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A self-explanatory method for the black box problem on discrimination part of CNN | 1 INTRODUCTION . Convolution neural network ( CNN ) has surpassed human abilities in some specific tasks such as computer game and computer vision etc . However , they are considered difficult to understand and explain ( Brandon , 2017 ) , which leads to many problems in aspects of privacy leaking , reliability and rob... | This paper proposes an interesting framework for training interpretable CNNs, similar to distillation methods. The authors propose a probabilistic model to approximate CNN predictions (specifically the discriminatory part i.e. fully connected network, and a procedure for training CNN+ DCLM as a game. Results show inter... | SP:59c5d78884feb7f21eb812b69d71827770f6fe39 |
ANOCE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning | 1 INTRODUCTION . In the era of causal revolution , identifying the causal effect of an exposure on the outcome of interest is an important problem in many areas , such as epidemics ( Hernán , 2004 ) , medicine ( Hernán et al. , 2000 ) , education ( Card , 1999 ) , and economics ( Panizza & Presbitero , 2014 ) . Under a... | The paper proposes a framework for the analysis of causal inference. Its main contribution is to decompose the indirect effect by teasing out the causal contribution of a set of mediators. In a series of experiments with simulated data the authors show that the proposed method, ANOCE, outperforms other comparison partn... | SP:a8c2db9bf91b517ea4317c85cab34a53206f7090 |
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets? | 1 INTRODUCTION . Deep convolutional nets ( “ ConvNets ” ) are at the center of the deep learning revolution ( Krizhevsky et al. , 2012 ; He et al. , 2016 ; Huang et al. , 2017 ) . For many tasks , especially in vision , convolutional architectures perform significantly better their fully-connected ( “ FC ” ) counterpar... | The paper presents an interesting analysis of MLP and convnets, where they show a gap between the number of required training examples to generalize well. They show that due to orthogonality invariance in MLP training, then more examples are required compare to convnet, where one example is needed. This approach, which... | SP:75ea5f45677f0daa8a50a6e74737cfd7afc9f817 |
LAYER SPARSITY IN NEURAL NETWORKS | Sparsity has become popular in machine learning , because it can save computational resources , facilitate interpretations , and prevent overfitting . In this paper , we discuss sparsity in the framework of neural networks . In particular , we formulate a new notion of sparsity that concerns the networks ’ layers and ,... | The paper proposes a regularizer enforcing a novel form of sparsity that authors call "layer sparsity". Under certain conditions on layer weights, two consecutive layers in a deep neural network (with certain nonlinear activation functions) can be represented exactly as a single layer. The authors proposed a regularize... | SP:9fd718d9cc2318a1d6306c22a45b4e90ace9fd80 |
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification | 1 INTRODUCTION . While deep neural networks ( DNNs ) achieve state-of-the-art performance in many fields , they are known to require large quantities of reasonably high-quality labeled data , which can often be expensive to obtain . As such , transfer learning has emerged as a powerful methodology that can significantl... | This paper tries to investigate and understand if and how adversarial training helps the models trained on the source domain transfer easier and faster to target domains. With extensive different configurations (such as fine-tuning strategies) in experiments, the authors show that robust models transfer better than nat... | SP:c0072c347d78252701da4d55192f607131d97adf |
Optimizing Loss Functions Through Multivariate Taylor Polynomial Parameterization | 1 INTRODUCTION . As deep learning systems have become more complex , their architectures and hyperparameters have become increasingly difficult and time-consuming to optimize by hand . In fact , many good designs may be overlooked by humans with prior biases . Therefore , automating this process , known as metalearning... | This paper proposed a method, called TaylorGLO, to learn the loss functions, for training deep neural network, by meta-learning. Specifically, the authors proposed to parameterize the loss function with multivariate Taylor polynomial, and then learn the parameters in the polynomial using evolutionary algorithm within t... | SP:ffc8e46a5dbbcd0906458c0e302190997dfe8b5e |
Efficient Long-Range Convolutions for Point Clouds | 1 INTRODUCTION . Point-cloud representations provide detailed information of objects and environments . The development of novel acquisition techniques , such as laser scanning , digital photogrammetry , light detection and ranging ( LIDAR ) , 3D scanners , structure-from-motion ( SFM ) , among others , has increased t... | The paper proposes an efficient long-range convolution method for point clouds by using the non-uniform Fourier transform. The long-range convolutional (LRC)-layer mollifies the point cloud to an adequately sized regular grid, computes its Fourier transform, multiplies the results by a set of trainable Fourier multipli... | SP:6a0a4a33a8023f2bed39d64f92a054e494ecdb74 |
Counterfactual Self-Training | Unlike traditional supervised learning , in many settings only partial feedback is available . We may only observe outcomes for the chosen actions , but not the counterfactual outcomes associated with other alternatives . Such settings encompass a wide variety of applications including pricing , online marketing and pr... | The paper proposes to use self-training to tackle the fundamental problem of causal inference where only one potential outcome is seen. The proposed self-training method is iterative: after training a model on the observational dataset, they run points with different actions (treatments) through the trained model and c... | SP:23db11b6d3d07a1820fd393c16e447f1716a17ca |
An Empirical Exploration of Open-Set Recognition via Lightweight Statistical Pipelines | 1 INTRODUCTION . Embodied perception and autonomy require systems to be self-aware and reliably know their unknowns . This requirement is often formulated as the open set recognition problem ( Scheirer et al. , 2012 ) , meaning that the system , e.g. , a K-way classification model , should recognize anomalous examples ... | of Paper: The main claim of the paper is that out of distribution (OOD) detection can be done by use of pre-training and appropriately deriving a feature space from SOTA activations via pooling, PCA based dimensionality reduction, L2 normalization. Classical methods such as GMMs, k-means etc. can then be used to esti... | SP:4b0b0b58ac822beb29097ed55dfe44128530d5ed |
ChemistryQA: A Complex Question Answering Dataset from Chemistry | 1 INTRODUCTION . Recent years have witnessed huge advances for the question answering ( QA ) task , and some AI agents even beat human beings . For example , IBM Watson won Jeopardy for answering questions which requires a broad range of knowledge ( Ferrucci , 2012 ) . Transformer-based neural models , e.g . XLNet ( Ya... | This paper proposes a new dataset based on textbook / classroom chemistry questions for complex knowledge retrieval and aggregation. The authors scrape several thousands questions from online repositories and add additional natural language annotations signifying the quantities to be solved for in each question, as wel... | SP:5fc35f794bdf1281225c24a5096547e75904a2d0 |
Sufficient and Disentangled Representation Learning | 1 INTRODUCTION . Representation learning is a fundamental problem in machine learning and artificial intelligence ( Bengio et al. , 2013 ) . Certain deep neural networks are capable of learning effective data representation automatically and achieve impressive prediction results . For example , convolutional neural net... | The authors present a new representation learning algorithm that trades off between a sufficiency condition (that is, the label should be independent of the input conditioned on the representation) and what they call a "disentangling" condition - that the representation vectors should be independent of one another and ... | SP:804fada5af8ccfe842706ac812bcc294956b4fb4 |
Neural Partial Differential Equations with Functional Convolution | 1 INTRODUCTION ( 1+ ) Problem definition We aim to devise a learning paradigm to solve the inverse PDE identification problem . By observing a small data set in the PDE ’ s solution space with an unknown form of equations , we want to generate an effective neural representation that can precisely reconstruct the hidden... | Post-discussion update: The authors only partially adressed my concerns in their rebuttal. The paper suffers from lack of comparisons: only 2 baselines are compared, and only on few systems. Crucially the new Navier-Stokes experiment lacks comparisons. The authors also couldn't respond to my questions about research co... | SP:bd4bc912bd62fdcf54adeb77330f6cfbe4bb0352 |
