Abstract is missing.
- Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited DataBen Adcock, Simone Brugiapaglia, Nick C. Dexter, Sebastian Moraga. 1-36 [doi]
- Temporal-difference learning with nonlinear function approximation: lazy training and mean field regimesAndrea Agazzi, Jianfeng Lu. 37-74 [doi]
- BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear MemoryAmirali Aghazadeh, Vipul Gupta, Alex DeWeese, Onur Ozan Koyluoglu, Kannan Ramchandran. 75-92 [doi]
- Multilevel Stein variational gradient descent with applications to Bayesian inverse problemsTerrence Alsup, Luca Venturi, Benjamin Peherstorfer. 93-117 [doi]
- Interpretable and Learnable Super-Resolution Time-Frequency RepresentationRandall Balestriero, Hervé Glotin, Richard Baranuik. 118-152 [doi]
- Average-Case Integrality Gap for Non-Negative Principal Component AnalysisAfonso S. Bandeira, Dmitriy Kunisky, Alexander S. Wein. 153-171 [doi]
- Spectral Geometric Matrix CompletionAmit Boyarski, Sanketh Vedula, Alexander M. Bronstein. 172-196 [doi]
- Deep Autoencoders: From Understanding to Generalization GuaranteesRomain Cosentino, Randall Balestriero, Richard Baranuik, Behnaam Aazhang. 197-222 [doi]
- Numerical Calabi-Yau metrics from holomorphic networksMichael Douglas, Subramanian Lakshminarasimhan, Yidi Qi. 223-252 [doi]
- Some observations on high-dimensional partial differential equations with Barron dataWeinan E, Stephan Wojtowytsch. 253-269 [doi]
- On the emergence of simplex symmetry in the final and penultimate layers of neural network classifiersWeinan E, Stephan Wojtowytsch. 270-290 [doi]
- Reconstruction of Pairwise Interactions using Energy-Based ModelsChristoph Feinauer, Carlo Lucibello. 291-313 [doi]
- Sharp threshold for alignment of graph databases with Gaussian weightsLuca Ganassali. 314-335 [doi]
- Deep Generative Learning via Euler Particle TransportYuan Gao, Jian Huang 0003, Yuling Jiao, Jin Liu, Xiliang Lu, Jerry Zhijian Yang. 336-368 [doi]
- Ground States of Quantum Many Body Lattice Models via Reinforcement LearningWillem Gispen, Austen Lamacraft. 369-385 [doi]
- Solving Bayesian Inverse Problems via Variational AutoencodersHwan Goh, Sheroze Sheriffdeen, Jonathan Wittmer, Tan Bui-Thanh. 386-425 [doi]
- The Gaussian equivalence of generative models for learning with shallow neural networksSebastian Goldt, Bruno Loureiro, Galen Reeves, Florent Krzakala, Marc Mézard, Lenka Zdeborová. 426-471 [doi]
- Orientation-Preserving Vectorized Distance Between CurvesJeff M. Phillips, Hasan Pourmahmood Aghababa. 472-496 [doi]
- Adversarial Robustness of Stabilized Neural ODE Might be from Obfuscated GradientsYifei Huang, Yaodong Yu, Hongyang Zhang, Yi Ma 0001, Yuan Yao. 497-515 [doi]
- Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale ImagingHannah Lawrence, David Barmherzig, Henry Li, Michael Eickenberg, Marylou Gabrié. 516-567 [doi]
- A deep learning method for solving Fokker-Planck equationsJiayu Zhai, Matthew Dobson, Yao Li. 568-597 [doi]
- A semigroup method for high dimensional committor functions based on neural networkHaoya Li, Yuehaw Khoo, Yinuo Ren, Lexing Ying. 598-618 [doi]
- Decentralized Multi-Agents by Imitation of a Centralized ControllerAlex Tong Lin, Mark J. Debord, Katia Estabridis, Gary A. Hewer, Guido Montúfar, Stanley J. Osher. 619-651 [doi]
- A Data Driven Method for Computing QuasipotentialsBo Lin, Qianxiao Li, Weiqing Ren. 652-670 [doi]
- A Qualitative Study of the Dynamic Behavior for Adaptive Gradient AlgorithmsChao Ma 0012, Lei Wu, Weinan E. 671-692 [doi]
- Construction of optimal spectral methods in phase retrievalAntoine Maillard, Florent Krzakala, Yue M. Lu, Lenka Zdeborová. 693-720 [doi]
- Practical and Fast Momentum-Based Power MethodsTahseen Rabbani, Apollo Jain, Arjun Rajkumar, Furong Huang. 721-756 [doi]
- Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and GeneralizationGrant M. Rotskoff, Andrew R. Mitchell, Eric Vanden-Eijnden. 757-780 [doi]
- Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODEJohann Rudi, Julie Bessac, Amanda Lenzi. 781-808 [doi]
- Solvable Model for Inheriting the Regularization through Knowledge DistillationLuca Saglietti, Lenka Zdeborová. 809-846 [doi]
- Reduced Order Modeling using Shallow ReLU Networks with Grassmann LayersKayla Bollinger, Hayden Schaeffer. 847-867 [doi]
- Analyzing Finite Neural Networks: Can We Trust Neural Tangent Kernel Theory?Mariia Seleznova, Gitta Kutyniok. 868-895 [doi]
- Robust Certification for Laplace Learning on Geometric GraphsMatthew Thorpe, Bao Wang. 896-920 [doi]
- Kernel-Based Smoothness Analysis of Residual NetworksTom Tirer, Joan Bruna, Raja Giryes. 921-954 [doi]
- Dynamic Algorithms for Online Multiple TestingZiyu Xu, Aaditya Ramdas. 955-986 [doi]
- Optimal Policies for a Pandemic: A Stochastic Game Approach and a Deep Learning AlgorithmYao Xuan, Robert Balkin, Jiequn Han, Ruimeng Hu, Héctor D. Ceniceros. 987-1012 [doi]
- Generalization and Memorization: The Bias Potential ModelHongkang Yang, Weinan E. 1013-1043 [doi]
- Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy NetworksJiahao Yao, Paul Köttering, Hans Gundlach, Lin Lin, Marin Bukov. 1044-1081 [doi]
- Implicit Form Neural Network for Learning Scalar Hyperbolic Conservation LawsXiaoping Zhang, Tao Cheng, Lili Ju. 1082-1098 [doi]
- Borrowing From the Future: Addressing Double Sampling in Model-free ControlYuhua Zhu, Zachary Izzo, Lexing Ying. 1099-1136 [doi]
- Hessian-Aided Random Perturbation (HARP) Using Noisy Zeroth-Order QueriesJingyi Zhu. 1137-1160 [doi]
- Hessian Estimation via Stein's Identity in Black-Box ProblemsJingyi Zhu. 1161-1178 [doi]