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Fidelity-based Deep Adiabatic Scheduling | 1 INTRODUCTION . Many of the algorithms developed for quantum computing employ the quantum circuit model , in which a quantum state involving multiple qubits undergoes a series of invertible transformations . However , an alternative model , called Adiabatic Quantum Computation ( AQC ) ( Farhi et al. , 2000 ; McGeoch ,... | the paper proposes to learn parametric form of optimal quantum annealing schedule. Authors construct 2 versions of neural network parameterizations mapping problem data onto an optimal schedule. They train these networks on artifically generated sets of problem of different size and test final models on the Grover sear... | SP:9156d551adff4ed16ba1be79014188caefc901c7 |
Bayesian Context Aggregation for Neural Processes | 1 INTRODUCTION . Estimating statistical relationships between physical quantities from measured data is of central importance in all branches of science and engineering and devising powerful regression models for this purpose forms a major field of study in statistics and machine learning . When judging representative ... | The paper builds upon previous lines of research on multi-task learning problem, such as conditional latent variable models including the Neural Process. As shown by the extensive Related Work section, this seems to be an active research direction. This makes it difficult for me to judge originality and significance, b... | SP:13fb6d0e4b208c11e5d58df1afac2921c02be269 |
Multi-agent Deep FBSDE Representation For Large Scale Stochastic Differential Games | 1 INTRODUCTION . Stochastic differential games represent a framework for investigating scenarios where multiple players make decisions while operating in a dynamic and stochastic environment . The theory of differential games dates back to the seminal work of Isaacs ( 1965 ) studying two-player zero-sum dynamic games ,... | Till page 3 the paper was easy to follow, i.e., the analytical expressions in eq(5), and the basic idea of Algorithm 1 (which is same as prior works by Han et al. , Wang et al., Periera et al.) are clear. However, after page 3 the paper is hard to follow. The specific points are as follows: | SP:368ac9d4b7934e68651c1b54286d9332caf16473 |
Regularized Mutual Information Neural Estimation | 1 INTRODUCTION . Identifying a relationship between two variables of interest is one of the great linchpins in mathematics , statistics , and machine learning ( Goodfellow et al. , 2014 ; Ren et al. , 2015 ; He et al. , 2016 ; Vaswani et al. , 2017 ) . Not surprisingly , this problem is closely tied to measuring the re... | This paper attempts to answer the four questions raised from the mutual information estimator. To this end, this paper investigates why the MINE succeeds or fails during the optimization on a synthetic dataset. Based on the observations and discussions, the paper then proposes a novel lower bound to regularize the neur... | SP:e4664a073afd05446cb1ddc217163692a9a12c1c |
Contextual Dropout: An Efficient Sample-Dependent Dropout Module | 1 INTRODUCTION . Deep neural networks ( NNs ) have become ubiquitous and achieved state-of-the-art results in a wide variety of research problems ( LeCun et al. , 2015 ) . To prevent over-parameterized NNs from overfitting , we often need to appropriately regularize their training . One way to do so is to use Bayesian ... | The paper proposes contextual dropout as a sample-dependent dropout module, which can be applied to different models at the expense of marginal memory and computational overhead. The authors chose to focus on Visual Question Answering and Image classification tasks. The results in the paper show the contextual dropbox... | SP:b1c7e0c9656a0ec0399b6602f89f46323ff3436b |
Net-DNF: Effective Deep Modeling of Tabular Data | 1 INTRODUCTION . A key point in successfully applying deep neural models is the construction of architecture families that contain inductive bias relevant to the application domain . Architectures such as CNNs and RNNs have become the preeminent favorites for modeling images and sequential data , respectively . For exa... | The authors propose a end-to-end deep learning model called Net-DNF to handle tabular data. The architecture of Net-DNF has four layers: the first layer is a dense layer (learnable weights) with tanh activation eq(1). The second layer (DNNF) is formed by binary conjunctions over literals eq(2). The third layer is an em... | SP:ee9764a48b109b9860c0a6f657a6cdd819237e7e |
Decorrelated Double Q-learning | 1 INTRODUCTION . Q-learning Watkins & Dayan ( 1992 ) as a model free reinforcement learning approach has gained popularity , especially under the advance of deep neural networks Mnih et al . ( 2013 ) . In general , it combines the neural network approximators with the actor-critic architectures Witten ( 1977 ) ; Konda ... | The paper suggests an improvement over double-Q learning by applying the control variates technique to the target Q, in the form of $(q1 - \beta (q2 - E(q2))$ (eqn (8)). To minimize the variance, it suggests minimizing the correlation between $q1$ and $q2$. In addition, it applies the TD3 trick. The resulting algorithm... | SP:9962a592fe8663bbcfe752b83aa9b666fe3a9456 |
Linking average- and worst-case perturbation robustness via class selectivity and dimensionality | 1 INTRODUCTION . Methods for understanding deep neural networks ( DNNs ) often attempt to find individual neurons or small sets of neurons that are representative of a network ’ s decision ( Erhan et al. , 2009 ; Zeiler and Fergus , 2014 ; Karpathy et al. , 2016 ; Amjad et al. , 2018 ; Lillian et al. , 2018 ; Dhamdhere... | This work empirically studies the relationship between robustness and class selectivity, a measure of neuron variability between classes. Robustness to both adversarial ("worst-case") perturbations and corruptions ("average-case") are considered. This work builds off the recent work of Leavitt and Morcos (2020) (curren... | SP:73f0f92f476990989fa8339f789a77fadb5c1e26 |
Isotropy in the Contextual Embedding Space: Clusters and Manifolds | 1 INTRODUCTION . The polysemous English word “ bank ” has two common senses : 1. the money sense , a place that people save or borrow money ; 2. the river sense , a slope of earth that prevents the flooding . In modern usage , the two senses are very different from one another , though interestingly , both senses share... | The authors investigate the token embedding space of a variety of contextual embedding models for natural language. Using techniques based on nearest neighbors, clustering, and PCA, they report a variety of results on local dimensionality / anisotropy / clustering / manifold structure in these embedding models which ar... | SP:8fe8ad33a783b2f98816e57e88d20b67fed50e8d |
In Search of Lost Domain Generalization | 1 INTRODUCTION . Machine learning systems often fail to generalize out-of-distribution , crashing in spectacular ways when tested outside the domain of training examples ( Torralba and Efros , 2011 ) . The overreliance of learning systems on the training distribution manifests widely . For instance , self-driving car s... | This paper critically re-examines research in domain generalisation (DG), ie building models that robustly generalise to out-of-distribution data. It observes that existing methods are hard to compare, in particular due to unclear hyper-parameter and model selection criteria. It introduces a common benchmark suite incl... | SP:9e4a85fa5d76f345b5a38b6f86710a53e1d08503 |
Sparse Uncertainty Representation in Deep Learning with Inducing Weights | 1 Introduction . Deep learning models are becoming deeper and wider than ever before . From image recognition models such as ResNet-101 ( He et al. , 2016a ) and DenseNet ( Huang et al. , 2017 ) to BERT ( Xu et al. , 2019 ) and GPT-3 ( Brown et al. , 2020 ) for language modelling , deep neural networks have found consi... | This work proposes a specific parametrisation for the Gaussian prior and approximate posterior distribution in variational Bayesian neural networks in terms of inducing weights. The general idea is an instance of the sparse variational inference scheme for GPs proposed by Titsias back in 2009; for a given model with a ... | SP:04abdf6d039513f23e00e6686832cd4b950f1d75 |
Hybrid-Regressive Neural Machine Translation | 1 INTRODUCTION . Although autoregressive translation ( AT ) has become the de facto standard for Neural Machine Translation ( Bahdanau et al. , 2015 ) , its nature of generating target sentences sequentially ( e.g. , from left to right ) makes it challenging to respond quickly in a production environment . One straight... | This paper proposes a hybrid-regressive machine translation (HRT) approach—combining autoregressive (AT) and non-autoregressive (NAT) translation paradigms: it first uses an AT model to generate a “gappy” sketch (every other token in a sentence), and then applies a NAT model to fill in the gaps with a single pass. As a... | SP:4b4f70092c9fceabdc76c6ed5c5cf83c7791e119 |
D3C: Reducing the Price of Anarchy in Multi-Agent Learning | 1 INTRODUCTION . We consider a setting composed of multiple interacting artificially intelligent agents . These agents will be instantiated by humans , corporations , or machines with specific individual incentives . However , it is well known that the interactions between individual agent goals can lead to inefficienc... | This paper proposes a (decentralized) method for online adjustment of agent incentives in multi-agent learning scenarios, as a means to obtain higher outcomes for each agent and for the group as a whole. The paper uses the “price of anarchy” (the worst value of an equilibrium divided by the best value in the game) as a... | SP:41b23082a1439aa8601439e27c9abaa33e06959c |
