
Can Shallow Neural Networks Beat the Curse of Dimensionality? A mean field training perspective
We prove that the gradient descent training of a twolayer neural networ...
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A Meanfield Analysis of Deep ResNet and Beyond: Towards Provable Optimization Via Overparameterization From Depth
Training deep neural networks with stochastic gradient descent (SGD) can...
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Machine Intelligence at the Edge with Learning Centric Power Allocation
While machinetype communication (MTC) devices generate considerable amo...
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A Theoretical Analysis of Contrastive Unsupervised Representation Learning
Recent empirical works have successfully used unlabeled data to learn fe...
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Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
This paper presents a general approach for opendomain question answerin...
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Dreaming to Distill: Datafree Knowledge Transfer via DeepInversion
We introduce DeepInversion, a new method for synthesizing images from th...
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A Comparative Analysis of the Optimization and Generalization Property of Twolayer Neural Network and Random Feature Models Under Gradient Descent Dynamics
A fairly comprehensive analysis is presented for the gradient descent dy...
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Kolmogorov Width Decay and Poor Approximators in Machine Learning: Shallow Neural Networks, Random Feature Models and Neural Tangent Kernels
We establish a scale separation of Kolmogorov width type between subspac...
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Theoretical Analysis of Auto RateTuning by Batch Normalization
Batch Normalization (BN) has become a cornerstone of deep learning acros...
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DeepV2D: Video to Depth with Differentiable Structure from Motion
We propose DeepV2D, an endtoend differentiable deep learning architect...
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Better the Devil you Know: An Analysis of Evasion Attacks using OutofDistribution Adversarial Examples
A large body of recent work has investigated the phenomenon of evasion a...
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FineGrained Analysis of Optimization and Generalization for Overparameterized TwoLayer Neural Networks
Recent works have cast some light on the mystery of why deep nets fit an...
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Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Many machine learning problems can be framed in the context of estimatin...
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Second Order Optimization Made Practical
Optimization in machine learning, both theoretical and applied, is prese...
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Analysis of the Gradient Descent Algorithm for a Deep Neural Network Model with Skipconnections
The behavior of the gradient descent (GD) algorithm is analyzed for a de...
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The Nonstochastic Control Problem
We consider the problem of controlling an unknown linear dynamical syste...
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Tracking and Improving Information in the Service of Fairness
As algorithmic prediction systems have become widespread, fears that the...
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Physically Realizable Adversarial Examples for LiDAR Object Detection
Modern autonomous driving systems rely heavily on deep learning models t...
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Poisoning Attacks with Generative Adversarial Nets
Machine learning algorithms are vulnerable to poisoning attacks: An adve...
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HaarPooling: Graph Pooling with Compressive Haar Basis
Deep Graph Neural Networks (GNNs) are instrumental in graph classificati...
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DiabDeep: Pervasive Diabetes Diagnosis based on Wearable Medical Sensors and Efficient Neural Networks
Diabetes impacts the quality of life of millions of people. However, dia...
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Nonparametric Deconvolution Models
We describe nonparametric deconvolution models (NDMs), a family of Bayes...
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RDPGAN: A RényiDifferential Privacy based Generative Adversarial Network
Generative adversarial network (GAN) has attracted increasing attention ...
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A Mathematical Model for Linguistic Universals
Inspired by chemical kinetics and neurobiology, we propose a mathematica...
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Solving Discounted Stochastic TwoPlayer Games with NearOptimal Time and Sample Complexity
In this paper, we settle the sampling complexity of solving discounted t...
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Machine learning based nonNewtonian fluid model with molecular fidelity
We introduce a machinelearningbased framework for constructing continu...
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D3D: Distilled 3D Networks for Video Action Recognition
Stateoftheart methods for video action recognition commonly use an en...
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Annealing for Distributed Global Optimization
The paper proves convergence to global optima for a class of distributed...
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BioInspired Hashing for Unsupervised Similarity Search
The fruit fly Drosophila's olfactory circuit has inspired a new locality...
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Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks
Latent dynamics discovery is challenging in extracting complex dynamics ...
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Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy
Computer vision technology is being used by many but remains representat...
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Path Integral Based Convolution and Pooling for Graph Neural Networks
Graph neural networks (GNNs) extends the functionality of traditional ne...
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Online Control with Adversarial Disturbances
We study the control of a linear dynamical system with adversarial distu...
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On the Utility of Learning about Humans for HumanAI Coordination
While we would like agents that can coordinate with humans, current algo...
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SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Standard variational lower bounds used to train latent variable models p...
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Distributed Learning: Sequential Decision Making in ResourceConstrained Environments
We study costeffective communication strategies that can be used to imp...
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Inherent Noise in Gradient Based Methods
Previous work has examined the ability of larger capacity neural network...
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Modeling the Gaia ColorMagnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates
We demonstrate an algorithm for learning a flexible colormagnitude diag...
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The Efficiency of Human Cognition Reflects Planned Information Processing
Planning is useful. It lets people take actions that have desirable long...
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CornerNet: Detecting Objects as Paired Keypoints
We propose CornerNet, a new approach to object detection where we detect...
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On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
Adaptive gradient methods are workhorses in deep learning. However, the ...
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Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks
Bayesian neural networks (BNNs) hold great promise as a flexible and pri...
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Steerable ePCA
In photonlimited imaging, the pixel intensities are affected by photon ...
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Learning to Infer and Execute 3D Shape Programs
Human perception of 3D shapes goes beyond reconstructing them as a set o...
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An adaptive nearest neighbor rule for classification
We introduce a variant of the knearest neighbor classifier in which k i...
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Defending against Adversarial Attacks through Resilient Feature Regeneration
Deep neural network (DNN) predictions have been shown to be vulnerable t...
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A Theoretical Connection Between Statistical Physics and Reinforcement Learning
Sequential decision making in the presence of uncertainty and stochastic...
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Visualizing the PHATE of Neural Networks
Understanding why and how certain neural networks outperform others is k...
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Disentangling Adaptive Gradient Methods from Learning Rates
We investigate several confounding factors in the evaluation of optimiza...
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The Role of Randomness and Noise in Strategic Classification
We investigate the problem of designing optimal classifiers in the strat...
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