573 citations · 5.3k across the 57 of their papers we have counts for
4 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…
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…
Unsupervised Learning of 3D Structure from Images
Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed +3
A key goal of computer vision is to recover the underlying 3D structure from 2D observations of the world. In this paper we learn strong deep generative models of 3D structures, an…
Building Machines That Learn and Think Like People
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum +1
Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks t…