11 hours ago · Approximation theory provides useful perspectives in measuring the fundamental complexities of neural networks for approximating functions in certain classes.

4 days ago · what enable overparameterized and low-width networks to express unique functions. ... Approximating continuous functions by relu nets of minimal width. arXiv ...

4 days ago · Abstract—Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures.

6 days ago · We propose a generalization of neural networks to learn operators, termed neural operators, that map between infinite dimensional function spaces. We formulate ...

4 days ago · In this talk we introduce the problem of random walks on the Cayley graph of a finite group, some techniques for its study, and some of the basic results, ...

4 days ago · Approximating Two-Layer Feedforward Networks for Efficient Transformers ... A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks.

4 days ago · Xception [14] is a deep CNN architecture that proposes efficient low-rank approximation techniques for deep CNNs. It introduces depth-wise separable ...

5 days ago · ... approximate value functions to re- cursively determine a dynamic decisions considering the uncertainty of exogenous electricity prices. Our work introduces ...