| Título |
Autores |
Link |
Tema |
| Optimal sampling for least‑squares approximation |
Ben Adcock |
arXiv:2409.02342 |
CD kernel |
| CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions |
Ben Adcock, Juan M. Cardenas, Nick Dexter |
arXiv:2306.00945 |
CD kernel |
| Geometry and Optimization of Shallow Polynomial Networks |
Y. Arjevani, J. Bruna, J. Kileel, E. Polak, M. Trager |
arXiv:2501.06074 |
DL / Optimization |
| Geometry of Polynomial Neural Networks |
Kaie Kubjas, Jiayi Li, Maximilian Wiesmann |
arXiv:2402.00949 |
DL / Algebraic Geometry |
| Convolutional Neural Networks on Manifolds: From Graphs and Back |
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro |
arXiv:2210.00376 |
Geometric DL / GNNs |
The Barron Space and the Flow‑Induced Function Spaces for Neural Network Models |
Weinan E, Chao Ma, Lei Wu |
arXiv:1906.08039 |
Barron |
| Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective |
Samuel Lanthaler |
arXiv:2406.18794 |
DL |
| Breaking the Curse of Dimensionality with Convex Neural Networks |
Francis Bach |
arXiv:1412.8690 |
Barron |
| Stability Properties of Graph Neural Networks |
Fernando Gama,Joan Bruna,Alejandro Ribeiro |
arXiv:1905.04497 |
GNN1 |
| On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning |
Álvaro Arroyo, Alessio Gravina, Benjamin Gutteridge, Federico Barbero, Claudio Gallicchio, Xiaowen Dong, Michael Bronstein, Pierre Vandergheynst |
arXiv:2502.10818 |
GNN2 |
| Graph Attention Networks |
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio |
arXiv:1710.10903 |
GNN2 |
| Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks |
Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, Martin Grohe |
arXiv:1810.02244 |
GNN2 |
| Reconciling Modern Machine Learning Practice and the Bias-Variance Trade-Off |
Mikhail Belkin, Daniel Hsu, Siyuan Ma, Soumik Mandal |
arXiv:1812.11118 |
DD1 |
| Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle |
Rylan Schaeffer, Mikail Khona, Zachary Robertson, Akhilan Boopathy, Kateryna Pistunova, Jason W. Rocks, Ila Fiete, Oluwasanmi Koyejo |
arXiv:2303.14151 |
DD1 |
| Deep Double Descent: Where Bigger Models and More Data Hurt |
Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris, Tristan Yang, Benjamin L. Edelman, Fred Zhang, Boaz Barak |
arXiv:1912.02292 |
DD2 |
| The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization |
Ben Adlam, Jeffrey Pennington |
PMLR v119 |
DD2 |