3 papers
cs.LG2019
Compression with Flows via Local Bits-Back Coding
Jonathan Ho, Evan Lohn, Pieter Abbeel
Likelihood-based generative models are the backbones of lossless compression due to the guaranteed existence of codes with lengths close to negative log likelihood. However, there…
cs.LG2019
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables
Friso H. Kingma, Pieter Abbeel, Jonathan Ho
The bits-back argument suggests that latent variable models can be turned into lossless compression schemes. Translating the bits-back argument into efficient and practical lossles…
cs.LG2018
Evolved Policy Gradients
Rein Houthooft, Richard Y. Chen, Phillip Isola +4
We propose a metalearning approach for learning gradient-based reinforcement learning (RL) algorithms. The idea is to evolve a differentiable loss function, such that an agent, whi…