José Miguel Hernández Lobato

Published in Towards Data Science

·Dec 8, 2019

Successor Uncertainties

Efficient Exploration in Model-free Reinforcement Learning — I describe here our recent NeurIPS paper [1] [code], which introduces Successor Uncertainties (SU), a state-of-the-art method for efficient exploration in model-free reinforcement learning. The leading authors are David Janz and Jiri Hron, two PhD students from the Cambridge Machine Learning group, with the work having originated during an internship…

Machine Learning

11 min read

Machine Learning

11 min read

Published in Towards Data Science

·Jul 21, 2017

Bayesian Neural Networks with Random Inputs for Model Based Reinforcement Learning

I describe here our recent ICLR paper [1] [code] [talk], which introduces a novel method for model-based reinforcement learning. The main author of this work is Stefan Depeweg, a phd student at Technical University in Munich who I am co-supervising. The key contribution is in our models: Bayesian neural networks…

Machine Learning

9 min read

Bayesian Neural Networks with Random Inputs for Model Based Reinforcement Learning

Machine Learning

9 min read

Published in Towards Data Science

·Jul 20, 2017

Neural networks with optimal accuracy and speed in their predictions

Deep neural networks are currently used by companies such as Facebook, Google, Apple, etc. for making predictions from massive amounts of user generated data. A few examples are the Deep Face and Deep Text systems used by Facebook for face recognition and text understanding or the speech recognition systems used…

Machine Learning

9 min read

Neural networks with optimal accuracy and speed in their predictions

Machine Learning

9 min read