A Chain Graph Interpretation of Real-World Neural Networks | 1 INTRODUCTION . During the last decade , deep learning ( Goodfellow et al. , 2016 ) , the study of neural networks ( NNs ) , has achieved ground-breaking results in diverse areas such as computer vision ( Krizhevsky et al. , 2012 ; He et al. , 2016 ; Long et al. , 2015 ; Chen et al. , 2018 ) , natural language process... | This paper tries to interpret neural networks with chain graphs that provides theoretical analysis on various neural network components. Furthermore, this chain graph interpretation has been used to propose a new approach (architecture), which is a partially collapsed feed-forward. A layered chain graph representation ... | SP:e7c149067b48a63680ae063c880c00a304309b90 |
Convex Regularization in Monte-Carlo Tree Search | 1 INTRODUCTION . Monte-Carlo Tree Search ( MCTS ) is a well-known algorithm to solve decision-making problems through the combination of Monte-Carlo planning with an incremental tree structure ( Coulom , 2006 ) . Although standard MCTS is only suitable for problems with discrete state and action spaces , recent advance... | The authors consider planning for Markov Decision Process. Precisely they study the benefit of convex regularization in Monte-Carlo Tree Search (MCTS). They generalize the E2W by xiao et al., 2019 by considering any strictly convex function as regularizer instead of the intial negative entropy. They provide a regret an... | SP:46354d6dca2faa7f4553f9a00059c86178ab87e2 |
On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes | 1 INTRODUCTION . While 3D surface representation has been a foundational topic of study in the computer graphics community for over four decades , recent developments in machine learning have highlighted the potential that neural networks can play as effective parameterizations of solid shapes . The success of neural a... | The paper proposes a weight-encoded neural implicit representation for 3D shapes. The idea is to encode every shape in the network weights of its own designated small MLP network, instead of trying to learn a latent space of shapes. This leads to a really compact shape representation based on signed distance fields tha... | SP:222cf20bdaa0a95c8dd13031acf16dd19ca3f318 |
Isometric Transformation Invariant and Equivariant Graph Convolutional Networks | 1 INTRODUCTION . Graph-structured data embedded in Euclidean spaces can be utilized in many different fields such as object detection , structural chemistry analysis , and physical simulations . Graph neural networks ( GNNs ) have been introduced to deal with such data . The crucial properties of GNNs include permutati... | The paper proposes a network that operates on features of graphs that are embedded in a d-dim Euclidean space. The paper considers equivariance to a group G that is the direct product of permutations of N points and Euclidean transformations. The features they consider are tensor products of the N-dimensional natural r... | SP:d4831b759e850c4a630024c55aa6ccd957d337e1 |
A Distributional Approach to Controlled Text Generation | 1 INTRODUCTION . Neural language models , such as GPT-2/3 ( Radford et al. , 2019 ; Brown et al. , 2020a ) , pretrained on huge amounts of text , have become pre-eminent in NLP , producing texts of unprecedented quality . In this paper , we are concerned with the problem of controlling a generic pretrained LM in order ... | The paper studies the controlled sequence generation problem based on pretrained language models, i.e., controlling a generic pretrained LM to satisfy certain constraints, e.g., removing certain biases in language models. Specifically, the paper proposes a distributional view and imposes constraints based on collective... | SP:41a9a0e893ccd973ebf57ca7f99b9b6f22e8d339 |
Self-training For Few-shot Transfer Across Extreme Task Differences | 1 INTRODUCTION . Despite progress in visual recognition , training recognition systems for new classes in novel domains requires thousands of labeled training images per class . For example , to train a recognition system for identifying crop types in satellite images , one would have to hire someone to go to the diffe... | Problem: The paper introduces the problem of few-shot transfer when there is an extreme difference between the base task and the target task. The usual few-shot learning setup considers a representation that is trained on a large amount of labeled data. This base representation is then fine-tuned for the target task (... | SP:0961e5b8ac98e0d66b599c7b91bd636a75d07b35 |
Augmentation-Interpolative AutoEncoders for Unsupervised Few-Shot Image Generation | 1 INTRODUCTION . Modern generative models can synthesize high-quality ( Karras et al. , 2019 ; Razavi et al. , 2019 ; Zhang et al. , 2018a ) , diverse ( Ghosh et al. , 2018 ; Mao et al. , 2019 ; Razavi et al. , 2019 ) , and highresolution ( Brock et al. , 2018 ; Karras et al. , 2017 ; 2019 ) images of any class , but o... | The paper investigates the generative model which generalizes to new domain with limited samples. Authors firstly explore the current hot generative models: VAEs and GANs, and experimentally find that both VAEs and GANs fail to learn a model which generalizes well to novel domain. Interestingly, AutoEncoders exhibi... | SP:144d436a6cbb52de49b6934f3cc4fca95e480647 |
Mapping the Timescale Organization of Neural Language Models | 1 INTRODUCTION . Language processing requires tracking information over multiple timescales . To be able to predict the final word “ timescales ” in the previous sentence , one must consider both the short-range context ( e.g . the adjective “ multiple ” ) and the long-range context ( e.g . the subject “ language proce... | This paper applies tools from neuroscience to understand how language models integrate across time. The basic approach is to present a phrase, preceded by two different context phrases: one that is natural (i.e. the phrase that actually preceded it in the corpus) and one that is randomly selected. The authors then meas... | SP:a7605f203e883bb5d782cd9e090cebff0cf504ef |
An Adversarial Attack via Feature Contributive Regions | 1 INTRODUCTION . The development of deep learning technology has promoted the successful application of deep neural networks ( DNNs ) in various fields , such as image classification ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2014 ) , computer vision ( He et al. , 2016 ; Taigman et al. , 2014 ) , natural langu... | This paper focuses on the problem of generating sparse l2-adversarial examples in a white-box and surrogate/transfer setting. The authors consider “local attacks” – perturbing on a limited number of pixels while achieving high attack success rate. The main contribution of this work is to define the region to perturb us... | SP:345a245503d9e3acaf695de66d73d9f4ff3eab83 |
Multi-Task Learning by a Top-Down Control Network | 1 INTRODUCTION . The goal of multi-task learning is to improve the learning efficiency and increase the prediction accuracy of multiple tasks learned and performed in a shared network . In recent years , several types of architectures have been proposed to combine multiple tasks training and evaluation . Most current s... | In this paper a novel top-down control network is introduced for multi-task learning. Different from the traditional bottom-up attention models, the authors introduce a top-down module to modify the activation of recognition network based on different tasks. Specifically，the proposed module consists of three identical ... | SP:652a231a924a97e438595264ea869986e40d45a7 |
Computing Preimages of Deep Neural Networks with Applications to Safety | To apply an algorithm in a sensitive domain it is important to understand the set of input values that result in specific decisions . Deep neural networks suffer from an inherent instability that makes this difficult : different outputs can arise from very similar inputs . We present a method to check that the decision... | Deep neural networks are known to be brittle, and can lead to dangerous consequences if left unverified. Forward reach set computation can be used as a basic primitive to verify properties of deep neural networks used in a robotic setting. There has been a rising interest in verifying larger neural networks used in saf... | SP:d00483a38437b6c706f04cc03b34cc593a3f7273 |
Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding | 1 INTRODUCTION . Variational autoencoder ( VAE ) ( Kingma & Welling , 2014 ) is one of the most successful generative models , estimating posterior parameters of latent variables for each input data . In VAE , the latent representation is obtained by maximizing an evidence lower bound ( ELBO ) . A number of studies ( H... | The paper builds on a branch of recent works that consider and analyse Variational autoencoders (VAE) from the view point of data compression. This started from Alemi et al. 2018, where the authors consider and analyse the mutual information between data and latent codes and culminates in Kato et al. 2020, where the au... | SP:c00c16048e11229025e209fc0e547af1471dae90 |
Universal Approximation Theorem for Equivariant Maps by Group CNNs | Group symmetry is inherent in a wide variety of data distributions . Data processing that preserves symmetry is described as an equivariant map and often effective in achieving high performance . Convolutional neural networks ( CNNs ) have been known as models with equivariance and shown to approximate equivariant maps... | This paper considers a certain generalization of convolutional neural networks and equivariant linear networks to the infinite dimensional case, while covering also the discrete case, and offers a universality result. In more detail, the paper first characterizes equivariant maps as the unique extensions of "generato... | SP:9f4c8080e3e3b45abdd1d906312bd1271670a805 |
