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Editors: Jianfeng Lu, Rachel Ward
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Deep learning interpretation: Flip points and homotopy methods
; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:1-26
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Rademacher complexity and spin glasses: A link between the replica and statistical theories of learning
Alia Abbaras, Benjamin Aubin, Florent Krzakala, Lenka Zdeborová; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:27-54
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Exact asymptotics for phase retrieval and compressed sensing with random generative priors
Benjamin Aubin, Bruno Loureiro, Antoine Baker, Florent Krzakala, Lenka Zdeborová; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:55-73
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SchrödingerRNN: Generative modeling of raw audio as a continuously observed quantum state
Beñat Mencia Uranga, Austen Lamacraft; Proceedings of the First Mathematical and Scientific Machine Learning Conference, PMLR 107:74-106
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On the stable recovery of deep structured linear networks under sparsity constraints
François Malgouyres; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:107-127
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Neural network integral representations with the ReLU activation function
Armenak Petrosyan, Anton Dereventsov, Clayton G. Webster; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:128-143
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A type of generalization error induced by initialization in deep neural networks
Yaoyu Zhang, Zhi-Qin John Xu, Tao Luo, Zheng Ma; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:144-164
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Non-Gaussian processes and neural networks at finite widths
Sho Yaida; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:165-192
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SelectNet: Learning to Sample from the Wild for Imbalanced Data Training
Yunru Liu, Tingran Gao, Haizhao Yang; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:193-206
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Calibrating Multivariate Lévy Processes with Neural Networks
Kailai Xu, Eric Darve; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:207-220
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Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games
Jiequn Han, Ruimeng Hu; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:221-245
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Borrowing From the Future: An Attempt to Address Double Sampling
Yuhua Zhu, Lexing Ying; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:246-268
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Deep Domain Decomposition Method: Elliptic Problems
Wuyang Li, Xueshuang Xiang, Yingxiang Xu; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:269-286
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Landscape Complexity for the Empirical Risk of Generalized Linear Models
Antoine Maillard, Gérard Ben Arous, Giulio Biroli; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:287-327
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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; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:328-351
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NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data
Yifan Sun, Linan Zhang, Hayden Schaeffer; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:352-372
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The Slow Deterioration of the Generalization Error of the Random Feature Model
Chao Ma, Lei Wu, Weinan E; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:373-389
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Large deviations for the perceptron model and consequences for active learning
Hugo Cui, Luca Saglietti, Lenka Zdeborova; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:390-430
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Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform Initialization
Zhongshu Xu, Yingzhou Li, Xiuyuan Cheng; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:431-450
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Deep learning Markov and Koopman models with physical constraints
Andreas Mardt, Luca Pasquali, Frank Noé, Hao Wu; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:451-475
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Gating creates slow modes and controls phase-space complexity in GRUs and LSTMs
Tankut Can, Kamesh Krishnamurthy, David J. Schwab; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:476-511
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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; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:512-536
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New Potential-Based Bounds for the Geometric-Stopping Version of Prediction with Expert Advice
Vladimir A. Kobzar, Robert V. Kohn, Zhilei Wang; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:537-554
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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; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:555-569
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Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds
Michael Perlmutter, Feng Gao, Guy Wolf, Matthew Hirn; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:570-604
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Policy Gradient based Quantum Approximate Optimization Algorithm
Jiahao Yao, Marin Bukov, Lin Lin; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:605-634
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Quantum Ground States from Reinforcement Learning
Ariel Barr, Willem Gispen, Austen Lamacraft; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:635-653
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