Papers
4,122 papers found
General Loss Functions Lead to (Approximate) Interpolation in High Dimensions
Kuo-Wei Lai, Vidya Muthukumar
Generative Adversarial Networks: Dynamics
Matias G. Delgadino, Bruno B. Suassuna, Rene Cabrera
Geodesic Slice Sampling on the Sphere
Michael Habeck, Mareike Hasenpflug, Shantanu Kodgirwar et al.
Geometric Operator Learning with Optimal Transport
Xinyi Li, Zongyi Li, Nikola Kovachki et al.
Geometry and Stability of Supervised Learning Problems
Facundo Mémoli, Brantley Vose, Robert C. Williamson
Geometry-Dependent Matching Pursuit: a Transition Phase for Convergence on Linear Regression and LASSO
Celine Moucer, Adrien B. Taylor, Francis Bach
Global Convergence of Adjoint-Optimized Neural PDEs
Konstantin Riedl, Justin Sirignano, Konstantinos Spiliopoulos
Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2
Yuri Chervonyi, Trieu H. Trinh, Miroslav Olšák et al.
Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult
Yuqing Wang, Zhenghao Xu, Tuo Zhao et al.
Gradient Equilibrium in Online Learning: Theory and Applications
Anastasios N. Angelopoulos, Michael I. Jordan, Ryan J. Tibshirani
Graph-accelerated Markov Chain Monte Carlo using Approximate Samples
Leo L. Duan, Anirban Bhattacharya
GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia
Carlo Lucibello, Aurora Rossi
gsplat: An Open-Source Library for Gaussian Splatting
Vickie Ye, Ruilong Li, Justin Kerr et al.
Hierarchical Decision Making Based on Structural Information Principles
Xianghua Zeng, Hao Peng, Dingli Su et al.
High-Dimensional L2-Boosting: Rate of Convergence
Ye Luo, Martin Spindler, Jannis Kueck
High-Rank Irreducible Cartesian Tensor Decomposition and Bases of Equivariant Spaces
Shihao Shao, Yikang Li, Zhouchen Lin et al.
Hopfield-Fenchel-Young Networks: A Unified Framework for Associative Memory Retrieval
Saul Santos, Vlad Niculae, Daniel McNamee et al.
How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences
Mikolaj J. Kasprzak, Ryan Giordano, Tamara Broderick
Identifiability of Causal Graphs under Non-Additive Conditionally Parametric Causal Models
Juraj Bodik, Valérie Chavez-Demoulin
Implicit vs Unfolded Graph Neural Networks
Yongyi Yang, Tang Liu, Yangkun Wang et al.
Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick
Xiyuan Wang, Pan Li, Muhan Zhang
Inferring Change Points in High-Dimensional Regression via Approximate Message Passing
Gabriel Arpino, Xiaoqi Liu, Julia Gontarek et al.