the prior work aims at achievin... | The paper proposes a two-stage defense method to improve the adversarial robustness over different perturbation types. Specifically, it first builds a hierarchical binary classifier to differentiable the perturbation types and then uses the result to guide to its corresponding defense models. It first proves the diffe... | SP:f33566d66d4f232d32107d392bb27c110b0b0ae3 |
BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network | 1 INTRODUCTION . Graphs are important research objects in the field of machine learning as they are good carriers for structural data such as social networks and citation networks . Recently , graph neural networks ( GNNs ) received extensive attention due to their great performances in graph representation learning . ... | This paper proposed a new graph convolutional network. It considers not only the original graph structure information but also the latent correlations between features, resulting in a graph neural network as a bi-directional low-pass filter. The new filter is derived using the alternating direction method of multiplier... | SP:8051813e72f10269c587a17450be5f23973595de |
Adversarially Guided Actor-Critic | 1 INTRODUCTION . Research in deep reinforcement learning ( RL ) has proven to be successful across a wide range of problems ( Silver et al. , 2014 ; Schulman et al. , 2016 ; Lillicrap et al. , 2016 ; Mnih et al. , 2016 ) . Nevertheless , generalization and exploration in RL still represent key challenges that leave mos... | This paper proposed a new actor-critic framework with adversary guide for deep reinforcement learning (RL), and introduced new Kullback-Leiblier divergence bonus term based on the difference between actor network and adversary network to deal with the exploration in RL. The experimental results showed the merit of this... | SP:4a7558123aa3ce672415a3e07eb3077d3ff92730 |
Accounting for Unobserved Confounding in Domain Generalization | 1 INTRODUCTION . Prediction algorithms use data , necessarily sampled under specific conditions , to learn correlations that extrapolate to new or related data . If successful , the performance gap between these two domains is small , and we say that algorithms generalize beyond their training data . Doing so is diffic... | This paper proposes a new regularizer that can be plugged in gradient-based learning algorithms, which aims at solving the problems induced by unobserved confounders. And the authors provide the upper bound for one specific kind of distributionally robust optimization problem, whose uncertainty set is defined as the af... | SP:5964ce1b29c23bb9e4b9a83a466ca0bc3f869183 |
Group-Connected Multilayer Perceptron Networks | 1 INTRODUCTION . Deep neural networks have been quite successful across various machine learning tasks . However , this advancement has been mostly limited to certain domains . For example in image and voice data , one can leverage domain properties such as location invariance , scale invariance , coherence , etc . via... | The paper describes an MLP architectures for problems in which the features do not have a known structure (eg, tabular data). A "differentiable routing matrix" partitions the data into K blocks. Then, standard MLPs are applied to each block and the results are recursively aggregated by moving forward in the model. | SP:dbb0ed3b53fc0905982b51853e83f5cdbaf3b535 |
Task-Agnostic and Adaptive-Size BERT Compression | 1 INTRODUCTION . Pre-trained Transformer ( Vaswani et al. , 2017 ) -based language models like BERT ( Devlin et al. , 2019 ) , XLNet ( Yang et al. , 2019 ) and RoBERTa ( Liu et al. , 2019 ) have achieved impressive performance on a variety of downstream natural language processing tasks . These models are pre-trained o... | This paper proposes to search architectures of BERT model under various memory and latency contraints. The search algorithm is conducted by pretraining a big supernet that contains the all the sub-network structures, where the optimal models for different requirements are selected from it. Once an architecture is found... | SP:adfae2d05cdf908663fa093cd58f0e8d50ab2d9a |
Deep Learning Is Composite Kernel Learning | 1 INTRODUCTION . The success of deep learning is attributed to feature learning . The conventional view is that feature learning happens in the hidden layers of a deep network : in the initial layers simple low level features are learnt , and sophisticated high level features are learnt as one proceeds in depth . In th... | This paper builds on recent work characterising deep neural networks in terms of Neural Tangent Kernels and Neural Path Features. Over the past few years, a number of papers have developed the theory of Neural Tangent Kernels, which can be used to interpret infinite width deep neural networks in the context of a partic... | SP:55f630e6b41243dfe92ea4269bb1a1e6e8109974 |
Return-Based Contrastive Representation Learning for Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( RL ) algorithms can learn representations from high-dimensional inputs , as well as learn policies based on such representations to maximize long-term returns simultaneously . However , deep RL algorithms typically require large numbers of samples , which can be quite expe... | The authors propose the inclusion of an auxiliary task for training an RL model, where the auxiliary task objective is to learn an abstraction of the state-action space that clusters (s,a) pairs according to their expected return. The authors first describe a basic abstraction learning framework (Z-learning) followed ... | SP:e17f92caae3e2bd4830eadeb4b268c1c82d43e4d |
Adaptive Hierarchical Hyper-gradient Descent | 1 INTRODUCTION . The basic optimization algorithm for training deep neural networks is the gradient descent method ( GD ) , which includes stochastic gradient descent ( SGD ) , mini-batch gradient descent , and batch gradient descent . The model parameters are updated according to the first-order gradients of the empir... | Setting appropriate learning rate for network optimization is an important task in deep learning applications. This paper investigates the setting of learning rates for network parameters in different levels, e.g., individual parameter, each layer and global levels. By setting the constraints on the learning rates at m... | SP:bd0775160c5ab06f765a031236995c84926b5f70 |
Linear Convergence and Implicit Regularization of Generalized Mirror Descent with Time-Dependent Mirrors | 1 INTRODUCTION . Recent work has established the optimization and generalization benefits of over-parameterization in machine learning ( Belkin et al. , 2019 ; Liu et al. , 2020 ; Zhang et al. , 2017 ) . In particular , several works including Vaswani et al . ( 2019 ) ; Du et al . ( 2018 ) ; Liu et al . ( 2020 ) ; Li &... | This paper studies the interesting property of generalized mirror descent (GMD) and its stochastic variant for nonconvex optimization problems. First, for GMD this paper shows the linear convergence under PL* condition (in Lemma 1) and finds out a new sufficient condition for the linear convergence (in Theorem 2). Next... | SP:0007eeef2280b8cd027be08249b27e2116328ab8 |