THE EFFICACY OF L1 REGULARIZATION IN NEURAL NETWORKS | 1 INTRODUCTION . Neural networks have been successfully applied in modeling nonlinear regression functions in various domains of applications . A critical evaluation metric for a predictive learning model is to measure its statistical risk bound . For example , the L1 or L2 risks of typical parametric models such as li... | This paper studies the statistical risk bounds for two-layer neural networks with $L_1$-regularization. The authors consider two types of $L_1$-regularization: the $L_1$-regularization on output layer and the $L_1$-regularization on the input layer. For the $L_1$-regularization on output layer, the authors develop near... | SP:d8da07759331a59ef4062e5893eef1a8a8d2c589 |
Neural Learning of One-of-Many Solutions for Combinatorial Problems in Structured Output Spaces | 1 INTRODUCTION . Neural networks have become the de-facto standard for solving perceptual tasks over low level representations , such as pixels in an image or audio signals . Recent research has also explored their application for solving symbolic reasoning tasks , requiring higher level inferences , such as neural the... | The authors work in the domain of applying neural networks to combinatorial problems with structured output space, such as sudoku and n-queens. They notice how models currently performing well at this task encounter difficulties when there are multiple possible solutions. They formalize the task of learning any of mult... | SP:44e95502bce4ea4e27495a27aa1bf56e962ca6fd |
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features | 1 INTRODUCTION . Calibrated uncertainty is crucial for safety-critical decision making by neural networks ( NNs ) ( Amodei et al. , 2016 ) . Standard training methods of NNs yield point estimates that , even if they are highly accurate , can still be severely overconfident ( Guo et al. , 2017 ) . Approximate Bayesian m... | The authors consider the issue of overconfidence in ReLU NN and BNNs, particularly for data that are far (in Euclidean distance) from the training data. They address this by modeling the residual (to the NN) in the latent space with a GP. The kernel for this GP is derived as the limit of infinitely many ReLU-based rand... | SP:3665fd208fe6506f389defd4267ebc6ed5fefe98 |
Model-Free Counterfactual Credit Assignment | 1 INTRODUCTION . Reinforcement learning ( RL ) agents act in their environments and learn to achieve desirable outcomes by maximizing a reward signal . A key difficulty is the problem of credit assignment ( Minsky , 1961 ) , i.e . to understand the relation between actions and outcomes and to determine to what extent a... | The paper explores a new approach to credit assignment that complements existing work. It focuses on model-free approaches to credit assignment using hindsight information. In contrast to some prior work on this topic, e.g., (Harutyunyan et al. 2019), the paper does not rely explicitly on hand-crafted information, but ... | SP:bad1f2bea2a00f6edc474cd1e78c4011525348e5 |
Towards Finding Longer Proofs | We present a reinforcement learning ( RL ) based guidance system for automated theorem proving geared towards Finding Longer Proofs ( FLoP ) . FLoP focuses on generalizing from short proofs to longer ones of similar structure . To achieve that , FLoP uses state-of-the-art RL approaches that were previously not applied ... | This work introduces a method for learning to prove theorems which can leverage prior proving experience in order to discover very long proofs. At its core it works by inputting a corpus of training problems (which can also be annotated with solutions i.e. proofs), training a policy to solve these training problems by ... | SP:2093f2f9d4bf15531dd76e02f8d36cddf6961352 |
UserBERT: Self-supervised User Representation Learning | 1 INTRODUCTION . The choice of data representations , i.e. , how to create meaningful features , imposes tremendous impact on the performance of machine learning applications ( Bengio et al. , 2013 ) . Therefore , data processing and feature engineering have been the decisive steps in developing machine learning models... | The paper presents an approach to learning user representations based on activity patterns on e-commerce websites and a user profile. The method turns activity patterns into a sequence of discrete tokens based on the action type and attributes that correspond to a certain action. A self-supervised transformer is traine... | SP:6a4302d604b03b5c7ce0c30450808705348d4e9c |
More Side Information, Better Pruning: Shared-Label Classification as a Case Study | 1 INTRODUCTION . Pruning Neural networks , the task of compressing a network by removing parameters , has been an important subject both for practical deployment and theoretical research . Some pruning algorithms have focused on manipulating pre-trained models , ( Mozer & Smolensky , 1989 ; LeCun et al. , 1990 ; Reed ,... | The authors study how to improve the prediction and pruning performance with additional information generated by labels in the shared-label classification problem. As a starting point, the authors consider a simple scenario where side information can be extracted from the same labeled batch. To train the neural network... | SP:4c72c81f76d16b52fbef2e1804d913d0fbd61b2c |
Bridging the Imitation Gap by Adaptive Insubordination | 1 Introduction . Imitation learning ( IL ) can be remarkably successful in settings where reinforcement learning ( RL ) struggles . For instance , IL has been shown to succeed in complex tasks with sparse rewards [ 8 , 47 , 44 ] , and when the observations are high-dimensional , e.g. , in visual 3D environments [ 31 , ... | This paper identifies a problem in imitation learning when an expert has access to privileged information that is not available to the learner. When a decision has to be made based on the privileged information, the learner tends to choose average or uniformly random actions of the expert due to the lack of important i... | SP:32040641c0cbdc186c2db90470bec7856c89cb38 |
Representation learning for improved interpretability and classification accuracy of clinical factors from EEG | 1 INTRODUCTION . Mental health disorders make up one of the main causes of the overall disease burden worldwide ( Vos et al. , 2013 ) , with depression ( e.g. , Major Depressive Disorder , MDD ) believed to be the second leading cause of disability ( Lozano et al. , 2013 ; Whiteford et al. , 2013 ) , and around 17 % of... | The authors propose a beta-VAE network to learn EEG representation as biomarkers for diagnosing depression from EEG data. They show improved performance compared to an off-the shelf linear classifier. The paper is well-written but lacks a description of related work in the field and also a detailed analysis of the resu... | SP:40b48e4e0455356fe1dd476f4515a1811af9d0bf |
MoPro: Webly Supervised Learning with Momentum Prototypes | 1 INTRODUCTION . Large-scale datasets with human-annotated labels have revolutionized computer vision . Supervised pretraining on ImageNet ( Deng et al. , 2009 ) has been the de facto formula of success for almost all state-of-the-art visual perception models . However , it is extremely labor intensive to manually anno... | To train a model with a noisy weakly supervised training set, this paper proposed a momentum prototypes method for label noise correction and OOD sample removal. Noise correction is done by a heuristic rule, that if the prediction is confident enough or the prediction on original label is higher than uniform probabilit... | SP:c1089bb29c0bac6e75d163ef843098a1d8c008da |
Importance and Coherence: Methods for Evaluating Modularity in Neural Networks | 1 INTRODUCTION . Deep neural networks have achieved state-of-the-art performance in a variety of applications , but this success contrasts with the challenge of making them more intelligible . As these systems become more advanced and widely-used , there are a number of reasons we may need to understand them more effec... | The authors identify putative clusters of units/neurons in deep networks using spectral clustering on a graph defined by synaptic weights. The authors then argue that these structurally defined clusters of neurons have similar *functional representations*. Finding interpretable relationships between weight matrices and... | SP:98f5d14f7167266f06fd7e2a30c93a20905e7a6c |
Model agnostic meta-learning on trees | 1 INTRODUCTION . Deep learning models require a large amount of data in order to perform well when trained from scratch . When data is scarce for a given task , we can transfer the knowledge gained in a source task to quickly learn a target task , if the two tasks are related . The field of Multi-task learning studies ... | The submission proposes a meta-learning algorithm attuned to the hierarchical structure of a dataset of tasks. Hierarchy is enforced in a set of synthetically-generated regression tasks via the data-sampling procedure, which is modified from the task-sampling procedure of [1] to include an additional source of randomne... | SP:e5719e04d242e5f1b4646cf4bfe43b8aeaa950ad |
Multi-resolution modeling of a discrete stochastic process identifies causes of cancer | 1 INTRODUCTION . Numerous domains involve modeling highly non-stationary discrete-time and integer-valued stochastic processes where event counts vary dramatically over time or space . An important open problem of this nature in biology is understanding the stochastic process by which mutations arise across the genome ... | The authors present the split Poisson Gamma (SPG) distribution, an extension of the Poisson-Gamma distribution, to model a discrete non-stationary stochastic process. SPG has an analytical posterior allowing accurate prediction after the model parameters have been inferred a single time. The authors apply the SPG to mo... | SP:18a31dc5f6d12d1d30a3d1e4698523336cd67eb1 |
