492 citations · 2k across the 17 of their papers we have counts for
4 papers · 1 filter
Towards a Definition of Disentangled Representations
Irina Higgins, David Amos, David Pfau +4
How can intelligent agents solve a diverse set of tasks in a data-efficient manner? The disentangled representation learning approach posits that such an agent would benefit from s…
Spectral Inference Networks: Unifying Deep and Spectral Learning
David Pfau, Stig Petersen, Ashish Agarwal +2
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature…
Unrolled Generative Adversarial Networks
Luke Metz, Ben Poole, David Pfau +1
We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This al…
Connecting Generative Adversarial Networks and Actor-Critic Methods
David Pfau, Oriol Vinyals
Both generative adversarial networks (GAN) in unsupervised learning and actor-critic methods in reinforcement learning (RL) have gained a reputation for being difficult to optimize…