Primal-Dual Wasserstein GAN

no code implementations • 24 May 2018 • Mevlana Gemici, Zeynep Akata, Max Welling

We introduce Primal-Dual Wasserstein GAN, a new learning algorithm for building latent variable models of the data distribution based on the primal and the dual formulations of the optimal transport (OT) problem.

Unsupervised Predictive Memory in a Goal-Directed Agent

1 code implementation • 28 Mar 2018 • Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley, Josh Abramson, Shakir Mohamed, Danilo Rezende, David Saxton, Adam Cain, Chloe Hillier, David Silver, Koray Kavukcuoglu, Matt Botvinick, Demis Hassabis, Timothy Lillicrap

Animals execute goal-directed behaviours despite the limited range and scope of their sensors.

Reinforcement Learning (RL)

Generative Temporal Models with Memory

no code implementations • 15 Feb 2017 • Mevlana Gemici, Chia-Chun Hung, Adam Santoro, Greg Wayne, Shakir Mohamed, Danilo J. Rezende, David Amos, Timothy Lillicrap

We consider the general problem of modeling temporal data with long-range dependencies, wherein new observations are fully or partially predictable based on temporally-distant, past observations.

Variational Inference