Towards Robust Graph Neural Networks against Label Noise | 1 INTRODUCTION . Deep Neural Networks ( DNNs ) have achieved great success in various domains , but the necessity of collecting large amount of samples with high-quality labels is both expensive and time-consuming . To address this problem , cheaper alternatives have emerged . For example , the onerous labeling process... | The paper proposes a robust training algorithm for graph neural networks against label noise. The authors assume the labeled nodes are divided into two parts, clean part without noise and train part with some noise. The proposed method contains two parts. Firstly, it leverages label propagation (LP) trained on the clea... | SP:9deac038d6aedcb20ea92ca2d40863e859515d9a |
Recycling sub-optimial Hyperparameter Optimization models to generate efficient Ensemble Deep Learning | Ensemble Deep Learning improves accuracy over a single model by combining predictions from multiple models . It has established itself to be the core strategy for tackling the most difficult problems , like winning Kaggle challenges . Due to the lack of consensus to design a successful deep learning ensemble , we intro... | This paper is proposing to build ensembles of deep models, components of which have different hyperparameter (HP) configurations. This is done by first running Hyperband to create a large pool, and then run a greedy algorithm to construct an ensemble. This algorithm is termed Dykstra's algorithm on a certain graph, but... | SP:280c877eeaeb18c931ef41182155ce29a95adb06 |
Deep Learning is Singular, and That's Good | In singular models , the optimal set of parameters forms an analytic set with singularities and classical statistical inference can not be applied to such models . This is significant for deep learning as neural networks are singular and thus “ dividing ” by the determinant of the Hessian or employing the Laplace appro... | This paper is more like a review of singular learning theory and its implication on deep learning. The authors point out that deep neural networks are singular models and ways to characterize generalization error for regular models cannot produce satisfactory results in this setting. Then the authors introduce the sing... | SP:4a55b108b8ae5fe388f54028d939a84dcd677c49 |
On the Decision Boundaries of Neural Networks. A Tropical Geometry Perspective | 1 INTRODUCTION . Deep Neural Networks ( DNNs ) have demonstrated outstanding performance across a variety of research domains , including computer vision ( Krizhevsky et al. , 2012 ) , speech recognition ( Hinton et al. , 2012 ) , natural language processing ( Bahdanau et al. , 2015 ; Devlin et al. , 2018 ) , quantum c... | This work studies the decision boundaries of neural networks (NN) with piecewise linear (ReLU) activation functions from a tropical geometry perspective. Leveraging the work of [1], the authors show that NN decision boundaries form subsets of tropical hypersurfaces. This geometric characterization of NN decision bounda... | SP:f478e45dfb8dcd578090da3010b2b1df73595b66 |
Attentional Constellation Nets for Few-Shot Learning | 1 INTRODUCTION . Tremendous progress has been made in both the development and the applications of the deep convolutional neural networks ( CNNs ) ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2015 ; Szegedy et al. , 2015 ; He et al. , 2016 ; Xie et al. , 2017 ) . Visualization of the internal CNN structure train... | The paper proposes a constellation model that performs feature clustering and encoding dense part representations. The constellation module is placed after convolutional blocks. The module clusters cell features and calculates distance map between each cluster centroids and cell feature. The self-attention mechanism is... | SP:6a900a782e440dc5225d8ecb39155f594fa2cfb5 |
Progressively Stacking 2.0: A Multi-stage Layerwise Training Method for BERT Training Speedup | 1 INTRODUCTION . In recent years , the pre-trained language models , such as BERT ( Devlin et al. , 2018 ) , XLNet ( Yang et al. , 2019 ) , GPT ( Radford et al. , 2018 ) , have shown their powerful performance in various areas , especially in the field of natural language processing ( NLP ) . By pre-trained on unlabele... | The work proposes a simple enough idea to speed up the training of BERT by progressively stacking new layers while fixing older layers. Empirically, with the same number of training steps (and less time), the proposed method can achieve a comparable performance to the original BERT. When the same amount of running time... | SP:e6866231757407d20d8fbd8059cf1d0414efe018 |
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition | 1 INTRODUCTION . Innovations in Deep Neural Network ( DNN ) architecture design , data augmentation techniques and a continuous increase in the amount of available high quality training datasets , resulted in a massive reduction in ASR word-error-rate over the past decade [ Amodei et al. , 2016 ; Kim et al. , 2019 ; Pa... | The authors contribute to the NAS literature by presenting a framework that works decently well on small ASR tasks, specifically TIMIT. They make judicious decisions regard the macro and micro cells that are then swept over. They also show that there is some correlation between training for TIMIT and tasks that have mo... | SP:247dfe2208798ffebd81477467ac4dab8661ef3a |
Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability | Current methods for the interpretability of discriminative deep neural networks commonly rely on the model ’ s input-gradients , i.e. , the gradients of the output logits w.r.t . the inputs . The common assumption is that these input-gradients contain information regarding pθ ( y | x ) , the model ’ s discriminative ca... | The key message of this paper is that input-gradients (gradient of the logit wrt to input) or loss-gradients are/might be unrelated to the discriminative capabilities of a DNN. The input-gradient is a key primitive in several interpretability and visualization methods. Until now, it has been taken as a given that these... | SP:06c032ed2556090f71a474a5ff4ee340c103d5c2 |
Learning Subgoal Representations with Slow Dynamics | 1 INTRODUCTION . Deep Reinforcement Learning ( RL ) has demonstrated increasing capabilities in a wide range of domains , including playing games ( Mnih et al. , 2015 ; Silver et al. , 2016 ) , controlling robots ( Schulman et al. , 2015 ; Gu et al. , 2017 ) and navigation in complex environments ( Mirowski et al. , 20... | This paper proposes a new method for learning subgoal representations in HRL. The method learns a representation that emphasises features that change slowly, through a “slowness objective”. The slowness objective minimises changes in the subgoal representation between low level time steps, while maximising feature chan... | SP:f0ab80d4f3742a539ea2559845d00e8110ab9e98 |
Learning Monotonic Alignments with Source-Aware GMM Attention | 1 INTRODUCTION . In recent years , transformer models with soft attention have been widely adopted in various sequence generation tasks ( Raffel et al. , 2019 ; Vaswani et al. , 2017 ; Parmar et al. , 2018 ; Karita et al. , 2019 ) . Soft attention does not explicitly model the order of elements in a sequence and attend... | This paper introduces “source-aware” GMM attention and applies it to offline, online, long-form ASR. The value of source-aware GMM attention appears to be its ability to “ignore” long segments of silence in the input audio, which could potentially be more difficult to do using other attention mechanisms. Fairly compe... | SP:1d56942da0ed8d8280bd444bf9265b79b33b07eb |
On The Adversarial Robustness of 3D Point Cloud Classification | 1 INTRODUCTION . Despite the prominent achievements that deep neural networks ( DNN ) have reached in the past decade , adversarial attacks ( Szegedy et al. , 2013 ) are becoming the Achilles ’ heel in modern deep learning deployments , where adversaries generate imperceptible perturbations to mislead the DNN models . ... | The paper addresses the problem of adversarial robustness in 3D point cloud representations. It claims that two of the previous defense designs do not prevent adaptive attacks. The authors then propose to use adversarial training (AT) to improve the robustness. It claims that the standard MAX pooling operation within P... | SP:22bf1d0b48da000c80613747d59bc93c1270064e |
Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality | 1 INTRODUCTION . Wide adoption of electronic health records ( EHR ) in the medical care industry has led to accumulation of large volumes of medical data ( Pathak et al. , 2013 ) . This data contains information about the symptoms , syndromes , diseases , lab results , patient treatments and presents an important sourc... | This paper proposes a method to do medical entity extraction from HER data by fine-tuning a transformer model pretrained on a large EHR dataset. The model combines a two-step process of NER and NEN into a single step on a multi-label classification task by distantly supervised training. The main contribution of this p... | SP:1dff36cb48bfef13cafeed2e263fa0fd9c85ab08 |
