252 citations · 611 across the 14 of their papers we have counts for
8 papers · 1 filter
Towards Differentiable Resampling
Michael Zhu, Kevin Murphy, Rico Jonschkowski
Resampling is a key component of sample-based recursive state estimation in particle filters. Recent work explores differentiable particle filters for end-to-end learning. However,…
Regularized Autoencoders via Relaxed Injective Probability Flow
Abhishek Kumar, Ben Poole, Kevin Murphy
Invertible flow-based generative models are an effective method for learning to generate samples, while allowing for tractable likelihood computation and inference. However, the in…
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
Zhe Dong, Bryan A. Seybold, Kevin P. Murphy +1
We propose an efficient inference method for switching nonlinear dynamical systems. The key idea is to learn an inference network which can be used as a proposal distribution for t…
Language as an Abstraction for Hierarchical Deep Reinforcement Learning
Yiding Jiang, Shixiang Gu, Kevin Murphy +1
Solving complex, temporally-extended tasks is a long-standing problem in reinforcement learning (RL). We hypothesize that one critical element of solving such problems is the notio…
Learning Video Representations using Contrastive Bidirectional Transformer
Chen Sun, Fabien Baradel, Kevin Murphy +1
This paper proposes a self-supervised learning approach for video features that results in significantly improved performance on downstream tasks (such as video classification, cap…
Stochastic Prediction of Multi-Agent Interactions from Partial Observations
Chen Sun, Per Karlsson, Jiajun Wu +2
We present a method that learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of…