DHOG: Deep Hierarchical Object Grouping | 1 INTRODUCTION . It is very expensive to label a dataset with respect to a particular task . Consider the alternative where a user , instead of labelling a dataset , specifies a simple set of class-preserving transformations or ‘ augmentations ’ . For example , lighting changes will not change a dog into a cat . Is it ... | This paper addresses the problem of unsupervised learning of class representation using data augmentation. Its key idea is to encourage the learned representations to have low MI while maximizing the original augmentation-driven MI objective. It reports the improved performance for the benchmarks of Ji et al. 2019 – cl... | SP:2eec02429adee2ab91752629c85df9f1463e54d8 |
Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU | 1 INTRODUCTION . The signature transform , sometimes referred to as the path signature or simply signature , is a central object in rough path theory ( Lyons , 1998 ; 2014 ) . It is a transformation on differentiable paths1 , and may be thought of as loosely analogous to the Fourier transform . However whilst the Fouri... | The paper presents the first GPU-capable library implementing the _"signature"_ and _"log-signature"_ functions as well as their gradients. It introduces these transformations to a machine learning audience, as well as their recent uses in ML, then proposes algorithmic improvements that reduce the necessary computation... | SP:22fbfa80cf81ea79a19faee749e9c8b2e23f1f3f |
K-PLUG: KNOWLEDGE-INJECTED PRE-TRAINED LANGUAGE MODEL FOR NATURAL LANGUAGE UNDERSTANDING AND GENERATION | 1 INTRODUCTION . Pre-trained language models ( PLMs ) , such as ELMo ( Peters et al. , 2018 ) , GPT ( Radford et al. , 2018 ) , BERT ( Devlin et al. , 2019 ) , RoBERTa ( Liu et al. , 2019 ) , and XLNet ( Yang et al. , 2019 ) , have made remarkable breakthroughs in many natural language understanding ( NLU ) tasks , inc... | This paper proposes pretraining language model for e-commerce domain. Specifically, the authors design five pretraining objectives to incorporate various domain knowledge into the the models with an encoder-decoder architecture. When further finetuned on language understanding and generation tasks in the e-commerce dom... | SP:42107a5481baa3bdf72b965d0db08ef92b78a92f |
Learning Self-Similarity in Space and Time as a Generalized Motion for Action Recognition | 1 INTRODUCTION . Learning spatio-temporal dynamics is the key to video understanding . To this end , extending convolutional neural networks ( CNNs ) with spatio-temporal convolution has been actively investigated in recent years ( Tran et al. , 2015 ; Carreira & Zisserman , 2017 ; Tran et al. , 2018 ) . The empirical ... | This submission proposed a motion representation method based on spatio-temporal self-similarity (STSS), which represents each local region as similarities to its neighbors in both spatial and temporal dimension. There are previous works (e.g., Ref[1] , [2], [5] listed here) which utilize STSS for feature extractions, ... | SP:970151fd51696294ccd5746783a07d4cfab90054 |
Zero-Cost Proxies for Lightweight NAS | 1 INTRODUCTION . Instead of manually designing neural networks , neural architecture search ( NAS ) algorithms are used to automatically discover the best ones ( Tan & Le , 2019a ; Liu et al. , 2019 ; Bender et al. , 2018 ) . Early work by Zoph & Le ( 2017 ) proposed using a reinforcement learning ( RL ) controller tha... | This paper provides an extensive empirical evaluation of zero-cost proxies which can be combined with existing NAS methods to speed up search time. The proposed method utilizes ‘pruning-at-initialization’ works which computes gradient-computation at initialization as a proxy for performance of the given neural architec... | SP:2409111bd2e2211c6e3c11c4c4eaf494d14e3f44 |
Hard Masking for Explaining Graph Neural Networks | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) are a flexible and powerful family of models that build representations of nodes or edges on irregular graph-structured data and have experienced significant attention in recent years . These methods are based on the so-called “ neighborhood aggregation ” scheme in which ... | This work proposes to explain graph neural networks using hard masking techniques. Specifically, it tries to find the node mask $V_s$ and feature mask $F_s$ which can identify the most important information of the input such that the masked information can yield a high fidelity score. This work proposes a greedy method... | SP:ee844974cf8fa5c95205cf27dfc9b80a277aa469 |
Approximation Algorithms for Sparse Principal Component Analysis | 1 INTRODUCTION . Principal Component Analysis ( PCA ) and the related Singular Value Decomposition ( SVD ) are fundamental data analysis and dimension reduction tools in a wide range of areas including machine learning , multivariate statistics and many others . They return a set of orthogonal vectors of decreasing imp... | This paper proposed three simple algorithms for sparse principal component analysis (SPCA): a) randomized matrix multiplication; b) deterministic thresholding scheme; and c) semidefinite programming relaxation. All of the proposed algorithms look like native combinations of existing techniques and simple sparsification... | SP:0f29e5886a7840aacdbce931b6c795d43b545172 |
Should Ensemble Members Be Calibrated? | 1 INTRODUCTION . Deep learning approaches achieve state-of-the-art performance in a wide range of applications , including image classification . However , these networks tend to be overconfident in their predictions , they often exhibit poor calibration . A system is well calibrated , if when the system makes a predic... | The paper makes an analysis of calibration in ensembles of deep learning models. Through some theoretical developments, the paper supports that a given ensemble cannot be more confident than the average individual members for regions where the ensemble is well calibrated. Empirical results, on CIFAR-100 and three diffe... | SP:c7c0fc5a3d6319117b445707e7818c6f292bf533 |
ERMAS: Learning Policies Robust to Reality Gaps in Multi-Agent Simulations | 1 INTRODUCTION . Reinforcement learning ( RL ) offers a tool to optimize policy decisions affecting complex , multiagent systems ; for example , to improve traffic flow or economic productivity . In practice , the need for efficient policy evaluation necessitates training on simulations of multi-agent systems ( MAS ) .... | This paper proposes an interesting method for being able to act and plan robustly in a multiagent simulation and be robust to the reality gap between training time and testing time for agents in a marl setting. the method does show improvements in terms of being able to train the policy for this use case and being more... | SP:2e8e7fca411be533fbe6069ba360c17189be2fee |
CANVASEMB: Learning Layout Representation with Large-scale Pre-training for Graphic Design | Layout representation , which models visual elements in a canvas and their interrelations , plays a crucial role in graphic design intelligence . With a large variety of layout designs and the unique characteristic of layouts that visual elements are defined as a list of categorical ( e.g . shape type ) and numerical (... | This paper applies state-of-the-art transformer-based neural networks to layout representation learning of slides. The most notable contribution of this paper is the construction of large-scale parsed slide layout dataset. This paper proposes to pre-train the network on this large-scale dataset without masked reconstru... | SP:a4900e2a8fbd39245400e377869f8c5350ce12fd |