Why Does Decentralized Training Outperform Synchronous Training In The Large Batch Setting? | 1 INTRODUCTION . Deep Learning ( DL ) has revolutionized AI training across application domains : Computer Vision ( CV ) ( Krizhevsky et al. , 2012 ; He et al. , 2015 ) , Natural Language Processing ( NLP ) ( Vaswani et al. , 2017 ) , and Automatic Speech Recognition ( ASR ) ( Hinton et al. , 2012 ) . Stochastic Gradie... | This paper claims that decentralized parallel SGD (DPSGD) performs better than synchronous SGD (SSGD) and noisy version of synchronous SGD (SSGD*) in large batch setting. Theoretically, it shows that the noise in DPSGD is landscape-dependent, which may help generalization. Experimental results on CV and ASR tasks show ... | SP:efd742fa15a8751c1b97e553bb6259944b2be339 |
Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking | 1 INTRODUCTION : STATE OF THE ART . Many real-world optimization challenges are black-box problems ; i.e. , instead of having an explicit problem formulation , they can only be accessed through the evaluation of solution candidates . These evaluations often require simulations or even physical experiments . Black-box o... | The paper proposes a benchmarking suite to overcome the problem low generalizability with black box optimization algorithm. The benchmarking suite consists of standard academic benchmarks to real world optimization problems. It also covers several scenarios such as dynamic-static, small to large-scale, discrete to mixe... | SP:8ac7287ce4e46fbcfd0b4231d6afab4238e7ca2c |
Latent Causal Invariant Model | 1 INTRODUCTION . Current data-driven deep learning models , revolutionary in various tasks though , heavily rely on i.i.d data to exploit all types of correlations to fit data well . Among such correlations , there can be spurious ones corresponding to biases ( e.g. , selection or confounding bias due to coincidence of... | This paper proposes a VAE based model for learning latent causal factors given data from multiple domains. Similar to [Kingma and Hyv¨arinen, 2020], it utilizes additional labels as supervision signals and learns the model using a Bayesian optimization approach given a fixed hypothetical causal structure. The identifia... | SP:7f369156e476623039e657c05ddc65aabdd923a8 |
BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayesian Fine-tuning | Despite their theoretical appealingness , Bayesian neural networks ( BNNs ) are falling far behind in terms of adoption in real-world applications compared with deterministic NNs , mainly due to their limited scalability in training and low fidelity in uncertainty estimates . In this work , we develop a new framework ,... | This paper introduces a fast way to get Bayesian posterior by using a pretrained deterministic model. Specifically, the authors first train a standard DNN model and then use it to initialize the variational parameters. Finally the variational parameters are optimized through standard variational inference (VI) training... | SP:d021dc94272c00ac362f53e3deb239da1292a734 |
WordsWorth Scores for Attacking CNNs and LSTMs for Text Classification | Black box attacks on traditional deep learning models trained for text classification target important words in a piece of text , in order to change model prediction . Current approaches towards highlighting important features are time consuming and require large number of model queries . We present a simple yet novel ... | This paper proposes WordsWorth score (WW score), a score to represent the importance of the word obtained from the trained model. Then, the score is applied to the greedy attack proposed by (Yang et al., 2018). In detail, the greedy attack first tries to search for the most important $k$ words in a text, and then it se... | SP:8359aea398860c827e9751215f55d399b2c9cfc0 |
Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent | 1 INTRODUCTION . Training many machine learning ( ML ) models reduces to solving the following finite-sum optimization problem min w f ( w ) : = min w 1 N N∑ i=1 fi ( w ) : = min w 1 N N∑ i=1 L ( g ( xi , w ) , yi ) , w ∈ Rd , ( 1 ) where { xi , yi } Ni=1 are the training samples and L is the loss function , e.g. , cro... | This paper proposes to restart the momentum parameter in SGD (with Nesterov's momentum) according to some carefully chosen schedules in training deep neural network, which is named as SRSGD. Two different restarting schedules are proposed: linear schedule and exponential schedule. The strong point of this paper is its ... | SP:2788722ffb82bb4ee15189b47e16d178eccecf3e |
Exploring single-path Architecture Search ranking correlations | 1 INTRODUCTION . The development and study of algorithms that automatically design neural networks , Neural Architecture Search ( NAS ) , has become a significant influence in recent years ; owed to the promise of creating better models with less human effort and in shorter time . Whereas the first generations of algor... | + This paper studies the single-path one-shot super-network predictions and ranking correlation throughout an entire search space, as all stand-alone model results are known in advance. This is a crucial step in NAS. As we know, inaccurate architecture rating is the cause of ineffective NAS in almost all existing NAS m... | SP:2c21ee98d8ae42925da9d69e11cc2584e7e9dce8 |
Set Prediction without Imposing Structure as Conditional Density Estimation | 1 INTRODUCTION . This paper strives for set prediction . Making multiple predictions with intricate interactions is essential in a variety of applications . Examples include predicting the set of attributes given an image ( Rezatofighi et al. , 2020 ) , detecting all pedestrians in video footage ( Wang et al. , 2018 ) ... | Authors propose a new method for formulating set prediction tasks. They propose to use a noisy energy-based model with langevin mcmc + noisy startup as their model. The can approximate the gradient of the likelihood function by computing the enery of ground truth pairs and energy of synthesized pairs where the target i... | SP:fd7c0858a0f642af7bfe4340bbbd8c598a4f5e32 |
Learning Irreducible Representations of Noncommutative Lie Groups | 1 INTRODUCTION . Many tasks in machine learning exactly or approximately obey a continuous symmetry such as 2D rotations . An ML model is said to be equivariant to such a symmetry if the model respects it automatically ( without training ) . Equivariant models have been applied to tasks ranging from computer vision to ... | The paper proposes the algorithm LearnRep that uses gradient descent methods to learn Lie algebras from structure constants, before obtaining the corresponding group representation through the exponential map. The algorithm is tested on SO(3), SO(2, 1), and SO(3, 1). In addition to this, the paper proposes SpaceTimeNet... | SP:18fb9d26da8c96c91e9787d3b539c483f9fe4871 |
ProGAE: A Geometric Autoencoder-based Generative Model for Disentangling Protein Conformational Space | 1 INTRODUCTION . The complex and time-consuming calculations in molecular simulations have been significantly impacted by the application of machine learning techniques in recent years . In particular , deep learning has been applied to analysis and simulation of molecular trajectories to address diverse problems , suc... | The paper introduces a geometric variational autoencoder for capturing protein structural ensembles, disentangling intrinsic and extrinsic geometry into separate latent spaces. The model is shown to accurately reconstruct protein structure, and the difference between the intrinsic and extrinsic latent spaces are explor... | SP:f21bf18198261a5400f8aa437e305ea60b7695ac |
Adaptive Single-Pass Stochastic Gradient Descent in Input Sparsity Time | We study sampling algorithms for variance reduction methods for stochastic optimization . Although stochastic gradient descent ( SGD ) is widely used for large scale machine learning , it sometimes experiences slow convergence rates due to the high variance from uniform sampling . In this paper , we introduce an algori... | This paper develops an efficient streaming algorithm to approximate the optimal importance sampling weights for variance reduction in finite-sum SGD. The optimal weights are proportional to each sample's gradient norm; this work uses AMS-like moment estimation to sketch gradient norms which take the form of a bounded-d... | SP:637780028802e048cce8c2a18cbaaa851e915b38 |
Interpretable Models for Granger Causality Using Self-explaining Neural Networks | 1 INTRODUCTION . Granger causality ( GC ) ( Granger , 1969 ) is a popular practical approach for the analysis of multivariate time series and has become instrumental in exploratory analysis ( McCracken , 2016 ) in various disciplines , such as neuroscience ( Roebroeck et al. , 2005 ) , economics ( Appiah , 2018 ) , and... | This paper primarily deals with learning Granger-causal relationships in multivariate time series in the nonlinear dynamics setting. The core method uses vector autoregressive modeling with sparsity inducing regularizers (elastic net and smoothness based fused lasso) along with the recently proposed with self-explainin... | SP:f7c98dd7ab57f9ffc12e7d462ac5d2ae04504504 |
Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model | 1 INTRODUCTION . Large-scale pre-trained language models have attracted tremendous attention recently due to their impressive empirical performance on a wide variety of NLP tasks . These models typically abstract semantic information from massive unlabeled corpora in a self-supervised manner . Masked language model ( M... | This paper theoretically shows that the gradient variance of the standard MLM (masked language modeling) task in BERT-style training depends on the covariance of the gradient covariance between different masks within the mini-batch. This paper then empirically shows that the covariance can be reduced by making the mask... | SP:0ae8f7b5bbb7f3cb1f97a95af2d936f44a494a9c |
Thinking Like Transformers | 1 INTRODUCTION . While Yun et al . ( 2019 ) show that sufficiently large transformers can approximate any constantlength sequence-to-sequence function , and Hahn ( 2019 ) provides theoretical limitations on their ability to compute functions on unbounded input length , neither of these provide insight on how a transfor... | The authors introduce a DSL, the Restricted Access Sequence Processing (RASP) language, that they claim can serve as a computational model for the transformer-encoder. They develop the reader's intuition for RASP by providing RASP implementations of many basic operations such as computing histograms, sorting, and reve... | SP:bafc54f2425a7c809ceb795b0c972efba778d06d |
Drift Detection in Episodic Data: Detect When Your Agent Starts Faltering | 1 INTRODUCTION . Reinforcement learning ( RL ) algorithms have recently demonstrated impressive success in a variety of sequential decision-making problems ( Badia et al. , 2020 ; Hessel et al. , 2018 ) . While most RL works focus on the maximization of rewards under various conditions , a key issue in real-world RL ta... | This paper considers the drift detection for episodic data, where data episodes are assumed to be i.i.d. but data within each episodic can be correlated. It is assumed that the pre-change (nominal) mean and covariance of each episodic is perfectly known or can be accurately estimated from reference data. The Uniform De... | SP:43947cdb5064af3146a898c27347d7d987f92e30 |
Batch Inverse-Variance Weighting: Deep Heteroscedastic Regression | 1 INTRODUCTION . In supervised learning , a central assumption is that the samples in the training dataset , used to train the model , and the samples in the testing set , used to evaluate the model , are sampled from identical distributions . Formally , for input x and label y , this assumption implies that ptrain ( x... | In this paper, a reweighting technique is proposed to suppress the impact of heteroscedastic label noise in regression model training. The objective function of the regression model training process is composed of a weighted combination of instance-wise training loss. The instance-wise weight is determined by the estim... | SP:355303ce20a95719616333e88b1732715e1a9ff7 |
The Role of Momentum Parameters in the Optimal Convergence of Adaptive Polyak's Heavy-ball Methods | t ) , as opposed to that of O ( log t√ t ) of SGD , where t is the number of iterations . Our new analysis not only shows how the HB momentum and its timevarying weight help us to achieve the acceleration in convex optimization but also gives valuable hints how the momentum parameters should be scheduled in deep learni... | The authors investigate the convergence of the projected Heavy-ball method (and an adaptive variant) for convex problems with convex constraints. The authors prove 4 results: 2 individual (last iterate) convergence rates and 2 rates using averaging. Notably, in their proofs they require an increasing (from 1/2 to 1) mo... | SP:88a54725f8b4e2e8b1876b37b783876ed14a205b |
On the Inversion of Deep Generative Models | 1 INTRODUCTION . In the past several years , deep generative models , e.g . Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) and Variational Auto-Encoders ( VAEs ) ( Kingma & Welling , 2013 ) , have been greatly developed , leading to networks that can generate images , videos , and speech voices a... | In this submission, the authors study the inversion of ReLU networks (where the output of the network is subject to an invertible activation function). This is an important task, for example for inverse problems using generative priors. The authors introduce spark-based conditions for the invertibility of each layer ... | SP:26a9ea5bc6af46b1e59b1e34390a1bdb5a660312 |
SiamCAN:Simple yet Effective Method to enhance Siamese Short-Term Tracking | 1 INTRODUCTION . Visual object tracking is the fundamental task of computer vision , aiming at tracking unknown object of which the information is given by the first frame . Although great progress has been achieved in recent years , a robust tracker is still in desperate demand due to tricky challenge such as scale va... | The architecture of the tracker is standard siamese. The novelty is at a technical level, modules of the "cross-guided" type have been proposed. It does bring an improvement, but not to the state-of-the-art level. There is no significant insight, training, updating novelty or theoretical. Recent short-term trackers ou... | SP:bba4f71cb381146e980c7cb32dd2510e1bcdb226 |
Supervision Accelerates Pre-training in Contrastive Semi-Supervised Learning of Visual Representations | We investigate a strategy for improving the efficiency of contrastive learning of visual representations by leveraging a small amount of supervised information during pre-training . We propose a semi-supervised loss , SuNCEt , based on noise-contrastive estimation and neighbourhood component analysis , that aims to dis... | This paper designs a new loss, called SuNCTt, to speed up the convergence of semi-supervised training. Specifically, the loss involves the computation of similarity between anchor and other images with the same class, and the similarity between anchor and other labeled images. It is claimed to be considered as the form... | SP:2385685fee86534706f021a67f2393812f063415 |
A Benchmark for Voice-Face Cross-Modal Matching and Retrieval | Cross-modal associations between a person ’ s voice and face can be learned algorithmically , and this is a useful functionality in many audio and visual applications . The problem can be defined as two tasks : voice-face matching and retrieval . Recently , this topic has attracted much research attention , but it is s... | This paper aims to propose a benchmark for voce-face matching and retrieval problem. As shown by the test confidence analysis, the model is suggested to be evaluated on a large dataset or multiple datasets to avoid the large deviation in the accuracy. A baseline method TriNet and joint matching & retrieval are proposed... | SP:e3942da570a78a6c9668db22ab5d6ddce52f756f |
Learning a Latent Search Space for Routing Problems using Variational Autoencoders | 1 INTRODUCTION . Significant progress has been made in learning to solve optimization problems via machine learning ( ML ) . Especially for practical applications , learning-based approaches are of great interest because of the high labor costs associated with the development of completely hand-crafted solution approac... | This paper proposes a method to learn a continuous latent space via CVAE to represent solutions to routing problems. Combined with differentiable evolution search algorithms, one can search in the learned latent space for solutions to new problem instances at test time. The proposed method is evaluated on two classes o... | SP:ddd2ae85b54dbb9143d25adf8bb2977732dae29b |
Efficient Wasserstein Natural Gradients for Reinforcement Learning | A novel optimization approach is proposed for application to policy gradient methods and evolution strategies for reinforcement learning ( RL ) . The procedure uses a computationally efficient Wasserstein natural gradient ( WNG ) descent that takes advantage of the geometry induced by a Wasserstein penalty to speed opt... | This paper proposes to use natural gradient instead of standard gradient to optimize a regularized objective with the regularization being the Wasserstein distance between the so-called behaviour distributions for the previous policy and new policy. It then combines this Wasserstein gradient descent with Policy Gradien... | SP:2fbbc4ff1a587e2239a4f5b8672dd310d0124e39 |
Learning to live with Dale's principle: ANNs with separate excitatory and inhibitory units | 1 INTRODUCTION . In recent years , artificial neural networks ( ANNs ) have been increasingly used in neuroscience research for modelling the brain at the algorithmic and computational level ( Richards et al. , 2019 ; Kietzmann et al. , 2018 ; Yamins & DiCarlo , 2016 ) . They have been used for exploring the structure ... | Inspired by the observations of feedforward inhibition in the brain, the authors propose a novel ANN architecture that respects Dale’s rule (DANN). They provide two improvements for training DANNs: better initialization and update scaling for synaptic weights. As a result, they empirically demonstrate that DANNs perfor... | SP:c3835de54da82e1b07406d118aca719082367ffb |
SketchEmbedNet: Learning Novel Concepts by Imitating Drawings | 1 INTRODUCTION . Upon encountering a novel concept , such as a six-legged turtle , humans can quickly generalize this concept by composing a mental picture . The ability to generate drawings greatly facilitates communicating new ideas . This dates back to the advent of writing , as many ancient written languages are ba... | This paper proposes learning embeddings for sketch or natural images by training a network that takes in a raster image and outputs and collection of sketch strokes. The architecture consists of a standard CNN encoder followed by an RNN decoder. The authors evaluate their learned embeddings on few-shot classification t... | SP:765c8b969d795ab629aa74bc20e8f19558a4e165 |
