7.8k citations · 21.1k across the 144 of their papers we have counts for
6 papers · 1 filter
Interaction Networks for Learning about Objects, Relations and Physics
Peter W. Battaglia, Razvan Pascanu, Matthew Lai +2
Reasoning about objects, relations, and physics is central to human intelligence, and a key goal of artificial intelligence. Here we introduce the interaction network, a model whic…
Local minima in training of neural networks
Grzegorz Swirszcz, Wojciech Marian Czarnecki, Razvan Pascanu
There has been a lot of recent interest in trying to characterize the error surface of deep models. This stems from a long standing question. Given that deep networks are highly no…
Learning to Navigate in Complex Environments
Piotr Mirowski, Razvan Pascanu, Fabio Viola +9
Learning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents. In this work we formulate the navigation question as a reinfor…
Sim-to-Real Robot Learning from Pixels with Progressive Nets
Andrei A. Rusu, Mel Vecerik, Thomas Rothörl +3
Applying end-to-end learning to solve complex, interactive, pixel-driven control tasks on a robot is an unsolved problem. Deep Reinforcement Learning algorithms are too slow to ach…
Progressive Neural Networks
Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins +5
Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding catastrophic forgetting--remains a key obstacle to achieving human-level intelligence. The…
Theano: A Python framework for fast computation of mathematical expressions
The Theano Development Team, Rami Al-Rfou, Guillaume Alain +110
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has bee…