697 citations · 2.5k across the 36 of their papers we have counts for
6 papers · 1 filter
Learning to Learn without Gradient Descent by Gradient Descent
Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo +4
We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfe…
Learning to Perform Physics Experiments via Deep Reinforcement Learning
Misha Denil, Pulkit Agrawal, Tejas D Kulkarni +3
When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way…
Distributed Parameter Estimation in Probabilistic Graphical Models
Yariv Dror Mizrahi, Misha Denil, Nando de Freitas
This paper presents foundational theoretical results on distributed parameter estimation for undirected probabilistic graphical models. It introduces a general condition on composi…
Narrowing the Gap: Random Forests In Theory and In Practice
Misha Denil, David Matheson, Nando de Freitas
Despite widespread interest and practical use, the theoretical properties of random forests are still not well understood. In this paper we contribute to this understanding in two…
Linear and Parallel Learning of Markov Random Fields
Yariv Dror Mizrahi, Misha Denil, Nando de Freitas
We introduce a new embarrassingly parallel parameter learning algorithm for Markov random fields with untied parameters which is efficient for a large class of practical models. Ou…
Consistency of Online Random Forests
Misha Denil, David Matheson, Nando de Freitas
As a testament to their success, the theory of random forests has long been outpaced by their application in practice. In this paper, we take a step towards narrowing this gap by p…