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MSML 2021: Virtual Conference / Lausanne, Switzerland
- Joan Bruna, Jan S. Hesthaven, Lenka Zdeborová:
Mathematical and Scientific Machine Learning, 16-19 August 2021, Virtual Conference / Lausanne, Switzerland. Proceedings of Machine Learning Research 145, PMLR 2021 - Ben Adcock, Simone Brugiapaglia, Nick C. Dexter
, Sebastian Moraga
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data. 1-36 - Andrea Agazzi, Jianfeng Lu:
Temporal-difference learning with nonlinear function approximation: lazy training and mean field regimes. 37-74 - Amirali Aghazadeh, Vipul Gupta, Alex DeWeese, Onur Ozan Koyluoglu, Kannan Ramchandran:
BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory. 75-92 - Terrence Alsup, Luca Venturi, Benjamin Peherstorfer:
Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. 93-117 - Randall Balestriero, Hervé Glotin, Richard G. Baraniuk:
Interpretable and Learnable Super-Resolution Time-Frequency Representation. 118-152 - Afonso S. Bandeira, Dmitriy Kunisky, Alexander S. Wein:
Average-Case Integrality Gap for Non-Negative Principal Component Analysis. 153-171 - Amit Boyarski, Sanketh Vedula, Alexander M. Bronstein:
Spectral Geometric Matrix Completion. 172-196 - Romain Cosentino, Randall Balestriero, Richard G. Baraniuk, Behnaam Aazhang:
Deep Autoencoders: From Understanding to Generalization Guarantees. 197-222 - Michael Douglas, Subramanian Lakshminarasimhan, Yidi Qi
Numerical Calabi-Yau metrics from holomorphic networks. 223-252 - Weinan E, Stephan Wojtowytsch:
Some observations on high-dimensional partial differential equations with Barron data. 253-269 - Weinan E, Stephan Wojtowytsch:
On the emergence of simplex symmetry in the final and penultimate layers of neural network classifiers. 270-290 - Christoph Feinauer, Carlo Lucibello:
Reconstruction of Pairwise Interactions using Energy-Based Models. 291-313 - Luca Ganassali:
Sharp threshold for alignment of graph databases with Gaussian weights. 314-335 - Yuan Gao
Deep Generative Learning via Euler Particle Transport. 336-368 - Willem Gispen, Austen Lamacraft:
Ground States of Quantum Many Body Lattice Models via Reinforcement Learning. 369-385 - Hwan Goh, Sheroze Sheriffdeen, Jonathan Wittmer, Tan Bui-Thanh:
Solving Bayesian Inverse Problems via Variational Autoencoders. 386-425 - Sebastian Goldt, Bruno Loureiro, Galen Reeves, Florent Krzakala
The Gaussian equivalence of generative models for learning with shallow neural networks. 426-471 - Jeff M. Phillips, Hasan Pourmahmood Aghababa:
Orientation-Preserving Vectorized Distance Between Curves. 472-496 - Yifei Huang, Yaodong Yu, Hongyang Zhang, Yi Ma, Yuan Yao:
Adversarial Robustness of Stabilized Neural ODE Might be from Obfuscated Gradients. 497-515 - Hannah Lawrence, David Barmherzig, Henry Li, Michael Eickenberg, Marylou Gabrié:
Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging. 516-567 - Jiayu Zhai, Matthew Dobson, Yao Li:
A deep learning method for solving Fokker-Planck equations. 568-597 - Haoya Li, Yuehaw Khoo, Yinuo Ren, Lexing Ying:
A semigroup method for high dimensional committor functions based on neural network. 598-618 - Alex Tong Lin, Mark J. Debord, Katia Estabridis, Gary A. Hewer, Guido Montúfar, Stanley J. Osher:
Decentralized Multi-Agents by Imitation of a Centralized Controller. 619-651 - Bo Lin, Qianxiao Li, Weiqing Ren:
A Data Driven Method for Computing Quasipotentials. 652-670 - Chao Ma, Lei Wu, Weinan E:
A Qualitative Study of the Dynamic Behavior for Adaptive Gradient Algorithms. 671-692 - Antoine Maillard, Florent Krzakala
Construction of optimal spectral methods in phase retrieval. 693-720 - Tahseen Rabbani, Apollo Jain, Arjun Rajkumar, Furong Huang:
Practical and Fast Momentum-Based Power Methods. 721-756 - Grant M. Rotskoff, Andrew R. Mitchell, Eric Vanden-Eijnden:
Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization. 757-780 - Johann Rudi
Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODE. 781-808 - Luca Saglietti, Lenka Zdeborová:
Solvable Model for Inheriting the Regularization through Knowledge Distillation. 809-846 - Kayla Bollinger, Hayden Schaeffer:
Reduced Order Modeling using Shallow ReLU Networks with Grassmann Layers. 847-867 - Mariia Seleznova, Gitta Kutyniok:
Analyzing Finite Neural Networks: Can We Trust Neural Tangent Kernel Theory? 868-895 - Matthew Thorpe, Bao Wang:
Robust Certification for Laplace Learning on Geometric Graphs. 896-920 - Tom Tirer, Joan Bruna, Raja Giryes:
Kernel-Based Smoothness Analysis of Residual Networks. 921-954 - Ziyu Xu, Aaditya Ramdas:
Dynamic Algorithms for Online Multiple Testing. 955-986 - Yao Xuan, Robert Balkin, Jiequn Han, Ruimeng Hu, Héctor D. Ceniceros:
Optimal Policies for a Pandemic: A Stochastic Game Approach and a Deep Learning Algorithm. 987-1012 - Hongkang Yang, Weinan E:
Generalization and Memorization: The Bias Potential Model. 1013-1043 - Jiahao Yao, Paul Köttering, Hans Gundlach, Lin Lin, Marin Bukov:
Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks. 1044-1081 - Xiaoping Zhang, Tao Cheng, Lili Ju:
Implicit Form Neural Network for Learning Scalar Hyperbolic Conservation Laws. 1082-1098 - Yuhua Zhu, Zachary Izzo, Lexing Ying:
Borrowing From the Future: Addressing Double Sampling in Model-free Control. 1099-1136 - Jingyi Zhu:
Hessian-Aided Random Perturbation (HARP) Using Noisy Zeroth-Order Queries. 1137-1160 - Jingyi Zhu:
Hessian Estimation via Stein's Identity in Black-Box Problems. 1161-1178
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