DISE: Dynamic Integrator Selection to Minimize Forward Pass Time in Neural ODEs | 1 INTRODUCTION . Neural ordinary differential equations ( Neural ODEs ) are to learn time-dependent physical dynamics describing continuous residual networks ( Chen et al. , 2018 ) . It is well known that residual connections are numerically similar to the explicit Euler method , the simplest integrator to solve ODEs .... | This paper addresses the complexity of the forward pass inference in neural ODEs. The paper proposes to augment training of the neural ODE with an auxiliary neural network that dynamically selects the best numerical integrator for a given input sample. Furthermore, the paper also proposes a regularizer that uses the e... | SP:e030bf232cd040a4c2ea834f6d803d7fcf4aa971 |
Perfect density models cannot guarantee anomaly detection | 1 INTRODUCTION . Several machine learning methods aim at extrapolating a behavior observed on training data in order to produce predictions on new observations . But every so often , such extrapolation can result in wrong outputs , especially on points that we would consider infrequent with respect to the training dist... | Detecting anomalies is a notoriously ill-defined problem. The notion of anomaly is not a rigorous concept and different algorithms produce different results. The paper critiques a broad set of methods which involve likelihood (or density) estimations. It's main idea revolves around the 'Principle' set on Page 4. The pr... | SP:7005dadb8330bdc6f7f2a066ff816bbe174ec843 |
AUTOSAMPLING: SEARCH FOR EFFECTIVE DATA SAMPLING SCHEDULES | 1 INTRODUCTION . Data sampling policies can greatly influence the performance of model training in computer vision tasks , and therefore finding robust sampling policies can be important . Handcrafted rules , e.g . data resampling , reweighting , and importance sampling , promote better model performance by adjusting t... | The authors mainly concentrate on data sampling. To address the issue of optimizing high-dimensional sampling hyper-parameter in data sampling and release the requirement of prior knowledge from current methods, the authors introduce a searching-based method named AutoSampling. This method is comprised of exploration s... | SP:6b7e12310d7b29f8d66442933dd71b1b915805be |
Adversarial representation learning for synthetic replacement of private attributes | 1 INTRODUCTION . Increasing capacity and performance of modern machine learning models lead to increasing amounts of data required for training them ( Goodfellow et al. , 2016 ) . However , collecting and using large datasets which may contain sensitive information about individuals is often impeded by increasingly str... | The paper introduces a framework to privatize sensitive attributes of data using adversarial representation learning. The proposed method consists of a “filter” that removes the sensitive attribute from the data representation, and a “generator” that replaces the removed sensitive attribute with a randomly sampled synt... | SP:fb5575d5c26f54fbccbc9de46440c174fe46abdf |
Model-Based Offline Planning | 1 INTRODUCTION . Learnt policies for robotic and industrial systems have the potential to both increase existing systems ’ efficiency & robustness , as well as open possibilities for systems previously considered too complex to control . Learnt policies also afford the possibility for non-experts to program controllers... | This work studies the offline RL problem and proposes MBOP for the same. The proposed method learns ensembles of dynamics models, behavioral policies, and value functions using the offline dataset. Subsequently, the approach uses online MPC with a learned terminal value function. The paper demonstrates experimental res... | SP:0cd88bb9be953a5db3d9ac0208848b26a6f4e1bd |
Variational Auto-Encoder Architectures that Excel at Causal Inference | 1 INTRODUCTION . As one of the main tasks in studying causality ( Peters et al. , 2017 ; Guo et al. , 2018 ) , the goal of Causal Inference is to figure out how much the value of a certain variable would change ( i.e. , the effect ) had another certain variable ( i.e. , the cause ) changed its value . A prominent examp... | Some generative models have been proposed for causal effect estimation but they often do not have a competitive performance. Recent work suggested that a combination of generative and discriminative model may improve treatment estimation with observational data, and further suggests a generic latent variable model for ... | SP:06ca143a8bf0570ccfba6abcb37a22ab23a9c3dd |
Real-time Uncertainty Decomposition for Online Learning Control | 1 INTRODUCTION . With improved sensor quality and more powerful computational resources , data-driven models are increasingly applied in safety-critical domains such as autonomous driving or human-robot interaction ( Grigorescu et al. , 2020 ) . However , measurements usually suffer from noise and the available data is... | The authors consider the problem of efficient modeling of epistemic uncertainty, separated from aleatoric uncertainty, for neural networks. They propose a novel methodology, involving automatically constructing a epistemic uncertainty support data set used to extend a given NN with an epistemic uncertainty output. The... | SP:1efc842f413903e41727a6b79b9d3ea89011a85b |
Large Batch Simulation for Deep Reinforcement Learning | 1 INTRODUCTION . Speed matters . It is now common for modern reinforcement learning ( RL ) algorithms leveraging deep neural networks ( DNNs ) to require billions of samples of experience from simulated environments ( Wijmans et al. , 2020 ; Petrenko et al. , 2020 ; OpenAI et al. , 2019 ; Silver et al. , 2017 ; Vinyals... | This paper shows that batch simulation can accelerate reinforcement learning in 3D environments. Batch simulation accepts and executes large batches of simulation requests at the same time on one accelerator. The authors demonstrate that this technique can substantially speed up the processing and achieve ~100x speed u... | SP:085509d909d9fc476066424fd561bcebf6c57e51 |
DINO: A Conditional Energy-Based GAN for Domain Translation | 1 INTRODUCTION . Domain translation methods exploit the information redundancy often found in data from different domains in order to find a mapping between them . Successful applications of domain translation include image style transfer ( Zhu et al. , 2017a ) and speech-enhancement ( Pascual et al. , 2017 ) . Further... | The paper proposes an adversarial framework DINO to train translation models from source to target and target to source. The basic idea is to replace generator and discriminator in the energy based GAN with two source-to-target generation models. The discriminator(reverse generator) and the generator competes in a mini... | SP:24ee3df238dc009de59a51589f2e171d750b345e |