Learn what you can't learn: Regularized Ensembles for Transductive out-of-distribution detection | 1 INTRODUCTION . Modern machine learning ( ML ) systems can achieve good test set performance and are gaining popularity in many real-world applications - from aiding medical diagnosis ( Beede et al. , 2020 ) to making recommendations for the justice system ( Angwin et al. , 2016 ) . In reality however , some of the da... | The problem of good predictive uncertainty-based out of distribution (OOD) detection is essential for classification systems to be deployed in safety-critical environments. The authors present a method RETO that achieves state-of-the-art performance in a transductive OOD detection setting. Like other predictive uncerta... | SP:9f14c6cce4e92d92e0025b6ede2a04a862c3b5a9 |
Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations | 1 INTRODUCTION . Self-supervised learning aims to learn representations of data that generalize to a large variety of downstream tasks . Recently , contrastive self-supervised learning ( CSL ) has achieved impressive results on several computer vision tasks ( Oord et al. , 2018 ; Hjelm et al. , 2018 ; He et al. , 2020 ... | This paper presents a new contrastive audio-visual learning method. Like previous work, they use self-supervision to learn a video feature set by training a network to associate audio and visual "views" taken from the same video. Their main contribution is to jointly learn from both "local" and "global" information. Th... | SP:d3a089d045255fe67d84efc540969b6ce8bb4448 |
Layer-wise Adversarial Defense: An ODE Perspective | 1 INTRODUCTION . Recent years have witnessed the prosperity of deep learning in many tasks ( Hinton & Salakhutdinov , 2006 ; Sutskever et al. , 2014 ; He et al. , 2016 ; LeCun et al. , 2015 ; Huang et al. , 2017 ; Vaswani et al. , 2017 ) . Stacked with multiple layers , neural networks provide an end-to-end solution to... | This paper proposed a layer-wise adversarial defense which added perturbations in each hidden layer considering the influence of hidden features in latent space from the ODE perspective. It is essential to enhance the adversarial model robustness by stabilizing both inputs and hidden layers. The proposed method leverag... | SP:9cf8f7dba8b4e672d685bc89295f237f422937cf |
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization | 1 INTRODUCTION . As Deep Learning methods are being deployed across society , academia and industry , the need to understand their decisions becomes ever more pressing . Under certain conditions , a “ right to explanation ” is even required by law in the European Union ( GDPR , 2016 ; Goodman & Flaxman , 2017 ) . Fortu... | This paper asks a simple question: do extreme-activating synthetic images for a CNN unit help a human observer to predict that unit’s response to natural images, compared with maximally/minimally activating natural images. The authors present human observers with images synthesized to maximally or minimally activate a ... | SP:075f74ff0eec8a4d36e3d9d6c62276776dd465ba |
MVP: Multivariate polynomials for conditional generation | Conditional Generative Adversarial Nets ( cGANs ) have been widely adopted for image generation . cGANs take i ) a noise vector and ii ) a conditional variable as input . The conditional variable can be discrete ( e.g. , a class label ) or continuous ( e.g. , an input image ) resulting into class-conditional ( image ) ... | This paper proposes a conditional generation framework (cGAN) that bridges the gap between discrete and continuous variable used in the generation. They do so by proposing a new network architecture that implements higher order multi variate polynomials (MVP). They show that MVP generalizes well to different types of c... | SP:400ec44ff0b658f1acbd74ab8c710f88bea6f7dd |
Transforming Recurrent Neural Networks with Attention and Fixed-point Equations | 1 INTRODUCTION . Recurrent Neural Network , known as RNN , has been widely applied to various tasks in the last decade , such as Neural Machine Translation ( Kalchbrenner & Blunsom , 2013 ; Sutskever et al. , 2014 ) , Text Classification ( Zhou et al. , 2016 ) , Name Entity Recognition ( Zhang & Yang , 2018 ; Chiu & Ni... | This paper aims to incorporate the attention mechanism into recurrent neural networks by using fixed point equations. In particular, the authors define a bidirectional RNN with attention by a fixed point equation and then transform it to a variant of the Transformer block. The proposed model StarSaber is shown to be mo... | SP:66df8bc94a4e5e99341cd1ad491018cca6207ad9 |
Dissecting graph measures performance for node clustering in LFR parameter space | 1 INTRODUCTION . Graph node clustering is one of the central tasks in graph structure analysis . It provides a partition of nodes into disjoint clusters , which are groups of nodes that are characterized by strong mutual connections . It can be of practical use for graphs representing real-life systems , such as social... | The paper deals with the problem of community detection on graphs, examining the impact of graph measures. To do so, the paper proposes an experimental framework where clustering is achieved using the kernel k-means algorithm, and the performance of graph measures is examined on various instances of artificially genera... | SP:741bcead336d8cc7288ce82bca8028516280fff0 |
DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation | 1 INTRODUCTION . The enormous computational intensity of Deep Neural Network ( DNN ) models has attracted great interest in optimizing their performance . Popular deep learning ( DL ) frameworks such as PyTorch ( Paszke et al. , 2019 ) and TensorFlow ( Abadi et al. , 2016 ) adopt custom optimized kernels such as Intel ... | In this paper, the authors develop DynaTune which achieves faster convergence speed to optimize a DNN model when compared to the state-of-the-art DL compiler, AutoTVM. The key idea is a time-slot-based scheduling method based on UCB-type multi-armed bandit policy. At each time, the scheduler chooses an action to maximi... | SP:cd671e0b2ae21fbca75c90741ccd008fefdd76ec |
GLUECode: A Benchmark for Source Code Machine Learning Models | 1 INTRODUCTION . In recent years , there has been considerable interest in researching machine learning models on source code artifacts . Machine learning models have been used to address a variety of software engineering tasks , as the inherent rich structure of code has allowed machine learning researchers to explore... | The objective of this paper is to present a benchmark of code understanding tasks in the spirit of GLUE benchmarks in NLP. Towards this, it designs 5 Java language tasks: NPath complexity, operator prediction, method naming, completion of method calls, and null dereference prediction. An evaluation on some common neura... | SP:fff5b8e98a9909fb289cd1455d381df4b75f01fe |
Testing Robustness Against Unforeseen Adversaries | 1 INTRODUCTION . Neural networks perform well on many datasets ( He et al. , 2016 ) yet can be consistently fooled by minor adversarial distortions ( Goodfellow et al. , 2014 ) . The research community has responded by quantifying and developing adversarial defenses against such attacks ( Madry et al. , 2017 ) , but th... | This paper proposes four novel efficient adversarial attack methods beyond Lp threat models. Together with other two existing attack methods, these six attack methods combine as a framework to evaluate robustness of defenses against unforeseen attacks. In this framework, the novel measure is normalized with the perform... | SP:b49b8d0d0ece60538ce7629c6affeefbcdaf2d3c |
Regularization Shortcomings for Continual Learning | 1 INTRODUCTION . Continual Learning is a sub-field of machine learning dealing with non-iid ( identically and independently distributed ) data French ( 1999 ) ; Lesort et al . ( 2019c ) . Its goal is to learn the global optima to an optimization problem where the data distribution changes through time . This is typical... | This paper presents a theoretical analysis of regularization based approaches to the problem of continually learning a sequence of tasks. The point of the paper is to demonstrate shortcomings of these kinds of approaches, in the context of class-incremental learning where classes are observed once and one after another... | SP:7d8d860da15b936e3976601cae537e18664c08e8 |
Beyond GNNs: A Sample Efficient Architecture for Graph Problems | 1 INTRODUCTION . In recent years Graph Neural Networks ( GNNs ) have become the predominant paradigm for learning problems over graph structured data ( Hamilton et al. , 2017 ; Kipf & Welling , 2016 ; Veličković et al. , 2017 ) . Computation in GNNs is performed by each node sending and receiving messages along the e... | This paper proposes a new building block for GNNs, called GNN$^+$. This building block trades of depth for width and involves multiple parallel regular GNN processing units. Using the GNN$^+$ architecture, the authors establish bounds for the required network depth (and total parameters) for several combinatorial probl... | SP:dae92debea3e0d59d4b74385540ee6f827cfa37e |