Representation and Bias in Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling | Inspired by the phenomenon of performance disparity between languages in machine translation , we investigate whether and to what extent languages are equally hard to “ conditional-language-model ” . Our goal is to improve our understanding and expectation of the relationship between language , data representation , si... | The paper investigates whether languages are equally hard to Conditional-Language-Model (CLM). To do this, the authors perform controlled experiments by modeling text from parallel data from 6 typologically diverse languages. They pair the languages and perform experiments in 30 directions with Transformers, and compar... | SP:87bda29654ffe25cda14e3b27a6e4b53e2a40164 |
Bractivate: Dendritic Branching in Medical Image Segmentation Neural Architecture Search | 1 INTRODUCTION Researchers manually composing neural networks must juggle multiple goals for their architectures . Architectures must make good decisions ; they must be fast , and they should work even with limited computational resources . These goals are challenging to achieve manually , and researchers often spend m... | The authors propose a neural architecture search (NAS) algorithm inspired by brain physiology. In particular, they propose a NAS algorithm based on neural dendritic branching, and apply it to three different segmentation tasks (namely cell nuclei, electron microscopy, and chest X-ray lung segmentation). The authors sha... | SP:0cab715d71a765b97066673f3a2d0e00d22ffa3c |
What to Prune and What Not to Prune at Initialization | 1 INTRODUCTION . Computational complexity and overfitting in neural networks is a well established problem Frankle & Carbin ( 2018 ) , Han et al . ( 2015 ) , LeCun et al . ( 1990 ) , Denil et al . ( 2013 ) . We utilize pruning approaches for the following two reasons : 1 ) To reduce the computational cost of a fully co... | The authors propose two approaches for pruning: (a) "Evolution-style": start with K random masks associated with the weights, update weights on gradient descent corresponding to those active in the “fittest” mask, and overtime throw away all but one masks which are less fit. (b) "Dissipating-gradients”: Here those weig... | SP:232edf223e799126992acd9ee04d88c22ff57110 |
Sparse Linear Networks with a Fixed Butterfly Structure: Theory and Practice | 1 INTRODUCTION . A butterfly network ( see Figure 6 in Appendix A ) is a layered graph connecting a layer of n inputs to a layer of n outputs withO ( log n ) layers , where each layer contains 2n edges . The edges connecting adjacent layers are organized in disjoint gadgets , each gadget connecting a pair of nodes in o... | The paper studies “butterfly networks”, where, a logarithmic number of linear layers with sparse connections resembling the butterfly structure of the FFT algorithm, along with linear layers in smaller dimensions are used to approximate linear layers in larger dimensions. In general, the paper follows the idea of sketc... | SP:bb0b99194e5d102320ca4cc7c89c4ae6ee514d83 |
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States | 1 INTRODUCTION . Most reinforcement learning ( RL ) algorithms scale polynomially with the size of the state space , which is inadequate for many real world applications . Consider for example a simple navigation task in a room with furniture where the set of furniture pieces and their locations change from episode to ... | The paper considers the problem of partitioning the atoms (e.g., pixels of an image) of a reinforcement learning task to latent states (e.g., a grid that determines whether there exists furniture in each cell). The number of states grows exponentially with the number of cells of the grid. So the algorithms that are pol... | SP:fb0eda1f20d9b0a63164e96a2bf9ab4bee365eea |
A Large-scale Study on Training Sample Memorization in Generative Modeling | Many recent developments on generative models for natural images have relied on heuristically-motivated metrics that can be easily gamed by memorizing a small sample from the true distribution or training a model directly to improve the metric . In this work , we critically evaluate the gameability of such metrics by r... | Motivated by the observation that prevalent metrics (Inception Score, Frechet Inception Distance) used to assess the quality of samples obtained from generative models are gameable (due to either the metric not correlating well with visually assessed sample quality or the metric being susceptible to training sample mem... | SP:5908636440ae0162f1bf98b6e7b8969cc163f9a6 |
Learning to Sample with Local and Global Contexts in Experience Replay Buffer | 1 INTRODUCTION . Experience replay ( Mnih et al. , 2015 ) , which is a memory that stores the past experiences to reuse them , has become a popular mechanism for reinforcement learning ( RL ) , since it stabilizes training and improves the sample efficiency . The success of various off-policy RL algorithms largely attr... | Observing that the existed ER-based sampling method may introduce bias or redundancy in sampled transitions, the paper proposes a new sampling method in the ER learning setting. The idea is to take into consideration the context, i.e. many visited transitions, rather than a single one, based on which one can measure th... | SP:9ce7a60c5f2e40f7d59e98c90171a7b49621c67c |
Anytime Sampling for Autoregressive Models via Ordered Autoencoding | 1 INTRODUCTION . Autoregressive models are a prominent approach to data generation , and have been widely used to produce high quality samples of images ( Oord et al. , 2016b ; Salimans et al. , 2017 ; Menick & Kalchbrenner , 2018 ) , audio ( Oord et al. , 2016a ) , video ( Kalchbrenner et al. , 2017 ) and text ( Kalch... | The paper considers the problem of slow sampling in autoregressive generative models. Sampling in such models is sequential, so its computational cost scales with the data dimensionality. Existing work speeds up autoregressive sampling by caching activations or distilling into normalizing flows with fast sampling. Auth... | SP:ca6ab92369346b3d457f575fc652333255f2dfec |
Explainable Deep One-Class Classification | 1 INTRODUCTION . Anomaly detection ( AD ) is the task of identifying anomalies in a corpus of data ( Edgeworth , 1887 ; Barnett and Lewis , 1994 ; Chandola et al. , 2009 ; Ruff et al. , 2021 ) . Powerful new anomaly detectors based on deep learning have made AD more effective and scalable to large , complex datasets su... | This paper presents a one-class classification method using a fully convolutional model and directly using the output map as an explanation map. The method is dubbed FCDD for fully convolutional data descriptor. FCDD uses a hypersphere classifier combined with a pseudo-Huber loss. FCDD is trained using outliers exposur... | SP:a4cda983cb5a670c3ad7054b9cd7797107af64b1 |
not-MIWAE: Deep Generative Modelling with Missing not at Random Data | 1 INTRODUCTION z x s θ φ γ N ( a ) PPCA not-MIWAE PPCA ( b ) Figure 1 : ( a ) Graphical model of the not-MIWAE . ( b ) Gaussian data with MNAR values . Dots are fully observed , partially observed data are displayed as black crosses . A contour of the true distribution is shown together with directions found by PPCA an... | This paper proposes an approach to training deep latent variable models on data that is missing not at random. To learn the parameters of deep latent variable models, the paper adopts importance-weighted variational inference techniques. Experiments on a variety of datasets show that the proposed approach is effective ... | SP:1d4d75e1bbb4e58273bc027f004aa986a587a6dd |
Sparse Gaussian Process Variational Autoencoders | 1 INTRODUCTION . Increasing amounts of large , multi-dimensional datasets that exhibit strong spatio-temporal dependencies are arising from a wealth of domains , including earth , social and environmental sciences ( Atluri et al. , 2018 ) . For example , consider modelling daily atmospheric measurements taken by weathe... | In this work generative models using a GP as prior and a deep network as likelihood (GP-DGMs) are considered. In the VAE formalism for inference, the novelty of this paper is located in the encoder: It is sparse and the posterior can be computed even when part of the observations are missing. Sparsity is obtained using... | SP:da630280f443afedfacaf7ad1abe20d97ebb60f2 |
End-to-End Egospheric Spatial Memory | 1 INTRODUCTION . Egocentric spatial memory is central to our understanding of spatial reasoning in biology ( Klatzky , 1998 ; Burgess , 2006 ) , where an embodied agent constantly carries with it a local map of its surrounding geometry . Such representations have particular significance for action selection and motor c... | The paper considers the problem of creating spatial memory representations, which play important roles in robotics and are crucial for real-world applications of intelligent agents. The paper proposes an ego-centric representation that stores depth values and features at each pixel in a panorama. Given the relative pos... | SP:30ceb5d450760e9954ac86f091fb97cb14a2d092 |
