Table of Contents

• Deep learning interpretation: Flip points and homotopy methodsRoozbeh Yousefzadeh, Dianne P. O'Leary. 1-26 [doi]
• Rademacher complexity and spin glasses: A link between the replica and statistical theories of learningAlia Abbara, Benjamin Aubin, Florent Krzakala, Lenka Zdeborová. 27-54 [doi]
• Exact asymptotics for phase retrieval and compressed sensing with random generative priorsBenjamin Aubin, Bruno Loureiro, Antoine Baker, Florent Krzakala, Lenka Zdeborová. 55-73 [doi]
• SchrödingerRNN: Generative modeling of raw audio as a continuously observed quantum stateBeñat Mencia Uranga, Austen Lamacraft. 74-106 [doi]
• On the stable recovery of deep structured linear networks under sparsity constraintsFrançois Malgouyres. 107-127 [doi]
• Neural network integral representations with the ReLU activation functionArmenak Petrosyan, Anton Dereventsov, Clayton G. Webster. 128-143 [doi]
• A type of generalization error induced by initialization in deep neural networksYaoyu Zhang, Zhi-Qin John Xu, Tao Luo, Zheng Ma 0009. 144-164 [doi]
• Non-Gaussian processes and neural networks at finite widthsSho Yaida. 165-192 [doi]
• SelectNet: Learning to Sample from the Wild for Imbalanced Data TrainingYunru Liu, Tingran Gao, Haizhao Yang. 193-206 [doi]
• Calibrating Multivariate Lévy Processes with Neural NetworksKailai Xu, Eric Darve. 207-220 [doi]
• Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent GamesJiequn Han, Ruimeng Hu. 221-245 [doi]
• Borrowing From the Future: An Attempt to Address Double SamplingYuhua Zhu, Lexing Ying. 246-268 [doi]
• Deep Domain Decomposition Method: Elliptic ProblemsWuyang Li, Xueshuang Xiang, Yingxiang Xu. 269-286 [doi]
• Landscape Complexity for the Empirical Risk of Generalized Linear ModelsAntoine Maillard, Gérard Ben Arous, Giulio Biroli. 287-327 [doi]
• DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERMBao Wang, Quanquan Gu, March Boedihardjo, Lingxiao Wang, Farzin Barekat, Stanley J. Osher. 328-351 [doi]
• NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent DataYifan Sun, Linan Zhang, Hayden Schaeffer. 352-372 [doi]
• The Slow Deterioration of the Generalization Error of the Random Feature ModelChao Ma 0012, Lei Wu, Weinan E. 373-389 [doi]
• Large deviations for the perceptron model and consequences for active learningHugo Cui, Luca Saglietti, Lenka Zdeborová. 390-430 [doi]
• Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform InitializationZhongshu Xu, Yingzhou Li, Xiuyuan Cheng. 431-450 [doi]
• Deep learning Markov and Koopman models with physical constraintsAndreas Mardt, Luca Pasquali, Frank Noé, Hao Wu 0035. 451-475 [doi]
• Gating creates slow modes and controls phase-space complexity in GRUs and LSTMsTankut Can, Kamesh Krishnamurthy, David J. Schwab. 476-511 [doi]
• Robust Training and Initialization of Deep Neural Networks: An Adaptive Basis ViewpointEric C. Cyr, Mamikon A. Gulian, Ravi G. Patel, Mauro Perego, Nathaniel A. Trask. 512-536 [doi]
• New Potential-Based Bounds for the Geometric-Stopping Version of Prediction with Expert AdviceVladimir A. Kobzar, Robert V. Kohn, Zhilei Wang. 537-554 [doi]
• Data-driven Compact Models for Circuit Design and AnalysisKarthik V. Aadithya, Paul Kuberry, Biliana S. Paskaleva, Pavel B. Bochev, K. Leeson, A. Mar, Ting Mei, Eric R. Keiter. 555-569 [doi]
• Geometric Wavelet Scattering Networks on Compact Riemannian ManifoldsMichael Perlmutter, Feng Gao, Guy Wolf, Matthew J. Hirn. 570-604 [doi]
• Policy Gradient based Quantum Approximate Optimization AlgorithmJiahao Yao, Marin Bukov, Lin Lin. 605-634 [doi]
• Quantum Ground States from Reinforcement LearningAriel Barr, Willem Gispen, Austen Lamacraft. 635-653 [doi]