174 citations · 991 across the 46 of their papers we have counts for
5 papers · 1 filter
Neural tangent kernels, transportation mappings, and universal approximation
Ziwei Ji, Matus Telgarsky, Ruicheng Xian
This paper establishes rates of universal approximation for the shallow neural tangent kernel (NTK): network weights are only allowed microscopic changes from random initialization…
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks
Ziwei Ji, Matus Telgarsky
Recent theoretical work has guaranteed that overparameterized networks trained by gradient descent achieve arbitrarily low training error, and sometimes even low test error. The re…
A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization
Yucheng Chen, Matus Telgarsky, Chao Zhang +3
This paper provides a simple procedure to fit generative networks to target distributions, with the goal of a small Wasserstein distance (or other optimal transport costs). The app…
Approximation power of random neural networks
Bolton Bailey, Ziwei Ji, Matus Telgarsky +1
This paper investigates the approximation power of three types of random neural networks: (a) infinite width networks, with weights following an arbitrary distribution; (b) finite…
Characterizing the implicit bias via a primal-dual analysis
Ziwei Ji, Matus Telgarsky
This paper shows that the implicit bias of gradient descent on linearly separable data is exactly characterized by the optimal solution of a dual optimization problem given by a sm…