Training Invertible Linear Layers through Rank-One Perturbations | 1 INTRODUCTION Many deep learning applications depend critically on the neural network parameters having a certain mathematical structure . As an important example , reversible generative models rely on invertibility and , in the case of normalizing flows , efficient inversion and computation of the Jacobian determinan... | This paper introduces an algorithm for training neural networks in a way that parameters preserve a given property. The optimization is based on using a transformation R that perturbs parameters in a way that the desired property is preserved. Instead of directly optimizing the parameters of the network, the optimizat... | SP:0cde0537137f3eef6c9c0d6d580a610a07112a39 |
On Noise Injection in Generative Adversarial Networks | 1 INTRODUCTION . Noise injection is usually applied as regularization to cope with overfitting or facilitate generalization in neural networks ( Bishop , 1995 ; An , 1996 ) . The effectiveness of this simple technique has also been proved in various tasks in deep learning , such as learning deep architectures ( Hinton ... | To summarize, this paper proposed a new noise injection method that is easy to implement and is able to replace the original noise injection method in StyleGAN 2. The approach is supported by detailed theoretical analysis and impactful performance improvement on GAN training and inversion. The results show that they ar... | SP:6ba57dba7e320797ca311e5c7d6e55e130384df2 |
TRIP: Refining Image-to-Image Translation via Rival Preferences | 1 INTRODUCTION . Image-to-image ( I2I ) translation ( Isola et al. , 2017 ) aims to translate an input image into the desired ones with changes in some specific attributes . Current literature can be classified into two categories : binary translation ( Zhu et al. , 2017 ; Kim et al. , 2017 ) , e.g. , translating an im... | The authors proposed in this paper a supervised approach relying on given relative and quantitative attribute discrepancies. A UNet-like generator learns adversarially tends to generate realistic images while a "ranker" tends to predict the magnitude of the input parameter used to control the image manipulation. The co... | SP:bdbb12951868ea0864f926192fdbe2e62ecdb0e3 |
A Transformer-based Framework for Multivariate Time Series Representation Learning | 1 INTRODUCTION . Multivariate time series ( MTS ) are an important type of data that is ubiquitous in a wide variety of domains , including science , medicine , finance , engineering and industrial applications . Despite the recent abundance of MTS data in the much touted era of “ Big Data ” , the availability of label... | This paper aims to develop a transformer-based pre-trained model for multivariate time series representation learning. Specifically, the transformer’s encoder is only used and a time-series imputation task is constructed as their unsupervised learning objective. This is a bit similar to the BERT model in NLP. But autho... | SP:878a518cb77731b8b376d5fd82542670e195f0d6 |
Connecting Sphere Manifolds Hierarchically for Regularization | 1 INTRODUCTION . Applying inductive biases or prior knowledge to inference models is a popular strategy to improve their generalization performance ( Battaglia et al. , 2018 ) . For example , a hierarchical structure is found based on the similarity or shared characteristics between samples and thus becomes a basic cri... | In this paper, the authors proposed a novel reparameterization framework of the last network layer that takes semantic hierarchy into account. Specifically, the authors assume a predefined hierarchy graph, and model the classifier of child classes as a parent classifier plus offsets $\delta$ recursively. The authors sh... | SP:2fe9ca0b44e57587b94159cb8fa201f79c13db50 |
On the Role of Pre-training for Meta Few-Shot Learning | 1 INTRODUCTION . In recent years , deep learning methods have outperformed most of the traditional methods in supervised learning , especially in image classification . However , deep learning methods generally require lots of labeled data to achieve decent performance . Some applications , however , do not have the lu... | This paper investigates the role of pre-training as an initialization for meta-learning for few-shot classification. In particular, they look at the extent to which the pre-trained representations are disentangled with respect to the class labels. They hypothesize that this disentanglement property of those representat... | SP:cb6afa05735201fecf8106b77c2d0a883d5cd996 |
RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior | 1 INTRODUCTION . One of the most important unsupervised learning tasks is to learn the data distribution and build generative models . Over the past few years , various types of generative models have been proposed . Flow-based generative models are a particular family of generative models with tractable distributions ... | The paper proposes a method, named as RG-flow, which combines the ideas of Renormalization group (RG) and flow-based models. The RG is applied to separate signal statistics of different scales in the input distribution and flow-based idea represents each scale information in its latent variables with sparse prior distr... | SP:2cfe676c21709d69aa3bab1480440fda0a365c3f |
Learning from Demonstrations with Energy based Generative Adversarial Imitation Learning | 1 INTRODUCTION . Motivated by applying reinforcement learning algorithms into more realistic tasks , we find that most realistic environments can not feed an explicit reward signal back to the agent immediately . It becomes a bottleneck for traditional reinforcement learning methods to be applied into more realistic sc... | The authors propose a discriminator-based approach to inverse reinforcement learning (IRL). The discriminator function is trained to attain large values ("energy") on trajectories from the current policy and small values on trajectories from an expert policy. The current policy is then improved by using the negative di... | SP:2f7f3a043edf8bbe4164dc748c7fbfc7c7338a02 |
Local Search Algorithms for Rank-Constrained Convex Optimization | rank ( A ) ≤r∗ R ( A ) given a convex function R : Rm×n → R and a parameter r∗ . These algorithms consist of repeating two steps : ( a ) adding a new rank-1 matrix to A and ( b ) enforcing the rank constraint on A . We refine and improve the theoretical analysis of Shalev-Shwartz et al . ( 2011 ) , and show that if the... | This paper considers solving rank-constrained convex optimization. This is a fairly general problem that contains several special cases such as matrix completion and robust PCA. This paper presents a local search approach along with an interesting theoretical analysis of their approach. Furthermore, this paper provided... | SP:6b06c93bb2394dae7e4d6e76a8c134b6808a46e9 |
Semi-supervised learning by selective training with pseudo labels via confidence estimation | 1 INTRODUCTION . Semi-supervised learning ( SSL ) is a powerful technique to deliver a full potential of complex models , such as deep neural networks , by utilizing unlabeled data as well as labeled data to train the model . It is especially useful in some practical situations where obtaining labeled data is costly du... | The paper uses selective training with pseudo labels. Specifically, the method selects the pseudo-labeled data associated with small loss after performing the data augmentation, and then uses the selected data for training the model. Here, the model computes the confidence of the pseudo labels and then puts a thresh... | SP:9eeb3b40542889b8a8e196f126a11f80e177f031 |
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures | Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes , real-world settings . Currently , the quality of a model ’ s uncertainty is evaluated using point-prediction metrics such as negative log-likelihood or the Brier score on heldout da... | Paper provides an evaluation of the reliability of confidence levels of well known uncertainty quantification techniques in deep learning on classification and regression tasks. The question that the authors are trying to answer empirically is: when a model claims accuracy at a confidence level within a certain interv... | SP:d818bed28daccbda111c39cdc9d097b5755b3d89 |
Teaching with Commentaries | 1 INTRODUCTION . Training , regularising , and understanding complex neural network models is challenging . There remain central open questions on making training faster and more data-efficient ( Kornblith et al. , 2019 ; Raghu et al. , 2019a ; b ) , ensuring better generalisation ( Zhang et al. , 2016 ) and improving ... | This paper proposes a general framework for boosting CNNs performance on different tasks by using'commentary' to learn meta-information. The obtained meta-information can also be used for other purposes such as the mask of objects within spurious background and the similarities among classes. The commentary module woul... | SP:74f12645ba675ccd4217ebfc0579cb4232406009 |
Computational Separation Between Convolutional and Fully-Connected Networks | 1 INTRODUCTION . Convolutional neural networks ( LeCun et al. , 1998 ; Krizhevsky et al. , 2012 ) achieve state-of-the-art performance on every possible task in computer vision . However , while the empirical success of convolutional networks is indisputable , the advantage of using them is not well understood from a t... | It is well-known that neural networks (NN) perform very well in various areas and in particular if one looks at computer vision convolutional neural networks perform very well. Although convolutional neural networks (CNN) are limited in their architecture (since they only allow nearest-neighbour connections) compared t... | SP:19e2493d7bdb4be73c3b834affdb925201243aef |
