58 citations · 110 across the 9 of their papers we have counts for
4 papers · 1 filter
On a continuous time model of gradient descent dynamics and instability in deep learning
Mihaela Rosca, Yan Wu, Chongli Qin +1
The recipe behind the success of deep learning has been the combination of neural networks and gradient-based optimization. Understanding the behavior of gradient descent however,…
Discretization Drift in Two-Player Games
Mihaela Rosca, Yan Wu, Benoit Dherin +1
Gradient-based methods for two-player games produce rich dynamics that can solve challenging problems, yet can be difficult to stabilize and understand. Part of this complexity ori…
Training Generative Adversarial Networks by Solving Ordinary Differential Equations
Chongli Qin, Yan Wu, Jost Tobias Springenberg +4
The instability of Generative Adversarial Network (GAN) training has frequently been attributed to gradient descent. Consequently, recent methods have aimed to tailor the models an…
The Kanerva Machine: A Generative Distributed Memory
Yan Wu, Greg Wayne, Alex Graves +1
We present an end-to-end trained memory system that quickly adapts to new data and generates samples like them. Inspired by Kanerva's sparse distributed memory, it has a robust dis…