Papers
Understanding the Role of Momentum in Stochastic Gradient Methods
Igor Gitman, Hunter Lang, Pengchuan Zhang et al.
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters
XIA XIAO, Zigeng Wang, Sanguthevar Rajasekaran
Online Prediction of Switching Graph Labelings with Cluster Specialists
Mark Herbster, James Robinson
Twin Auxilary Classifiers GAN
Mingming Gong, Yanwu Xu, Chunyuan Li et al.
Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients
Jun Sun, Tianyi Chen, Georgios Giannakis et al.
Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm
Amir-massoud Farahmand
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models
Farnam Mansouri, Yuxin Chen, Ara Vartanian et al.
Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images
Magauiya Zhussip, Shakarim Soltanayev, Se Young Chun
Beating SGD Saturation with Tail-Averaging and Minibatching
Nicole Muecke, Gergely Neu, Lorenzo Rosasco
A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models
Maxim Kuznetsov, Daniil Polykovskiy, Dmitry P Vetrov et al.
Superposition of many models into one
Brian Cheung, Alexander Terekhov, Yubei Chen et al.
Understanding Attention and Generalization in Graph Neural Networks
Boris Knyazev, Graham W. Taylor, Mohamed Amer
Large-scale optimal transport map estimation using projection pursuit
Cheng Meng, Yuan Ke, Jingyi Zhang et al.
Towards modular and programmable architecture search
Renato Negrinho, Matthew Gormley, Geoffrey J. Gordon et al.
Communication trade-offs for Local-SGD with large step size
Aymeric Dieuleveut, Kumar Kshitij Patel
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation
Runzhe Yang, Xingyuan Sun, Karthik Narasimhan
A Generic Acceleration Framework for Stochastic Composite Optimization
Andrei Kulunchakov, Julien Mairal
Learning Robust Options by Conditional Value at Risk Optimization
Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori et al.
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning
Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
Yaqin Zhou, Shangqing Liu, Jingkai Siow et al.
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling
Bichuan Guo, Yuxing Han, Jiangtao Wen
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems
Yi Xu, Rong Jin, Tianbao Yang
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks
Yuan Cao, Quanquan Gu
Covariate-Powered Empirical Bayes Estimation
Nikolaos Ignatiadis, Stefan Wager
A Kernel Loss for Solving the Bellman Equation
Yihao Feng, Lihong Li, Qiang Liu