Latent Convergent Cross Mapping | 1 INTRODUCTION . Inferring a right causal model of a physical phenomenon is at the heart of scientific inquiry . It is fundamental to how we understand the world around us and to predict the impact of future interventions ( Pearl , 2009 ) . Correctly inferring causal pathways helps us reason about a physical system , a... | This paper studies short, chaotic time series and uses the Taken's theorem to discover the causality between two time series. The main challenge is that for short time series, the delay embedding is not possible. Thus, the authors propose to fit a latent neural ODE and theoretically argue that they can use the Neural O... | SP:b7b4e29defc84ee37a5a4dcaf2d393363c153b52 |
Global Self-Attention Networks for Image Recognition | 1 INTRODUCTION . Self-attention is a mechanism in neural networks that focuses on modeling long-range dependencies . Its advantage in terms of establishing global dependencies over other mechanisms , e.g. , convolution and recurrence , has made it prevalent in modern deep learning . In computer vision , several recent ... | There have been multiple attempts to use self-attention in computer vision backbones for image classification and object detection. Most of these approaches either tried to combine convolution with global self-attention, or replace it completely with local self-attention operation. The proposed approach naturally combi... | SP:474e2b9be8a3ec69a48c4ccd04a7e390ebb96347 |
Randomized Automatic Differentiation | 1 INTRODUCTION . Deep neural networks have taken center stage as a powerful way to construct and train massivelyparametric machine learning ( ML ) models for supervised , unsupervised , and reinforcement learning tasks . There are many reasons for the resurgence of neural networks—large data sets , GPU numerical comput... | In the context of deep learning, back-propagation is stochastic in the sample level to attain bette efficiency than full-dataset gradient descent. The authors asked that, can we further randomize the gradient compute within each single minibatch / sample with the goal to achieve strong model accuracy. In modern deep le... | SP:bf70c9e16933774746d621a5b8475843e723ac24 |
A Simple Unified Information Regularization Framework for Multi-Source Domain Adaptation | 1 INTRODUCTION . Although a large number of studies have demonstrated the ability of deep neural networks to solve challenging tasks , the tasks solved by networks are mostly confined to a similar type or a single domain . One remaining challenge is the problem known as domain shift ( Gretton et al . ( 2009 ) ) , where... | This paper studies the multi-source domain adaptation problem. The authors examine the existing MDA solutions, i.e. using a domain discriminator for each source-target pair, and argue that the existing ones are likely to distribute the domain-discriminative information across multiple discriminators. By theoretically a... | SP:5b707bffe506d9556ffedbe49425c57d0e21c9fa |
Three Dimensional Reconstruction of Botanical Trees with Simulatable Geometry | 1 INTRODUCTION . Human-inhabited outdoor environments typically contain ground surfaces such as grass and roads , transportation vehicles such as cars and bikes , buildings and structures , and humans themselves , but are also typically intentionally populated by a large number of trees and shrubbery ; most of the moti... | This paper tackles the problem of geometrical and topological 3D reconstruction of a (botanical) tree using a drone-mounted stereo vision system and deep learning-based/aided tree branch image annotation procedures. This is an interesting computer vision 3D reconstruction task, which has important practical applicati... | SP:825132782872f2167abd5e45773bfdef83e4bb2e |
Target Training: Tricking Adversarial Attacks to Fail | 1 INTRODUCTION . Neural network classifiers are vulnerable to malicious adversarial samples that appear indistinguishable from original samples ( Szegedy et al. , 2013 ) , for example , an adversarial attack can make a traffic stop sign appear like a speed limit sign ( Eykholt et al. , 2018 ) to a classifier . An adver... | This paper addresses the task of adversarial defense, particularly against untargeted attack. It starts from the observation that these attacks mostly minimize the perturbation and the classification loss, and proposes a new training strategy named Target Training. The method duplicate training examples with a special ... | SP:8e3a07ed19e7b0c677aae1106da801d246f5aa0c |
Characterizing signal propagation to close the performance gap in unnormalized ResNets | Batch Normalization is a key component in almost all state-of-the-art image classifiers , but it also introduces practical challenges : it breaks the independence between training examples within a batch , can incur compute and memory overhead , and often results in unexpected bugs . Building on recent theoretical anal... | This paper proposes the signal propagation plot (spp) which is a tool for analyzing residual networks and analyzes ResNet with/without BN. Based on the investigation, the authors first provide ResNet results without normalization with the proposed scaled weight standardization. Furthermore, the authors provide a bunch ... | SP:8e2ac7405015f9d2d59c4a511df83d796ac00a9e |
Geometry-aware Instance-reweighted Adversarial Training | 1 INTRODUCTION . Crafted adversarial data can easily fool the standard-trained deep models by adding humanimperceptible noise to the natural data , which leads to the security issue in applications such as medicine , finance , and autonomous driving ( Szegedy et al. , 2014 ; Nguyen et al. , 2015 ) . To mitigate this is... | The paper focused on the sample importance in the adversarial training. The authors firstly revealed that over-parameterized deep models on natural data may have insufficient model capacity for adversarial data, because the training loss is hard to zero for adversarial training. Then, the authors argued that limited ca... | SP:206600e5bfcc9ccd494b82995a7898ae81a4e0bf |
Continual Lifelong Causal Effect Inference with Real World Evidence | 1 INTRODUCTION . Causal effect inference is a critical research topic across many domains , such as statistics , computer science , public policy , and economics . Randomized controlled trials ( RCT ) are usually considered as the gold-standard for causal effect inference , which randomly assigns participants into a tr... | This paper considers adopting continual learning on the problem of causal effect estimation. The paper combines methods and algorithms for storing feature representation and representative samples (herding algorithm), avoiding drifting feature representation when new data is learned (feature representation distillation... | SP:d729aacc2cd3f97011a04360a252ca7cb0489354 |
Global Node Attentions via Adaptive Spectral Filters | 1 INTRODUCTION . Graph neural networks ( GNNs ) have recently demonstrated great power in graph-related learning tasks , such as node classification ( Kipf & Welling , 2017 ) , link prediction ( Zhang & Chen , 2018 ) and graph classification ( Lee et al. , 2018 ) . Most GNNs follow a message-passing architecture where ... | In this paper, the authors study the problem of GCN for disassortative graphs. The authors proposed the GNAN method to allow attention on distant nodes indeed of limiting to local neighbors. The authors generalized the idea of graph wavelet with MLP to generate the attention score and utilized it to generate multiple a... | SP:864d98472c237daf2b227692c4765af9a89886cd |
Calibration of Neural Networks using Splines | 1 INTRODUCTION . Despite the success of modern neural networks they are shown to be poorly calibrated ( Guo et al . ( 2017 ) ) , which has led to a growing interest in the calibration of neural networks over the past few years ( Kull et al . ( 2019 ) ; Kumar et al . ( 2019 ; 2018 ) ; Müller et al . ( 2019 ) ) . Conside... | The paper presents a post-hoc calibration method for deep neural net classification. The method proposes to first reduces the well-known ECE score to a special case of the Kolmogorov-Smirnov (KS) test, and this way solves the dependency of ECE on the limiting binning assumption. The method proposes next to recalibrate ... | SP:28a5570540fa769396ee73c14c25ada9669dd95f |
ProxylessKD: Direct Knowledge Distillation with Inherited Classifier for Face Recognition | 1 INTRODUCTION . Knowledge Distillation ( KD ) is a process of transferring knowledge from a large model to a smaller one . This technique is widely used to enhance model performance in many machine learning tasks such as image classification ( Hinton et al. , 2015 ) , object detection ( Chen et al. , 2017b ) and speec... | This paper proposes ProxylessKD method from a novel perspective of knowledge distillation. Instead of minimizing the outputs of teacher and student models, ProxylessKD adopts a shared classifier for two models. The shared classifier yields better aligned embedding space, so the embeddings from teacher and student model... | SP:cdc407d403e1008ced29c7cda727db0d631cc966 |