Deconstructing the Regularization of BatchNorm | Batch normalization ( BatchNorm ) has become a standard technique in deep learning . Its popularity is in no small part due to its often positive effect on generalization . Despite this success , the regularization effect of the technique is still poorly understood . This study aims to decompose BatchNorm into separate... | The paper empirically studies the regularization of BN. It proposes the point that the BN's effect is connected with the regularizing against explosive growth in the final layer. To motivate this point, it takes a single-layer case and shows the BN approximately penalizes on the norm of the feature embedding thereon. T... | SP:88c3a4a7498801de3d7442253a4aeae5b83a3eb5 |
Frequency-aware Interface Dynamics with Generative Adversarial Networks | 1 INTRODUCTION . Complex and chaotic physical phenomena such as liquids , gels and goo are still very challenging when it comes to representing them as detailed and realistically as possible . A variety of numerical methods have been proposed to simulate such materials , from purely Eulerian methods ( Harlow & Welch , ... | This paper presents a GAN framework to learn spatial and temporal representation on complex physical surfaces to apply to simulation. The method represents data in a SDF-like way so it is agnostic to the properties of material and simulation model. The network is built upon conventional GAN, with two discriminator for ... | SP:da34f0f0c8f4887dc84cdb63ec13ac7550e0c37c |
Characterizing Lookahead Dynamics of Smooth Games | As multi-agent systems proliferate in machine learning research , games have attracted much attention as a framework to understand optimization of multiple interacting objectives . However , a key challenge in game optimization is that , in general , there is no guarantee for usual gradient-based methods to converge to... | This paper investigates „lookahead dynamics of smooth games“. By this the authors mean discrete-time dynamical systems generating from a given algorithm by adding a relaxation step in the updates. The main aim of the paper is to solve smooth games. Under sufficient convexity assumptions Nash equilibria for such games c... | SP:96e4c8e540941178aa3a9d9c0f11a58128a87e26 |
Disentangling Adversarial Robustness in Directions of the Data Manifold | 1 INTRODUCTION . In recent years , deep neural networks ( DNNs ) ( Krizhevsky et al . ( 2012 ) ; Hochreiter and Schmidhuber ( 1997 ) ) have become popular and successful in many machine learning tasks . They have been used in different problems with great success . But DNNs are shown to be vulnerable to adversarial exa... | This paper analytically considers two flavours of adversarial training in a Gaussian mixture model. The first uses regular adversarial examples, and the second uses examples drawn from a generative model. The authors show that the adversarial perturbations generated in the two cases differ in a cleanly-characterisabl... | SP:8ebdf09acf96ca3dc86a413fdcd2f524d2a54cb7 |
Towards Learning to Remember in Meta Learning of Sequential Domains | 1 INTRODUCTION . Humans have the ability to quickly learn new skills from a few examples , without erasing old skills . It is desirable for machine-learning models to adopt this capability when learning under changing contexts/domains , which are common scenarios for real-world problems . These tasks are easy for human... | The paper proposes a method for the sequential meta-learning problem. The author meta learn not only model parameters but also learning rate vectors for parameter blocks. To this end, the meta-learn model finds appropriate model parameters and adaptive learning rate vectors that capture task-general information. Overal... | SP:000d80bbed580799f47117d2c65cb08f17b783e3 |
Provable Robust Learning for Deep Neural Networks under Agnostic Corrupted Supervision | 1 INTRODUCTION . Corrupted supervision is a common issue in real-world learning tasks , where the learning targets are not accurate due to various factors in the data collection process . In deep learning models , such corruptions are especially severe , whose degree-of-freedom makes them easily memorize corrected exam... | In this paper, the authors studied the problem of training neural networks under data poisoning, i.e., when a small fraction of the training data is corrupted by the adversary. They considered two data corruption settings, one allows both the data x and supervision y to be corrupted, which is called general corruption,... | SP:e3330123a00c4e32e60792230c6a7a883e84aa98 |
Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning | 1 INTRODUCTION . Despite great ability in learning discriminative features , deep neural network ( DNN ) classifiers often make over-confident predictions . This can lead to potentially catastrophic consequences in safety critical applications , e.g. , medical diagnosis and autonomous driving perception tasks . A multi... | This paper highlights the issues with the scaling method and histogram binning i.e., underestimate calibration error in scaling methods and failing to preserve classification accuracy, and sample-inefficiency in HB. They use the I-Max concept for binning, which maximizes the mutual information between labels and quanti... | SP:6dd9907f23d32802fd10d9405d165269fd1492ee |
Mitigating Deep Double Descent by Concatenating Inputs | 1 INTRODUCTION . Underparameterization and overparameterization are at the heart of understanding modern neural networks . The traditional notion of underparameterization and overparameterization led to the classic U-shaped generalization error curve ( Trevor Hastie & Friedman , 2001 ; Stuart Geman & Doursat , 1992 ) ,... | The paper investigates the double descent phenomenon. It proposes the augmentation of the dataset via concatenating the covariate x and interpolating the label y, which increases the data size from n to n^2. The paper shows that the phenomenon of double descent can be mitigated via augmenting the input. The idea of inv... | SP:8be0ea7136590dd63b9a82556995ef1e7b1d644c |
Latent Skill Planning for Exploration and Transfer | 1 INTRODUCTION Humans can effortlessly compose skills , where skills are a sequence of temporally correlated actions , and quickly adapt skills learned from one task to another . In order to build re-usable knowledge about the environment , Model-based Reinforcement Learning ( MBRL ) ( Wang et al. , 2019 ) provides an ... | The paper proposes combining model-based RL with high-level skill learning and composition through hierarchical RL, into a single reinforcement learning framework. More specifically, the proposed approach leverages planning and composing skills in the low-dimensional, high-level representation, and learn low-level skil... | SP:496ef52f5094a12fe59e9966848b69b54c7763fd |
Bag of Tricks for Adversarial Training | 1 INTRODUCTION . Adversarial training ( AT ) has been one of the most effective defense strategies against adversarial attacks ( Biggio et al. , 2013 ; Szegedy et al. , 2014 ; Goodfellow et al. , 2015 ) . Based on the primary AT frameworks like PGD-AT ( Madry et al. , 2018 ) , many improvements have been proposed from ... | The paper provides an evaluation of different hyperparameter settings for adversarial training. Specifically, it evaluates combinations of warmup, early stopping, weight decay, batch size and other parameters on adversarially trained models. The paper states that its overarching goal is to ``investigate how the impleme... | SP:0e32b047c35f57579f4eb935720e6a4a61c33116 |