Variational Dynamic Mixtures | 1 INTRODUCTION . Making sense of time series data is an important challenge in various domains , including ML for climate change . One important milestone to reach the climate goals is to significantly reduce the CO2 emissions from mobility ( Rogelj et al. , 2016 ) . Accurate forecasting models of typical driving behav... | This paper introduces variational dynamic mixtures (VDM), a new variational family, and demonstrates that using VDM to model the approximate posterior in sequential latent variable models can better capture multi-modality in data. VDM includes a distribution over recurrent states in the inference model, such that a sam... | SP:ab6c0eee6eebb90361fa87f9beeaf1722e4ec983 |
Private Split Inference of Deep Networks | Splitting network computations between the edge device and the cloud server is a promising approach for enabling low edge-compute and private inference of neural networks . Current methods for providing the privacy train the model to minimize information leakage for a given set of private attributes . In practice , how... | This paper tackles a timely problem of privacy leakage on the edge devices when applying deep neural networks. Instead of mitigating the leakage of a set of private attributes, the proposed method tries to remove the information irrelevant to the primary task. The proposed method does not need to identify the private a... | SP:fd4240e0f2c6faa6783fe5e1d1e53d0d5f0945a0 |
Offline policy selection under Uncertainty | 1 INTRODUCTION . Off-policy evaluation ( OPE ) ( Precup et al. , 2000 ) in the context of reinforcement learning ( RL ) is often motivated as a way to mitigate risk in practical applications where deploying a policy might incur significant cost or safety concerns ( Thomas et al. , 2015a ) . Indeed , by providing method... | This paper proposes a method BayesDICE to estimate posteriors over candidate policy values, which can be used for downstream policy selection. Specifically, the authors estimate the posteriors over the correction ratios for state-action pairs, which optimize a combined metric of a chance constraint from collected data ... | SP:0cb9035abb016fd549b5606e20e2229dace5033d |
Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control | 1 INTRODUCTION . The use of regularization methods to prevent overfitting is a key technique in successfully training neural networks . Perhaps the most widely recognized regularization methods in deep learning are L2 regularization ( also known as weight decay ) and dropout ( Srivastava et al. , 2014 ) . These techniq... | This paper conducts a comprehensive study on the effect of different regularization on Deep RL algorithms. Regularization has been mostly neglected in RL as most benefits were believed to be in generalization to unseen test environments in supervised learning settings. However, this paper shows that regularization does... | SP:3d8801dc33baf1d1037f26f50be0da1001003cf3 |
Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning | 1 INTRODUCTION . Deep learning has recently shown promise to play a major role in devising new solutions to applications with natural phenomena , such as climate change ( Manepalli et al. , 2019 ; Drgona et al. , 2019 ) , ocean dynamics ( Cosne et al. , 2019 ) , air quality ( Soh et al. , 2018 ; Du et al. , 2018 ; Lin ... | The authors propose methodology for sharing learned differencing coefficients for estimating spatial derivatives between multiple spatio-temporal modeling tasks. They show that increased number of tasks improves learning. Additionally, the authors propose a meta-initialization procedure by which the differencing coeffi... | SP:824f8e8bc7c19ac46059d53c2ad192a2f905fd90 |
Descending through a Crowded Valley — Benchmarking Deep Learning Optimizers | 1 INTRODUCTION . Large-scale stochastic optimization drives a wide variety of machine learning tasks . Because choosing the right optimization algorithm and effectively tuning its hyperparameters heavily influences the training speed and final performance of the learned model , doing so is an important , every-day chal... | This paper benchmarks popular optimizers for training neural networks. The experiments consider all possible combinations of 3 different tuning budgets, and 4 different fixed learning rate schedules on 8 deep learning workloads for 14 optimizers. The paper highlights two main observations: 1) there is no clear dominati... | SP:0d632e93235a2e5b3016ba66b339e0141d510f1f |
Modifying Memories in Transformer Models | 1 INTRODUCTION . Large-scale Transformer based language models ( Vaswani et al. , 2017 ; Devlin et al. , 2018 ; Radford et al. , 2019 ; Raffel et al. , 2019 ; Brown et al. , 2020 ) have not only pushed state-of-the-art on standard natural language processing ( NLP ) benchmarks such as GLUE and SQuAD , but they have als... | Recently, pretrained Transformer language models have been shown to capture world knowledge (using testbeds containing facts). What if you want to update a fact, for example, with the current president of USA? This paper investigates different approaches to update the weights of a Transformer model such that the model ... | SP:86a3f8091d534d50e25612cbb933819d2a090941 |
Globetrotter: Unsupervised Multilingual Translation from Visual Alignment | 1 INTRODUCTION Machine translation aims to learn a mapping between sentences of different languages while also maintaining the underlying semantics . In the last few years , sequenceto-sequence models have emerged as remarkably powerful methods for this task , leading to widespread applications in robust language trans... | The authors propose to leverage images to train an unsupervised machine translation (MT) model. Their main idea is that the similarity of images can be used as a proxy for the similarity of sentences describing the images. The sentences, in turn, can be in different languages, and knowledge about their similarity can b... | SP:11d9e619756f936a241fb838a78157de03d22344 |
Matrix Data Deep Decoder - Geometric Learning for Structured Data Completion | 1 INTRODUCTION . Matrix completion ( MC ) consists of estimating the missing entries of an n×m matrixX ( usually , of very big dimensions ) given its measurements M on a ( usually , very sparse ) support Ω . An example of such matrices are signals on graphs/manifolds which are Non-Euclidean domains . The classical exam... | This paper aims to tackle the matrix completion problem by drawing connection from prior work in image completion domain. It seems to be a combination of prior work: Multi-graph convolution combined with Dirichlet energy on row and column graph laplacian where the input rating matrix is corrupted with noise. The writin... | SP:1e43e2ad50364f396fa19a2e9d8e9f7244a40178 |
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification | 1 INTRODUCTION . Complex systems are ubiquitous in natural and scientific disciplines , and how the relation between component parts gives rise to global behaviour of a system is a central research topic in many areas such as system biology ( Camacho et al. , 2018 ) , neural science ( Kriegeskorte , 2015 ) , and drug a... | The proposed SLIM algorithm organizes graph neural networks around substructures surrounding "landmarks" in the graph. In addition to presenting the three steps of the SLIM algorithm (sub-structure embedding, sub-structure landmarking, and "identity-preserving" graph pooling), the authors compare to other approaches o... | SP:b2f40913d778d27c888d81bec337aa81a1acb46c |
Federated learning using mixture of experts | 1 INTRODUCTION In many real-world scenarios , data is distributed over a large number of devices , due to privacy concerns or communication limitations . Federated learning is a framework that can leverage this data in a distributed learning setup . This allows for exploiting both the compute power of all participating... | The proposed method is a federated method allowing to have a certain amount of data shared between all the learners and some data specific to each learner. The targeted field of application is classification for problems where strong privacy is crucial. The method consists in learning a global classifier (with the shar... | SP:9a8aa745df5a94d693dde585ca37765f9d657978 |
Redefining The Self-Normalization Property | 1 INTRODUCTION . In recent years , deep neural networks ( DNNs ) have achieved state-of-the-art performance on different tasks like image classification ( He et al. , 2015 ; Zheng et al. , 2019 ) . This rapid development can be largely attributed to the initialization and normalization techniques that prevent the gradi... | The paper proposes two modifications to SELU activation function to improve it with regards to preserving forward-backward signal propagation in neural networks. The work builds on top of the mean-field theory literature and provides a modified self-normalization property (additional constraints compared to SELU). Furt... | SP:235d680e5cfac85db6704ba1d79eb7b728da8d08 |
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation | 1 INTRODUCTION . Recently , neural reasoning over knowledge graphs ( KGs ) has emerged as a popular paradigm in machine learning and natural language processing ( NLP ) . KG-augmented models have improved performance on a number of knowledge-intensive downstream tasks : for question answering ( QA ) , the KG provides c... | The paper provides a number of adversarial attacks on hybrid neural-symbolic systems. The systems are recommender and QA systems which use an underlying knowledge-graph (KG) such as ConceptNet. Previous work has suggested that the KGs are important for good performance, and moreover that the use of KGs lends the system... | SP:7d6dc558590032eefb2033e9a2c784124bac8ac1 |
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