Decomposing Mutual Information for Representation Learning | 1 INTRODUCTION . The ability to extract actionable information from data in the absence of explicit supervision seems to be a core prerequisite for building systems that can , for instance , learn from few data points or quickly make analogies and transfer to other tasks . Approaches to this problem include generative ... | This paper proposes a contrastive learning approach where one of the views, x, is converted into two subviews, x' and x'', and then separate InfoNCE style bounds constructed for each of I(x'';y) and I(x';y|x'') before being combined to form an overall training objective. Critically, the second of these is based on the... | SP:a15d5230fecc1dad8998905f17c82cf8e05c98d3 |
A Unified Bayesian Framework for Discriminative and Generative Continual Learning | 1 INTRODUCTION . Continual learning ( CL ) ( Ring , 1997 ; Parisi et al. , 2019 ) is the learning paradigm where a single model is subjected to a sequence of tasks . At any point of time , the model is expected to ( i ) make predictions for the tasks it has seen so far , ( ii ) if subjected to training data for a new t... | The paper proposes a continual learning framework based on Bayesian non-parametric approach. The hidden layer is modeled using Indian Buffet Process prior. The inference uses a structured mean-field approximation with a Gaussian family for the weights, and Beta-Bernoulli for the task-masks. The variational inference... | SP:70bed0f6f729c03edcb03678fca53e1d82fc06ab |
Into the Wild with AudioScope: Unsupervised Audio-Visual Separation of On-Screen Sounds | 1 INTRODUCTION . Audio-visual machine perception has been undergoing a renaissance in recent years driven by advances in large-scale deep learning . A motivating observation is the interplay in human perception between auditory and visual perception . We understand the world by parsing it into the objects that are the ... | This paper describes a system for separating "on-screen" sounds from "off-screen" sounds in an audio-visual task, meaning sounds that are associated with objects that are visible in a video versus not. It is trained to do this using mixture invariant training to separate synthetic mixtures of mixtures. It is evaluated ... | SP:d27e98774183ece8d82b87f1e7067bf2a28a4fca |
A Simple Sparse Denoising Layer for Robust Deep Learning | 1 INTRODUCTION . Deep neural networks have obtained a great success in many applications , including computer vision , reinforcement learning ( RL ) and natural language processing , etc . However , vanilla deep models are not robust to noise perturbations of the input . Even a small perturbation of input data would dr... | The paper is generally well presented. However, a main issue is that the optimization algorithms for the l0-norm regularized problems (Section 3.1.2 and Section 3.2) are not correctly presented. Specifically, in the algorithm development to solve the "Fix $\boldsymbol{R}$, optimize $\boldsymbol{Y}$" subproblem, it over... | SP:958f2aacb0790ffe7399fd918c023c7e4e4c314c |
Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes | We present the Additive Poisson Process ( APP ) , a novel framework that can model the higher-order interaction effects of the intensity functions in point processes using lower dimensional projections . Our model combines the techniques in information geometry to model higher-order interactions on a statistical manifo... | The paper under review proposes a new model for multi-dimensional temporal Point processes, allowing efficient estimation of high order interactions. This new model, called additive Poisson process, relies on a log-linear structure of the intensity function that is motivated thanks to the Kolmogorov-Arnold theorem. Suc... | SP:33673a515722e1d8288fd3014e7db507b7250b20 |
SyncTwin: Transparent Treatment Effect Estimation under Temporal Confounding | 1 INTRODUCTION . Estimating the causal individual treatment effect ( ITE ) on patient outcomes using observational data ( observational studies ) has become a promising alternative to clinical trials as large-scale electronic health records become increasingly available ( Booth & Tannock , 2014 ) . Figure 1 illustrates... | This paper provides an approach for treatment effect estimation when the observational data is longitudinal (with irregular time stamps) and consists of temporal confounding variables. The proposed method can be categorized under the matching methods, in which, in order to estimate the counterfactual outcomes, a subset... | SP:e6e46c0563e852189839b2f923788165800a0f17 |
PAC Confidence Predictions for Deep Neural Network Classifiers | 1 INTRODUCTION . Due to the recent success of machine learning , there has been increasing interest in using predictive models such as deep neural networks ( DNNs ) in safety-critical settings , such as robotics ( e.g. , obstacle detection ( Ren et al. , 2015 ) and forecasting ( Kitani et al. , 2012 ) ) and healthcare ... | This paper proposes a method for obtaining probably-approximately correct (PAC) predictions given a pre-trained classifier. The PAC intervals are connected to calibration, and take the form of confidence intervals given the bin a prediction falls in. They demonstrate and explore two use cases: applying this technique t... | SP:8997ab419d35acd51ef50ef6265e5c37c468a2ac |
Weak NAS Predictor Is All You Need | 1 INTRODUCTION . Neural Architecture Search ( NAS ) has become a central topic in recent years with great progress ( Liu et al. , 2018b ; Luo et al. , 2018 ; Wu et al. , 2019 ; Howard et al. , 2019 ; Ning et al. , 2020 ; Wei et al. , 2020 ; Luo et al. , 2018 ; Wen et al. , 2019 ; Chau et al. , 2020 ; Luo et al. , 2020 ... | of contribution: The authors propose an interesting approach to address the sample-efficiency issue in Neural Architecture Search (NAS). Compared to other existing predictor based methods, the approach distinguishes itself by progressive shrinking the search space. The paper correctly identifies the sampling is an impo... | SP:4c82d9d12ec6a9f171c4281739776da18bcc2906 |
R-GAP: Recursive Gradient Attack on Privacy | 1 INTRODUCTION . Distributed and federated learning have become common strategies for training neural networks without transferring data ( Jochems et al. , 2016 ; 2017 ; Konečný et al. , 2016 ; McMahan et al. , 2017 ) . Instead , model updates , often in the form of gradients , are exchanged between participating nod... | This paper studies the problem of gradient attack in deep learning models. In particular, this paper tries to form a system of linear equations to find a training data point when the gradient of the deep learning model with respect to that data point is available. The algorithm for finding the data point is called R-G... | SP:720f167592297c58d88272599fb66978f3ae8001 |
Lipschitz Recurrent Neural Networks | 1 INTRODUCTION . Many interesting problems exhibit temporal structures that can be modeled with recurrent neural networks ( RNNs ) , including problems in robotics , system identification , natural language processing , and machine learning control . In contrast to feed-forward neural networks , RNNs consist of one or ... | Considering a continuous time RNN with Lipschitz-continuous nonlinearity, the authors formulate sufficient conditions on the parameter matrices for the network to be globally stable, in the sense of a globally attracting fixed point. They provide a specific parameterization for the hidden-to-hidden weight matrices to c... | SP:6cf84af3e1ae0c84dc251ba41a5acb3dc7f61645 |
EEC: Learning to Encode and Regenerate Images for Continual Learning | 1 INTRODUCTION . Humans continue to learn new concepts over their lifetime without the need to relearn most previous concepts . Modern machine learning systems , however , require the complete training data to be available at one time ( batch learning ) ( Girshick , 2015 ) . In this paper , we consider the problem of c... | In continual learning settings, one of the important technique for avoiding catastrophe forgetting is to replay data points from the past. For memory efficiency purposes, representative samples can be generated from a generative model, such as GANs, rather than replaying the original samples which can be large in numbe... | SP:2ad12575818f72f453eb0c04c953a48be56e80e3 |
Using latent space regression to analyze and leverage compositionality in GANs | 1 INTRODUCTION . Natural scenes are comprised of disparate parts and objects that humans can easily segment and interchange ( Biederman , 1987 ) . Recently , unconditional generative adversarial networks ( Karras et al. , 2017 ; 2019b ; a ; Radford et al. , 2015 ) have become capable of mimicking the complexity of natu... | In this paper, the authors propose a latent space regression method for analyzing and manipulating the latent space of pre-trained GAN models. Unlike existing optimization-based methods, an explicit latent code regressor is learned to map the input to the latent space. The authors apply this approach to several applica... | SP:da8ca392a4eb366f4fdedb09d461ef804615b0b2 |