VA-RED$^2$: Video Adaptive Redundancy Reduction | 1 INTRODUCTION . Large computationally expensive models based on 2D/3D convolutional neural networks ( CNNs ) are widely used in video understanding ( Tran et al. , 2015 ; Carreira & Zisserman , 2017 ; Tran et al. , 2018 ) . Thus , increasing computational efficiency is highly sought after ( Feichtenhofer , 2020 ; Zhou... | The paper presents a framework to reduce internal redundancy in the video recognition model. To do so, given the input frames, the framework predicts two scaling factors to conduct temporal and channel dimension reduction. The remaining part is reconstructed by cheap operations. The authors show that the framework achi... | SP:4d41be9a2f6e949a140b7a81dd85cadaabba63ef |
Parameter-Based Value Functions | 1 INTRODUCTION . Value functions are central to Reinforcement Learning ( RL ) . For a given policy , they estimate the value of being in a specific state ( or of choosing a particular action in a given state ) . Many RL breakthroughs were achieved through improved estimates of such values , which can be used to find op... | On page 2, in the background section: the discounted state distribution, what you wrote is not a distribution (doesn't sum to 1). In order to define this $d^{\pi_\theta}$ properly, you can multiply everything by $1-\gamma$. The interpretation is that you "reset" in your initial distribution $\mu_0$ with probability $1 ... | SP:7757f1f1066f31276dcbc93ad684ee84d925206a |
VideoGen: Generative Modeling of Videos using VQ-VAE and Transformers | 1 INTRODUCTION . Deep generative models of multiple types ( Goodfellow et al. , 2014 ; van den Oord et al. , 2016b ; Dinh et al. , 2016 ) have seen incredible progress in the last few years on multiple modalities including natural images ( van den Oord et al. , 2016c ; Zhang et al. , 2019 ; Brock et al. , 2018 ; Kingma... | This paper proposes a generative model to synthesize videos using VQ-VAEs. The scheme works in latent space by using embeddings for video sequences learnt by the VQ-VAE. For inference, an autoregressive transformer prior for video sequences is learnt, which upon sampling from and sending to the VQ-VAE decoder, generate... | SP:01597fbcad0e467ed94efcdfde93a565cb3a763e |
How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers | 1 INTRODUCTION . Due to the enormous data size and non-convexity , stochastic optimization algorithms have become widely used in training deep neural networks . In addition to Stochastic Gradient Descent ( SGD ) ( Robbins & Monro , 1951 ) , many variations such as Adagrad ( Duchi et al. , 2011 ) and Adam ( Kingma & Ba ... | This paper studies the topic of evaluating the performance of optimizers for neural networks. The paper makes the argument that existing evaluation procedures either over emphasize the finding of optimal hyperparameters or under-evaluate the performance of an algorithm by randomly sampling hyperparameters. This paper's... | SP:2376a19af5be2a66ce8cf04713ab41c972f48382 |
Pointwise Binary Classification with Pairwise Confidence Comparisons | 1 INTRODUCTION . Traditional supervised learning techniques have achieved great advances , while they are demanding for precisely labeled data . In many real-world scenarios , it may be too difficult to collect such data . To alleviate this issue , a large number of weakly supervised learning problems ( Zhou , 2018 ) h... | The paper develops a method to learn a binary classifier based only on pairwise comparison data. For example, the classifier learns to classify pictures of people as "adult" versus "child" based on pairwise comparisons of the form "person C is older than person X". The authors derive their method based on an empirical ... | SP:8ff9e46f3d6f0c6d74158383600839bdd97478af |
Practical Marginalized Importance Sampling with the Successor Representation | 1 INTRODUCTION . Off-policy evaluation ( OPE ) is a reinforcement learning ( RL ) task where the aim is to measure the performance of a target policy from data collected by a separate behavior policy ( Sutton & Barto , 1998 ) . As it can often be difficult or costly to obtain new data , OPE offers an avenue for re-usin... | The paper proposes an approach to employ successor representation combined with marginalized importance sampling. The basic idea exploited in the paper consists of expressing the occupancies in terms of the successor representation and to model it via a linear combination of some features. This allows handling, althoug... | SP:0685dd85f87da44ee57de28dd64c6c06181cdc65 |
Tight Second-Order Certificates for Randomized Smoothing | 1 INTRODUCTION . A topic of much recent interest in machine learning has been the design of deep classifiers with provable robustness guarantees . In particular , for an m-class classifier h : Rd → [ m ] , the L2 certification problem for an input x is to find a radius ρ such that , for all δ with ‖δ‖2 < ρ , h ( x ) = ... | This paper presents a randomized second-order smoothing certificate for providing robustness guarantees against adversarial attacks. By additionally using the gradient estimation of smoothed classifier, the proposed method has been shown to outperform the existing randomized smoothing certificate in practice. A variant... | SP:98492c9032ac3381f5897bc6f17fd0f136546999 |
Beyond Trivial Counterfactual Generations with Diverse Valuable Explanations | 1 INTRODUCTION . Consider a face authentication system for unlocking a device . In case of non-authentications ( possible false-negative predictions ) , this system could provide generic advices to its user such as “ face the camera ” or “ remove any face occlusions ” . However , these may not explain the reason for th... | The authors propose interpreting the decision of a black-box (BB) image classifier using diverse counterfactual explanations. The proposed model consists of a pre-trained β-TCVAE, which learns to extract a disentangled latent representation for the input image. To generate explanations for a given image, the model opti... | SP:4590c3a3d2a389f0d09fb308793c06855ac02fea |
Regression from Upper One-side Labeled Data | 1 INTRODUCTION . This paper addresses a scenario in which a regression function is learned for label sensor values that are the results of sensing the magnitude of some phenomenon . A lower sensor value means not only a relatively lower magnitude than a higher value but also a missing or incomplete observation of a mon... | This paper considers a regression setting in which the missing values are observed with lower values than the true values. Authors provided appealing application for this problem setting. They rewrote the risk and provided an unbiased gradient estimator. However, there is a gap between the estimator and the actual impl... | SP:366b8c3549160787f24e8e585953ed99ecdb0aa2 |
