[edit]

Volume 107: Mathematical and Scientific Machine Learning, 20-24 July 2020, Princeton University, Princeton, NJ, USA

[edit]

Editors: Jianfeng Lu, Rachel Ward

[bib][citeproc]

Deep learning interpretation: Flip points and homotopy methods

Roozbeh Yousefzadeh, Dianne P. O’Leary; PMLR 107:1-26

Rademacher complexity and spin glasses: A link between the replica and statistical theories of learning

Alia Abbaras, Benjamin Aubin, Florent Krzakala, Lenka Zdeborová; PMLR 107:27-54

Exact asymptotics for phase retrieval and compressed sensing with random generative priors

Benjamin Aubin, Bruno Loureiro, Antoine Baker, Florent Krzakala, Lenka Zdeborová; PMLR 107:55-73

SchrödingerRNN: Generative modeling of raw audio as a continuously observed quantum state

Beñat Mencia Uranga, Austen Lamacraft; PMLR 107:74-106

On the stable recovery of deep structured linear networks under sparsity constraints

François Malgouyres; PMLR 107:107-127

Neural network integral representations with the ReLU activation function

Armenak Petrosyan, Anton Dereventsov, Clayton G. Webster; PMLR 107:128-143

A type of generalization error induced by initialization in deep neural networks

Yaoyu Zhang, Zhi-Qin John Xu, Tao Luo, Zheng Ma; PMLR 107:144-164

Non-Gaussian processes and neural networks at finite widths

Sho Yaida; PMLR 107:165-192

SelectNet: Learning to Sample from the Wild for Imbalanced Data Training

Yunru Liu, Tingran Gao, Haizhao Yang; PMLR 107:193-206

Calibrating Multivariate Lévy Processes with Neural Networks

Kailai Xu, Eric Darve; PMLR 107:207-220

Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games

Jiequn Han, Ruimeng Hu; PMLR 107:221-245

Borrowing From the Future: An Attempt to Address Double Sampling

Yuhua Zhu, Lexing Ying; PMLR 107:246-268

Deep Domain Decomposition Method: Elliptic Problems

Wuyang Li, Xueshuang Xiang, Yingxiang Xu; PMLR 107:269-286

Landscape Complexity for the Empirical Risk of Generalized Linear Models

Antoine Maillard, Gérard Ben Arous, Giulio Biroli; PMLR 107:287-327

DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM

Bao Wang, Quanquan Gu, March Boedihardjo, Lingxiao Wang, Farzin Barekat, Stanley J. Osher; PMLR 107:328-351

NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data

Yifan Sun, Linan Zhang, Hayden Schaeffer; PMLR 107:352-372

The Slow Deterioration of the Generalization Error of the Random Feature Model

Chao Ma, Lei Wu, Weinan E; PMLR 107:373-389

Large deviations for the perceptron model and consequences for active learning

Hugo Cui, Luca Saglietti, Lenka Zdeborova; PMLR 107:390-430

Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform Initialization

Zhongshu Xu, Yingzhou Li, Xiuyuan Cheng; PMLR 107:431-450

Deep learning Markov and Koopman models with physical constraints

Andreas Mardt, Luca Pasquali, Frank Noé, Hao Wu; PMLR 107:451-475

Gating creates slow modes and controls phase-space complexity in GRUs and LSTMs

Tankut Can, Kamesh Krishnamurthy, David J. Schwab; PMLR 107:476-511

Robust Training and Initialization of Deep Neural Networks: An Adaptive Basis Viewpoint

Eric C. Cyr, Mamikon A. Gulian, Ravi G. Patel, Mauro Perego, Nathaniel A. Trask; PMLR 107:512-536

New Potential-Based Bounds for the Geometric-Stopping Version of Prediction with Expert Advice

Vladimir A. Kobzar, Robert V. Kohn, Zhilei Wang; PMLR 107:537-554

Data-driven Compact Models for Circuit Design and Analysis

K. Aadithya, P. Kuberry, B. Paskaleva, P. Bochev, K. Leeson, A. Mar, T. Mei, E. Keiter; PMLR 107:555-569

Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds

Michael Perlmutter, Feng Gao, Guy Wolf, Matthew Hirn; PMLR 107:570-604

Policy Gradient based Quantum Approximate Optimization Algorithm

Jiahao Yao, Marin Bukov, Lin Lin; PMLR 107:605-634

Quantum Ground States from Reinforcement Learning

Ariel Barr, Willem Gispen, Austen Lamacraft; PMLR 107:635-653

subscribe via RSS