A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention | 1 INTRODUCTION . Many scientific fields such as bioinformatics or natural language processing ( NLP ) require processing sets of features with positional information ( biological sequences , or sentences represented by a set of local features ) . These objects are delicate to manipulate due to varying lengths and poten... | The authors propose a new way to aggregate the embeddings of elements in a set (or sequence) by comparing it with respect to (trainable) reference set(s) via Optimal Transport (OT). The motivation to build such a pooling operation is derived from self-attention and the authors suggest an OT spin to that (e.g., the diff... | SP:c0e827c33dbc9378404fe2a0949198cb74f13688 |
Combining Imitation and Reinforcement Learning with Free Energy Principle | 1 INTRODUCTION . Imitation Learning ( IL ) is a framework to learn a policy to mimic expert trajectories . As the expert specifies model behaviors , there is no need to do exploration or to design complex reward functions . Reinforcement Learning ( RL ) does not have these features , so RL agents have no clue to realiz... | This paper extends and explains how to apply the "free energy principle" and active inference to RL and imitation learning. They implement a neural network approximation of losses derived this way and test on some control tasks. Importantly the tasks focus on here are imitation + control tasks. That is, there is both a... | SP:a85b6d598513c8e03a013fd20da6b19a1108f71e |
Investigating and Simplifying Masking-based Saliency Methods for Model Interpretability | 1 INTRODUCTION . The success of CNNs ( Krizhevsky et al. , 2012 ; Szegedy et al. , 2015 ; He et al. , 2016 ; Tan & Le , 2019 ) has prompted interest in improving understanding of how these models make their predictions . Particularly in applications such as medical diagnosis , having models explain their predictions ca... | By the first look, this work itself does not introduce any new architecture or novel algorithm. It takes what is considered as the popular choices in generating classifier saliency masks, and conducts quite extensive sets of experiments to dissect the components by their importance. The writing is pretty clear in narra... | SP:69855e0bec141e9d15eec5cc37022f313e6600b2 |
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks | 1 INTRODUCTION . The conventional performance measure of accuracy for image classification treats all classes other than ground truth as equally wrong . However , some mistakes may have a much higher impact than others in real-world applications . An intuitive example being an autonomous vehicle mistaking a car for a b... | The authors propose a model to improve the output distribution of neural nets in image classification problems. Their model is a post hoc procedure and is based on the tree structure of WordNet. The model revises the classifier output based on the distance of the labels in the tree. Intuitively, their solution is to pi... | SP:e4e5b4e2bee43c920ed719dc331a370129845268 |
Toward Trainability of Quantum Neural Networks | 1 INTRODUCTION . Neural Networks ( Hecht-Nielsen , 1992 ) using gradient-based optimizations have dramatically advanced researches in discriminative models , generative models , and reinforcement learning . To efficiently utilize the parameters and practically improve the trainability , neural networks with specific ar... | The design of a useful generalization of neural networks on quantum computers has been challenging because the gradient signal will decay exponentially with respect to the depth of the quantum circuit (saturating to exponentially small in system size after the depth is linear in system size). This work provides a detai... | SP:7cc59c8f556d03597f7ab391ef14d1a96191a4db |
Solving Min-Max Optimization with Hidden Structure via Gradient Descent Ascent | 1 Introduction . Traditionally , our understanding of convex-concave games revolves around von Neumann ’ s celebrated minimax theorem , which implies the existence of saddle point solutions with a uniquely defined value . These solutions are called von Nemann solutions and guarantee to each agent their corresponding va... | In this paper, the authors introduce a class of games called Hidden Convex-Concave where a (stricly) convex-concave potential is composed with smooth maps. On this class of problems, they show that the continuous gradient dynamics converge to (a neighbordhood of) the minimax solutions of the problem. This is an explora... | SP:8a8aa5f245c2fb82beddb19c82dddb8d67f66f8a |
Predicting the Outputs of Finite Networks Trained with Noisy Gradients | 1 INTRODUCTION . Deep neural networks ( DNNs ) have been rapidly advancing the state-of-the-art in machine learning , yet a complete analytic theory remains elusive . Recently , several exact results were obtained in the highly over-parameterized regime ( N →∞ where N denotes the width or number of channels for fully c... | This paper shows a correspondence between deep neural networks (DNN) trained with noisy gradients and NNGP. It provides a general analytical form for the finite width correction (FWC) for NNSP expanding around NNGP. Finally, it argues that this FWC can be used to explain why finite width CNNs can improve the performanc... | SP:5e9b5c3ee27cf90eb73e2672a1bbf18a1b12e791 |
ON NEURAL NETWORK GENERALIZATION VIA PROMOTING WITHIN-LAYER ACTIVATION DIVERSITY | 1 INTRODUCTION . Neural networks are a powerful class of non-linear function approximators that have been successfully used to tackle a wide range of problems . They have enabled breakthroughs in many tasks , such as image classification ( Krizhevsky et al. , 2012 ) , speech recognition ( Hinton et al. , 2012a ) , and ... | This paper proposes adding regularization terms to encourage diversity of the layer outputs in order to improve the generalization performance. The proposed idea is an extension of Cogswell's work with different regularization terms. In addition, the authors performed detailed generalization analysis based on the Radem... | SP:95899f38fd0f1789510e67178b587c08a14203f5 |
Neural ODE Processes | 1 INTRODUCTION . Many time-series that arise in the natural world , such as the state of a harmonic oscillator , the populations in an ecological network or the spread of a disease , are the product of some underlying dynamics . Sometimes , as in the case of a video of a swinging pendulum , these dynamics are latent an... | The proposed NDP has two main advantages: 1- it has the capability to adapt the incoming data points in time-series (unlike NODE) without retraining, 2- it can provide a measure of uncertainty for the underlying dynamics of the time-series. NDP partitions the global latent context $z$ to a latent position $l$ and sub-c... | SP:4fd499ebe9fddb6a3f57663d76bb7bf3b5f29ef7 |
Dataset Meta-Learning from Kernel Ridge-Regression | One of the most fundamental aspects of any machine learning algorithm is the training data used by the algorithm . We introduce the novel concept of - approximation of datasets , obtaining datasets which are much smaller than or are significant corruptions of the original training data while maintaining similar model p... | This paper proposes a data-driven approach to choose an informative surrogate sub-dataset, termed "a \epsilon-approximation", from the original data set. A meta-learning algorithm called Kernel Inducing Points (KIP ) is proposed to obtain such sub-datasets for (Linear) Kernel Ridge Regression (KRR), with the potential... | SP:1c2c08605956eb4660a8f8a33ce13e80276582ed |
Status-Quo Policy Gradient in Multi-agent Reinforcement Learning | 1 INTRODUCTION . In sequential social dilemmas , individually rational behavior leads to outcomes that are sub-optimal for each individual in the group ( Hardin , 1968 ; Ostrom , 1990 ; Ostrom et al. , 1999 ; Dietz et al. , 2003 ) . Current state-of-the-art Multi-Agent Deep Reinforcement Learning ( MARL ) methods that ... | This paper focuses on the problem of multi-agent cooperation in social dilemmas, in which mutual defection is individually rational but collectively suboptimal. The authors use the bias toward status-quo in human psychology to motivate a new training method, called SQLoss: 1) for repeated matrix games, each agent is tr... | SP:c06539b9986064977dec933dcce4b81d42f47cc2 |
WaveGrad: Estimating Gradients for Waveform Generation | 1 INTRODUCTION . Deep generative models have revolutionized speech synthesis ( Oord et al. , 2016 ; Sotelo et al. , 2017 ; Wang et al. , 2017 ; Biadsy et al. , 2019 ; Jia et al. , 2019 ; Vasquez & Lewis , 2019 ) . Autoregressive models , in particular , have been popular for raw audio generation thanks to their tractab... | The work uses diffusion probabilistic models for conditional speech synthesis tasks, specifically to convert mel-spectrogram to the raw audio waveform. Results from the proposed approach match the state-of-the-art WaveRNN model. The paper is very well-written and it is quite easy to follow. The study of the total numbe... | SP:72f379cefb57913386cbd76978943bdc8d0545a7 |
HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark | 1 INTRODUCTION . The recent performance breakthroughs of deep neural networks ( DNNs ) have attracted an explosion of research in designing efficient DNNs , aiming to bring powerful yet power-hungry DNNs into more resource-constrained daily life devices for enabling various DNN-powered intelligent functions ( Ross , 20... | The paper presents a benchmark / dataset, HW-NAS-Bench, for evaluating various neural architecture search algorithms. The benchmark is based on extensive measurements on real hardware. An important goal with the proposal is to support neural architecture searches for non-hardware experts. Further, the paper provides a ... | SP:11cd869cd8c6dc657c136545fd2029f0c49843ba |