On the Impossibility of Global Convergence in Multi-Loss Optimization | 1 INTRODUCTION . Problem Setting . As multi-agent architectures proliferate in machine learning , it is becoming increasingly important to understand the dynamics of gradient-based methods when optimizing multiple interacting goals , otherwise known as differentiable games . This framework encompasses GANs ( Goodfellow... | 1. For Theorem 1, as the reviewer understands it, for an optimization problem whose only critical point is a strict maxima, it only has four outcomes, which are listed in the theorem. The result seems quite intuitive and provides very limited understanding for the problem. Please list other possible outcomes for the ge... | SP:07e927ae4286e3e227bf1c8ed5d17669ee871d96 |
Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels | 1 INTRODUCTION . Neural architecture search ( NAS ) aims to automate the design of good neural network architectures for a given task and dataset . Although different NAS strategies have led to state-of-the-art neural architectures , outperforming human experts ’ design on a variety of tasks ( Real et al. , 2017 ; Zoph... | The authors propose a new neural architecture search algorithm combining Bayesian optimization with the expressive and popular Weisfeiler-Lehman (WL) Graph Kernel. One advantage of using WL is the interpretable results that stem from the nature of how the kernel is computed, namely a propagation scheme through the grap... | SP:bac034cc8f02b43a03e24f0a8d327c4b68afed09 |
Adversarial Environment Generation for Learning to Navigate the Web | 1 INTRODUCTION . Autonomous web navigation agents that complete tedious , digital tasks , such a booking a flight or filling out forms , have a potential to significantly improve user experience and systems ’ accessibility . The agents could enable a user to issue requests such as , “ Buy me a plane ticket to Los Angel... | This paper improves upon existing approaches for learning to fill forms on the web automatically. The main idea is to train an adversary to generate a curriculum of environments to train an agent to learn to fill forms on the web. Training such an adversary can be challenging since the adversary may prove to be too str... | SP:90e01288266255a58201a01f06dd8fcc4cac4034 |
Uncertainty-Based Adaptive Learning for Reading Comprehension | 1 INTRODUCTION . The goal of machine reading comprehension ( MRC ) is to train an AI model which is able to understand natural language text ( e.g . a passage ) , and answer questions related to it ( Hirschman et al. , 1999 ) ; see Figure 1 for an example . MRC has been one of the most important problems in natural lan... | This paper proposes to apply uncertainty-based measures to guide the collection of training samples for reading comprehension. The paper describes a relatively simple metric to estimate model uncertainty of unlabeled examples, and develops an algorithm to sample examples that exhibit least model certainty. They describ... | SP:deb175b73241e3a04c2d2887934889508db4e39e |
Expressive Power of Invariant and Equivariant Graph Neural Networks | 1 INTRODUCTION . Graph Neural Networks ( GNN ) are designed to deal with graph structured data . Since a graph is not changed by permutation of its nodes , GNNs should be either invariant if they return a result that must not depend on the representation of the input ( typically when building a graph embedding ) or equ... | The authors prove several statements about the expressiveness of different classes of graph neural nets (GNNs): conventional message passing networks, linear GNNs (LGNN) and “folklore GNNs” (FGNN). The novel theoretical contributions include analysis of expressiveness of FGNNs that use tensors of arbitrary order in ter... | SP:53fbf29aa3f60001c2fc4f1a9bb797ebc9ceb986 |
SpreadsheetCoder: Formula Prediction from Semi-structured Context | 1 INTRODUCTION . Spreadsheets are ubiquitous for data storage , with hundreds of millions of users . Support for helping users write formulas in spreadsheets is a powerful feature for data analysis . Although spreadsheet formula languages are relatively simpler than general-purpose programming languages for data manipu... | This paper presents an interesting formulation for spreadsheet formula synthesis. Instead of taking the input output pairs as input, as is done in the programming by example (PBE) approaches, the proposed approach takes the semi-structured tabular context as input for predicting a formula for the target cell. A neural ... | SP:8ec8daeab04e7a3d11e9098a910df23c2d6665d1 |
Skinning a Parameterization of Three-Dimensional Space for Neural Network Cloth | 1 INTRODUCTION . Cloth is particularly challenging for neural networks to model due to the complex physical processes that govern how cloth deforms . In physical simulation , cloth deformation is typically modeled via a partial differential equation that is discretized with finite element models ranging in complexity f... | This paper proposes to model 3D cloth by embedding it into kinematically deforming skinned mesh (KDSM)[1], a tetrahedral mesh that parametrizes the volumetric region around the underlying body. A KDSM can be created and deformed using a variety of skinning and simulation techniques introduced in [1]. This paper extends... | SP:6c1d4e09a17d1a6abe209ab96356b837dbfbd710 |
Maximum Categorical Cross Entropy (MCCE): A noise-robust alternative loss function to mitigate racial bias in Convolutional Neural Networks (CNNs) by reducing overfitting | 1 INTRODUCTION . Convolutional Neural Networks ( CNNs ) offer state-of-the-art results in computer vision tasks He et al . ( 2016 ) ; Szegedy et al . ( 2015 ) ; Simonyan & Zisserman ( 2014 ) but are susceptible to inherent noises in the input training data preempting overfitting on the input data during information pro... | 1.The authors propose an extension of the CE loss to reduce classification bias that occurs in present methods and datasets. They calculate Maximum Entropy (ME) for images on the entire training dataset and then calculate the reconstruction loss between this and the ME for convolutional kernels during training. Their e... | SP:1480f9299a4918309d9d2b0f658fb0f863921387 |
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning | 1 INTRODUCTION TextWorld Embodied Welcome ! You are in the middle of the room . Looking around you , you see a diningtable , a stove , a microwave , and a cabinet . Your task is to : Put a pan on the diningtable . > goto the cabinet You arrive at the cabinet . The cabinet is closed . > open the cabinet The cabinet is e... | The paper presents a new interactive environment which is both text-based and contains visual simulation which are aligned. The authors also propose a first agent architecture which uses the visual observations as well as the text-based (named BUTLER). The authors tested the generalization capabilities of the proposed ... | SP:c6868fac7481cb241d9c5735f9184de9be9b72aa |
Detection Booster Training: A detection booster training method for improving the accuracy of classifiers. | Deep learning models owe their success at large , to the availability of a large1 amount of annotated data . They try to extract features from the data that contain2 useful information needed to improve their performance on target applications.3 Most works focus on directly optimizing the target loss functions to impro... | This paper proposes a training method for classification, with the goal of training with less data. The proposal is to train an auxiliary classifier at the same time. The auxiliary classifier and the main classifier share the early layers. The auxiliary classifier is a binary classifier that discriminates training data... | SP:4f7eaeae0559362f0caf13406b20914c120de74b |
