4 citations · 4 across the 1 of their papers we have counts for
3 papers
cs.LG2019★ 4 cited
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…
cs.LG2019
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…
cs.LG2018
Size-Noise Tradeoffs in Generative Networks
Bolton Bailey, Matus Telgarsky
This paper investigates the ability of generative networks to convert their input noise distributions into other distributions. Firstly, we demonstrate a construction that allows R…