Data-driven Learning of Geometric Scattering Networks | 1 INTRODUCTION . Geometric deep learning has recently emerged as an increasingly prominent branch of machine learning in general , and deep learning in particular ( Bronstein et al. , 2017 ) . It is based on the observation that many of the impressive achievements of neural networks come in applications where the data ... | This paper proposes a novel graph neural network-based architecture. Building upon the theoretical success of graph scattering transforms, the authors propose to learn some aspects of it providing them with more flexibility to adapt to data (recall that graph scattering transforms are built on pre-designed graph wavele... | SP:f65217b47950d0dbf8e77622489d8883211a012d |
DiffWave: A Versatile Diffusion Model for Audio Synthesis | 1 INTRODUCTION . Deep generative models have produced high-fidelity raw audio in speech synthesis and music generation . In previous work , likelihood-based models , including autoregressive models ( van den Oord et al. , 2016 ; Kalchbrenner et al. , 2018 ; Mehri et al. , 2017 ) and flow-based models ( Prenger et al. ,... | This paper describes a neural vocoder based on a diffusion probabilistic model. The model utilizes a fixed-length markov chain to convert between a latent uncorrelated Gaussian vector and a full-length observation. The conversion from observation to latent is fixed and amounts to adding noise at each step. The conversi... | SP:c90a894d965bf8e529df296b9d5c76864aa5f4f9 |
Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning | 1 INTRODUCTION . Arguably , dancing to music is one of human ’ s innate abilities , as we can spontaneously sway along with the tempo of music we hear . The research in neuropsychology indicates that our brain is hardwired to make us move and synchronize with music regardless of our intention ( Chen et al. , 2008 ) . A... | The authors present a seq2seq model with a sparse transformer encoder and an LSTM decoder. They utilize a learning curriculum wherein the autoregressive decoder is initially trained using teacher forcing and is gradually fed its past predictions as training progresses. The authors introduce a new dataset for long term ... | SP:efbb0e2e944f1d810a6f0b6bc71e636af9ae9c13 |
Block Skim Transformer for Efficient Question Answering | Transformer-based encoder models have achieved promising results on natural language processing ( NLP ) tasks including question answering ( QA ) . Different from sequence classification or language modeling tasks , hidden states at all positions are used for the final classification in QA . However , we do not always ... | This paper presents the "Block Skim Transformer" for extractive question answering tasks. The key idea in this model is using a classifier, on the self-attention distributions of a particular layer, to classify whether a large spans of non-contiguous text (blocks) contain the answer. If a block is rejected by the class... | SP:18e9f58ab4fc8532cbd298730cff5b7f8ec31a5f |
Predicting Classification Accuracy When Adding New Unobserved Classes | 1 INTRODUCTION . Advances in machine learning and representation learning led to automatic systems that can identify an individual class from very large candidate sets . Examples are abundant in visual object recognition ( Russakovsky et al. , 2015 ; Simonyan & Zisserman , 2014 ) , face identification ( Liu et al. , 20... | The authors discuss how a classifier’s performance over the initial class sample can be used to extrapolate its expected accuracy on a larger, unobserved set of classes by mean of the dual of the ROC function, swapping the roles of classes and samples. Grounded on such function, the authors develop a novel ANN approach... | SP:977fc8d3bb7266d1beaecc609a91970783347ed3 |
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition | 1 INTRODUCTION . Over the last few years , video action recognition has made rapid progress with the introduction of a number of large-scale video datasets ( Carreira & Zisserman , 2017 ; Monfort et al. , 2018 ; Goyal et al. , 2017 ) . Despite impressive results on commonly used benchmark datasets , efficiency remains ... | The paper presented an adaptive inference model for efficient action recognition in videos. The core of the model is the dynamic gating of feature channels that controls the fusion between two frame features, whereby the gating is conditioned on the input video and helps to reduce the computational cost at runtime. The... | SP:eb5f64c7d1e303394f4650a14806e60dba1afdd3 |
On the Geometry of Deep Bayesian Active Learning | 1 INTRODUCTION . Lack of training labels restricts the performance of deep neural networks ( DNNs ) , though prices of GPU resources were falling fast . Recently , leveraging the abundance of unlabeled data has become a potential solution to relieve this bottleneck whereby expert knowledge is involved to annotate those... | This paper is basically unreadable. The sentence structure / grammar is strange, and if that was the only issue it could be overlooked. The paper also does not describe or explain the motivation and interpretation of anything, but instead just lists equations. For example, eta is the parameter that projects a spherical... | SP:8b0cee077c1bcdf9a546698dc041654ca6a222ed |
Directional graph networks | 1 INTRODUCTION . One of the most important distinctions between convolutional neural networks ( CNNs ) and graph neural networks ( GNNs ) is that CNNs allow for any convolutional kernel , while most GNN methods are limited to symmetric kernels ( also called isotropic kernels in the literature ) ( Kipf & Welling , 2016 ... | The authors propose a convolution as a message passing of node features over edges where messages are aggregated weighted by a "direction" edge field. Furthermore, the authors propose to use the gradients of Laplace eigenfunctions as direction fields. Presumably, the aggregation is done with different direction fields ... | SP:09bbd1a342033a65e751a8878c23e3fa6facc636 |
Signal Coding and Reconstruction using Spike Trains | In many animal sensory pathways , the transformation from external stimuli to spike trains is essentially deterministic . In this context , a new mathematical framework for coding and reconstruction , based on a biologically plausible model of the spiking neuron , is presented . The framework considers encoding of a si... | The authors describe a method for representing a continuous signal by a pulse code, in a manner inspired by auditory processing in the brain. The resulting framework is somewhat like matching pursuit except that filters are run a single time in a causal manner to find the spike times (which would be faster than MP), an... | SP:540d8c615b5193239aa43717de8cacc749ccc4c6 |
Improved Contrastive Divergence Training of Energy Based Models | 1 INTRODUCTION . Energy-Based models ( EBMs ) have received an influx of interest recently and have been applied to realistic image generation ( Han et al. , 2019 ; Du & Mordatch , 2019 ) , 3D shapes synthesis ( Xie et al. , 2018b ) , out of distribution and adversarial robustness ( Lee et al. , 2018 ; Du & Mordatch , ... | Review: This paper studies how to improve contrastive divergence (CD) training of energy-based models (EBMs) by revisiting the gradient term neglected in the traditional CD learning. This paper also introduces some useful techniques, such as data augmentation, multi-scale energy design, and reservoir sampling to improv... | SP:725d036c0863e59f6bb0b0bb22cc0ad3a0988126 |
Efficient Architecture Search for Continual Learning | 1 INTRODUCTION . Continual learning , or lifelong learning , refers to the ability of continually learning new tasks and also performing well on learned tasks . It has attracted enormous attention in AI as it mimics a human learning process - constantly acquiring and accumulating knowledge throughout their lifetime ( P... | This paper falls into a class of continual learning methods which accommodate for new tasks by expanding the network architecture, while freezing existing weights. This freezing trivially resolves forgetting. The (hard) problem of determining how to expand the network is tackled with reinforcement learning, largely bui... | SP:6d6e083899bc17a2733aa16efd259ad4ed2076d6 |
Play to Grade: Grading Interactive Coding Games as Classifying Markov Decision Process | 1 INTRODUCTION . The rise of online coding education platforms accelerates the trend to democratize high quality computer science education for millions of students each year . Corbett ( 2001 ) suggests that providing feedback to students can have an enormous impact on efficiently and effectively helping students learn... | The authors contribute an approach to automatically distinguish between good and bad student assignment submissions by modeling the assignment submissions as MDPs. The authors hypothesize that satisfactory assignments modeled as MDPs will be more alike than they are to unsatisfactory assignments. Therefore this can pot... | SP:047761908963bea6350f5d65a253c09f1a626093 |