Prediction and generalisation over directed actions by grid cells | 1 INTRODUCTION . A `` cognitive map '' encodes relations between objects and supports flexible planning ( Tolman [ 40 ] ) , with hippocampal place cells and entorhinal cortical grid cells thought to instantiate such a map ( O ’ Keefe and Dostrovsky [ 32 ] ; Hafting et al . [ 20 ] ) . Each place cell fires when the anim... | The authors propose as extension of the successor-representation approach to Grid cells. The paper shows that this model can generate several experimentally observed properties of grid cells, and can be used in navigation of novel/mutable environments. Overall, the work should be of interest to any ICLR attendees who e... | SP:27aca0420a1a3fa6cc3fdcef19d0ffcc02345a3c |
Zero-shot Fairness with Invisible Demographics | 1 INTRODUCTION . Machine learning is already involved in decision-making processes that affect peoples ’ lives such as in screening job candidates ( Raghavan et al. , 2020 ) and in pricing credit ( Hurley & Adebayo , 2017 ) . Efficiency can be improved , costs can be reduced , and personalization of services and produc... | This paper tackles a fair classification problem with an invisible demographic, a situation where the records who have some specific target labels and sensitive attributes are missing. In this setting, the authors introduce a disentangled representation learning framework to make the resultant classifier fair by taking... | SP:6c57ec1533acf8cfcc2f8d9cdc8fe4d7acf9f77f |
Uncertainty in Gradient Boosting via Ensembles | 1 INTRODUCTION . Gradient boosting ( Friedman , 2001 ) is a widely used machine learning algorithm that achieves stateof-the-art results on tasks containing heterogeneous features , complex dependencies , and noisy data : web search , recommendation systems , weather forecasting , and many others ( Burges , 2010 ; Caru... | This paper studied the uncertainty estimation in GBDT method. The authors described 3 methods to estimate the uncertainty. With SGB, the estimation is achieved by training multiple models using data sub-samples. With SGLB, the authors derived that we can estimate the posterior distribution of the model parameters. Thes... | SP:4a6172aeb95ae800b1a1e86f15a61c6b82cca9d9 |
Mutual Calibration between Explicit and Implicit Deep Generative Models | 1 INTRODUCTION . Deep generative model , as a powerful unsupervised framework for learning the distribution of highdimensional multi-modal data , has been extensively studied in recent literature . Typically , there are two types of generative models : explicit and implicit ( Goodfellow et al. , 2014 ) . Explicit model... | In this paper, the task is to train an implicit and an explicit model simultaneously via GAN setting and a new regularizer called "stein bridge", which is constructed from the kernel Stein discrepancy between the implicit and explicit models. The idea of adding such regularization, with the notion of mutual regularizat... | SP:1fd72534803649141dce71dd19d3998faf96f625 |
The Advantage Regret-Matching Actor-Critic | 1 Introduction . The notion of regret is a key concept in the design of many decision-making algorithms . Regret minimization drives most bandit algorithms , is often used as a metric for performance of reinforcement learning ( RL ) algorithms , and for learning in games ( 3 ) . When used in algorithm design , the comm... | This paper considers the problem of counterfactual regret minimization and proposes an algorithm that does not use the importance sampling procedure. The claim is that this helps in reducing the variance usually introduced by the IS procedure. They propose a new algorithm that uses the previously used policies as a buf... | SP:057cf13c9fd038dc102253838b888580acc6e2b6 |
Identifying Physical Law of Hamiltonian Systems via Meta-Learning | 1 INTRODUCTION . Hamiltonian mechanics , a reformulation of Newtonian mechanics , can be used to describe classical systems by focusing on modeling continuous-time evolution of system dynamics with a conservative quantity called Hamiltonian ( Goldstein et al. , 2002 ) . Interestingly , the formalism of the Hamiltonian ... | The paper presents a meta-learning method for learning Hamiltonian dynamic systems from data. More specifically, the novelty is incorporating Hamiltonian Neural Networks (HNNs) within known meta-learning methods (MAML and ANIL) in order to model new dynamical systems (with previously known structures but unknown parame... | SP:87af788d5bd4c486de01969a93d8b49a9f494da1 |
Transferring Inductive Biases through Knowledge Distillation | 1 INTRODUCTION . Inductive biases are the characteristics of learning algorithms that influence their generalization behavior , independent of data . They are one of the main driving forces to push learning algorithms toward particular solutions ( Mitchell , 1980 ) . Having the right inductive biases is especially impo... | The paper investigates the oft-overlooked aspect of knowledge distillation (KD) -- why it works. The paper highlights the ability of KD for transferring not just the soft labels, but the inductive bias (assumptions inherent in the method, e.g. LSTM's notion of sequentiality, and CNN's translational invariance/equivaria... | SP:49e648763ccbfd619a4ee8286a36d85096176cc6 |
MCM-aware Twin-least-square GAN for Hyperspectral Anomaly Detection | 1 INTRODUCTION . Hyperspectral image ( HSI ) appears as a three-dimensional ( 3D ) data cube , two dimensions of which show the spatial information of materials , and the other reveals hundreds of contiguous bands to perceive each scene ( Yokoya et al. , 2012 ) . Among a wealth of HSIs interpretation techniques in prac... | In this paper, the authors proposed the MTGAN framework, a GAN-based approach to the task of anomaly detection in hyperspectral images. The main idea behind this work is to exploit twin-least-square loss to perform background modeling in feature and image domains to alleviate the gradient vanishing problem of the previ... | SP:a2081fef3126e03544d6c62d6b4b0e15f79d1cc6 |
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction | 1 INTRODUCTION . The use of function approximators for dynamical system modeling has become increasingly common . This has proven quite effective when a substantial amount of real data is available relative to the complexity of the model being learned ( Chua et al. , 2018 ; Janner et al. , 2019 ; Chen et al. , 1990 ) .... | The paper proposes a neural-network architecture for modeling dynamical systems that incorporates prior domain knowledge of the system's dynamics. More specifically, the main contributions are the mechanisms for incorporating such knowledge, in terms of fully or partially known structure (differential equations) of the... | SP:f0f3694b84631cb0ebb5cd4c3510f6279526a28c |