Hybrid and Non-Uniform DNN quantization methods using Retro Synthesis data for efficient inference | 1 INTRODUCTION . Quantization is a widely used and necessary approach to convert heavy Deep Neural Network ( DNN ) models in Floating Point ( FP32 ) format to a light-weight lower precision format , compatible with edge device inference . The introduction of lower precision computing hardware like Qualcomm Hexagon DSP ... | This paper considers the problem of data-free post-training quantization of classfication networks. It proposes three extensions of an existing framework ZeroQ (Cai et al., 2020): (1). in order to generate distilled data for network sensitivity analysis, the "Retro Synthesis" method is proposed to turn a random image i... | SP:2eed06887f51560197590d617b1a37ec6d22e943 |
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers | 1 INTRODUCTION . The goal of a generalization theory in supervised learning is to understand when and why trained models have small test error . The classical framework of generalization decomposes the test error of a model ft as : TestError ( ft ) = TrainError ( ft ) + [ TestError ( ft ) − TrainError ( ft ) ] ︸ ︷︷ ︸ G... | The authors propose a bootstrap framework for understanding generalization in deep learning. In particular, instead of the usual decomposition of test error as training error plus the generalization gap, the bootstrap framework decomposes the empirical test error as online error plus the bootstrap error (the gap betwe... | SP:259b64e62b640ccba4bc82c50e59db7662677e6b |
Self-Supervised Time Series Representation Learning by Inter-Intra Relational Reasoning | 1 INTRODUCTION . Time series data is ubiquitous and there has been significant progress for time series analysis ( Das , 1994 ) in machine learning , signal processing , and other related areas , with many real-world applications such as healthcare ( Stevner et al. , 2019 ) , industrial diagnosis ( Kang et al. , 2015 )... | This paper presents a general Self-supervised Time Series representation learning framework. It explores the inter-sample relation reasoning and intra-temporal relation reasoning of time series to capture the underlying structure pattern of the unlabeled time series data. The proposed method achieves new state-of-the-... | SP:1b984693f1a64c86306aff37d58f9ff188bcf67e |
Reviving Autoencoder Pretraining | 1 INTRODUCTION . While approaches such as greedy layer-wise autoencoder pretraining ( Bengio et al. , 2007 ; Vincent et al. , 2010 ; Erhan et al. , 2010 ) arguably paved the way for many fundamental concepts of today ’ s methodologies in deep learning , the pressing need for pretraining neural networks has been diminis... | This paper proposes to use orthogonal weight constraints for autoencoders. The authors demonstrate that under orthogonal weights (hence invertible), more features could be extracted. The theory is conducted under linear cases while the authors claim it can be applied to more complicated scenarios such as higher dimensi... | SP:9513f146a764d9e67b7d054692d0a923622ff007 |
GENERATIVE MODEL-ENHANCED HUMAN MOTION PREDICTION | 1 INTRODUCTION . Human motion is naturally intelligible as a time-varying graph of connected joints constrained by locomotor anatomy and physiology . Its prediction allows the anticipation of actions with applications across healthcare ( Geertsema et al. , 2018 ; Kakar et al. , 2005 ) , physical rehabilitation and trai... | This paper raises and studies concerns about the generalization of 3D human motion prediction approaches across unseen motion categories. The authors address this problem by augmenting existing architectures with a VAE framework. More precisely, an encoder network that is responsible for summarizing the seed sequence i... | SP:70fc08b1b6161c770b5019272c2eaa0d2e3c39ee |
Learning Latent Topology for Graph Matching | 1 INTRODUCTION . Being a long standing NP-hard problem ( Loiola et al. , 2007 ) , graph matching ( GM ) has received persistent attention from the machine learning and optimization communities for many years . Concretely , for two graphs with n nodes for each , graph matching seeks to solve1 : max z z > Mz s.t . Z ∈ { ... | The authors address the problem of discrete keypoint matching. For an input pair of images, the task is to match the unannotated (but given as part of the input) keypoints. The main contribution is identifying the bottleneck of the current SOTA algorithm: a fixed connectivity construction given by Delauney triangulatio... | SP:8f1c7fabe235bdf095007948007509102dd0c126 |
Intervention Generative Adversarial Nets | 1 INTRODUCTION . As one of the most important advances in generative models in recent years , Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) have been attracting great attention in the machine learning community . GANs aim to train a generator network that transforms simple vectors of noise to pr... | The paper proposes a method for stabilizing the training of GAN as well as overcoming the problem of mode collapse by optimizing several auxiliary models. The first step is to learn a latent space using an autoencoder. Then, this latent space is "intervened" by a predefined set of $K$ transformations to generate a set ... | SP:879ce870f09e422aced7d008abc42fe5a8db29bc |
Uniform Manifold Approximation with Two-phase Optimization | 1 INTRODUCTION . We present a novel dimensionality reduction method , Uniform Manifold Approximation with Twophase Optimization ( UMATO ) to obtain less biased and robust embedding over diverse initialization methods . One effective way of understanding high-dimensional data in various domains is to reduce its dimensio... | This work proposed a dimensionality reduction algorithm called Uniform Manifold Approximation with Two-phase Optimization (UMATO), which is an improved version of UMAP (Ref. [3] see below). UMATO has a two-phase optimization approach: global optimization to obtain the overall skeleton of data & local optimization to id... | SP:a9c70bdca13ee3800c633589a6ee028701e5bf51 |
A Reduction Approach to Constrained Reinforcement Learning | 1 INTRODUCTION . Contemporary approaches in reinforcement learning ( RL ) largely focus on optimizing the behavior of an agent against a single reward function . RL algorithms like value function methods ( Zou et al. , 2019 ; Zheng et al. , 2018 ) or policy optimization methods ( Chen et al. , 2019 ; Zhao et al. , 2017... | This paper presents a reduction approach to tackle the optimization problem of constrained RL. They propose a Frank-Wolfe type algorithm for the task, which avoids many shortcomings of previous methods, such as the memory complexity. They prove that their algorithm can find an $\epsilon$-approximate solution with $O(1/... | SP:fd70696898c5c725ad789565265274a37a6c2ca0 |
Learnable Uncertainty under Laplace Approximations | Laplace approximations are classic , computationally lightweight means for constructing Bayesian neural networks ( BNNs ) . As in other approximate BNNs , one can not necessarily expect the induced predictive uncertainty to be calibrated . Here we develop a formalism to explicitly “ train ” the uncertainty in a decoupl... | The paper proposes a post-hoc uncertainty tuning pipeline for Bayesian neural networks. After getting the point estimate, it adds extra dimensions to the weight matrices and hidden layers, which has no effect on the network output, with the hope that it would influence the variance of the original network weights under... | SP:df5fec4899d97f7d5df259a013f467e038895669 |
Selfish Sparse RNN Training | 1 INTRODUCTION . Recurrent neural networks ( RNNs ) ( Elman , 1990 ) , with a variant of long short-term memory ( LSTM ) ( Hochreiter & Schmidhuber , 1997 ) , have been highly successful in various fields , including language modeling ( Mikolov et al. , 2010 ) , machine translation ( Kalchbrenner & Blunsom , 2013 ) , q... | In this paper, the authors studied the possibility of sparsity exploration in Recurrent Neural Networks (RNNs) training. The main contributions include two parts: (1) Selfish-RNN training algorithm in Section 3.1 (2) SNT-ASGD optimizer in Section 3.2. The key idea of the Selfish-RNN training algorithm is a non-uniform ... | SP:2a2368b5bc6b59f66af75ea37f4cbc19c8fcf50f |
Adaptive Spatial-Temporal Inception Graph Convolutional Networks for Multi-step Spatial-Temporal Network Data Forecasting | 1 INTRODUCTION . Spatial-temporal data forecasting has attracted attention from researchers due to its wide range of applications and the same specific characteristics of spatial-temporal data . Typical applications include mobile traffic forecast ( He et al. , 2019 ) , traffic road condition forecast ( Song et al. , 2... | This paper proposes a spatial-temporal graph neural network, which is designed to adaptively capture the complex spatial-temporal dependency. Further, the authors design a spatial-temporal attention module, which aims to capture multi-scale correlations. For multi-step prediction instead of one-step prediction, they fu... | SP:60d704b4a1555e24c09963617c879a15d8f3c805 |
The Recurrent Neural Tangent Kernel | 1 INTRODUCTION . The overparameterization of modern deep neural networks ( DNNs ) has resulted in not only remarkably good generalization performance on unseen data ( Novak et al. , 2018 ; Neyshabur et al. , 2019 ; Belkin et al. , 2019 ) but also guarantees that gradient descent learning can find the global minimum of ... | This paper extends NTK to RNN to explain behavior of RNNs in overparametrized case. It’s a good extension study and interesting to see RNN with infinite-width limit converges to a kernel. The paper proves the same RNTK formula when the weights are shared and not shared. The proposed sensitivity for computationally frie... | SP:a99af0f9e848f4f9068ad407612745a85a262644 |
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