Learned Threshold Pruning | 1 INTRODUCTION . Deep neural networks ( DNNs ) have provided state-of-the-art solutions for several challenging tasks in many domains such as computer vision , natural language understanding , and speech processing . With the increasing demand for deploying DNNs on resource-constrained edge devices , it has become even... | The paper introduces a new type of soft threshold operator in conjunction with appropriate weight regularization that can be used in the context of neural network pruning to obtain sparse, performant networks from pre-trained, dense networks. The main idea is to replace the Heaviside step function that occurs in "hard ... | SP:b7e2096e6070edf0d080bcf5113e469563f98dc2 |
Visual Imitation with Reinforcement Learning using Recurrent Siamese Networks | 1 INTRODUCTION . Imitation learning and Reinforcement Learning ( RL ) often intersect when the goal is to imitate with incomplete information , for example , when imitating from motion capture data ( mocap ) or video . In this case , the agent needs to search for actions that will result in observations similar to the ... | This paper studies the problem of visual imitation learning: given a video of an expert demonstration, take actions to reproduce that same behavior. The proposed method learns a distance metric on videos and uses that distance metric as a reward function for RL. Experiments show that this method does recover reasonable... | SP:6cc0e3b4b6385061150d8e36bcbc022069b475ba |
A Surgery of the Neural Architecture Evaluators | Neural architecture search ( NAS ) has recently received extensive attention due to its effectiveness in automatically designing effective neural architectures . A major challenge in NAS is to conduct a fast and accurate evaluation ( i.e. , performance estimation ) of neural architectures . Commonly used fast architect... | The paper assesses two different approaches to speed up the evaluations of neural network architectures for neural architecture search (NAS). The first one is weight sharing, which trains a supernetwork that contains all possible architecture of the search space. The performance of single architectures can be then appr... | SP:cb3cb0e206f4c3560538906a34265fcc95ca950f |
Differentiable Approximations for Multi-resource Spatial Coverage Problems | 1 INTRODUCTION . Allocation of multiple resources for efficient spatial coverage is an important component of many practical single-agent and multi-agent systems , for e.g. , robotic surveillance , mobile sensor networks and security game modeling . Surveillance tasks generally involve a single agent assigning resource... | This paper studies the coverage game where agents allocate their resources to target spaces to maximize their coverage, and the goal of this paper is to (approximately) compute the Nash Equilibrium. The proposed method simulates the game by iteratively updating the best response, and the main contribution is an algorit... | SP:c98c40dda3d811ff76816182962ccbed03693eb4 |
Balancing Constraints and Rewards with Meta-Gradient D4PG | 1 INTRODUCTION . Reinforcement Learning ( RL ) algorithms typically try to maximize an expected return objective ( Sutton & Barto , 2018 ) . This approach has led to numerous successes in a variety of domains which include board-games ( Silver et al. , 2017 ) , computer games ( Mnih et al. , 2015 ; Tessler et al. , 201... | The paper focuses on soft-constrained RL techniques and proposes a meta-gradient approach for the same. It first extends the RCPO (Tessler et al) algorithm using the methodology of DDPG (Lillicarp et al) to propose an off-policy version of RCPO (called RC-D4PG). The main contribution of the work is the proposal of two... | SP:5d4084ca5f3570dfd854aa399f2778e0b649f862 |
Out-of-Distribution Generalization Analysis via Influence Function | 1 INTRODUCTION . Most machine learning systems assume both training and test data are independently and identically distributed , which does not always hold in practice ( Bengio et al . ( 2019 ) ) . Consequently , its performance is often greatly degraded when the test data is from a different domain ( distribution ) .... | The authors study the problem of out-of-distribution (OoD) generalization. The key question authors seek to answer is when given access to data from multiple training environments, can one only rely on test accuracy? or does one have to rely on some new measures to estimate the out-of-distribution performance of the mo... | SP:2dcfc5ac82356d824b2c4892372c73e678924caa |
Active Contrastive Learning of Audio-Visual Video Representations | 1 INTRODUCTION . Contrastive learning of audio and visual representations has delivered impressive results on various downstream scenarios ( Oord et al. , 2018 ; Hénaff et al. , 2019 ; Schneider et al. , 2019 ; Chen et al. , 2020 ) . This self-supervised training process can be understood as building a dynamic diction... | In this paper, the authors propose a cross-modal (audio-video) self-supervised representation learning method with a contrastive learning framework. To overcome the high redundancy in the negative samples, they propose an active negative sampling method. They use a gradient with respect to the pseudo label to measure t... | SP:df0e5190360b8dd9f9ddc35a6f7c57834f483fbb |
Automatic Music Production Using Generative Adversarial Networks | 1 INTRODUCTION . The development of home music production has brought significant innovations into the process of pop music composition . Software like Pro Tools , Cubase , and Logic – as well as MIDI-based technologies and digital instruments – provide a wide set of tools to manipulate recordings and simplify the comp... | In the paper, the authors adapt CycleGAN, a well-known model for unpaired image-to-image translation, to automatic music arrangement by treating MFCCs extracted from audio recordings as images. Also, the authors propose a novel evaluation metric, which learns how to rate generated audio from the ratings of (some) music... | SP:dc90daee29d8bea60a4033e06a9e36e660597ea2 |
Debiased Graph Neural Networks with Agnostic Label Selection Bias | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) are powerful deep learning algorithms on graphs with various applications ( Scarselli et al. , 2008 ; Kipf & Welling , 2016 ; Veličković et al. , 2017 ; Hamilton et al. , 2017 ) . Existing GNNs mainly learn a node embedding through aggregating the features from its neig... | This paper presents a novel method to remove the selection bias of graph data, which is neglected by previous methods. Specifically, the authors suspect that all variables observed by GNNs can be decomposed into two parts, stable variables and unstable variables. Then, DGNN, a differentiable decorrelation regularizati... | SP:bbada593ac1fae021d96b76f47f62772da50bdce |
Latent Programmer: Discrete Latent Codes for Program Synthesis | 1 INTRODUCTION . Our focus in this paper is program synthesis , one of the longstanding grand challenges of artificial intelligence research ( Manna & Waldinger , 1971 ; Summers , 1977 ) . The objective of program synthesis is to automatically write a program given a specification of its intended behavior , such as a n... | This paper proposes a two-level hierarchical program synthesizer, Latent Programmer, which first predicts a sequence of latent codes from given input-output examples, and then decodes the latent codes into a program. The sequence of latent codes can be viewed as a high-level synthesis plan, guiding the subsequent low-... | SP:e42647e1efc0582b03c3fe8f1bb8c73d6403a97c |
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