261 citations · 497 across the 10 of their papers we have counts for
10 papers
For SALE: State-Action Representation Learning for Deep Reinforcement Learning
Scott Fujimoto, Wei-Di Chang, Edward J. Smith +3
In the field of reinforcement learning (RL), representation learning is a proven tool for complex image-based tasks, but is often overlooked for environments with low-level states,…
Uncertainty-Driven Active Vision for Implicit Scene Reconstruction
Edward J. Smith, Michal Drozdzal, Derek Nowrouzezahrai +2
Multi-view implicit scene reconstruction methods have become increasingly popular due to their ability to represent complex scene details. Recent efforts have been devoted to impro…
Frame Averaging for Invariant and Equivariant Network Design
Omri Puny, Matan Atzmon, Heli Ben-Hamu +4
Many machine learning tasks involve learning functions that are known to be invariant or equivariant to certain symmetries of the input data. However, it is often challenging to de…
Active 3D Shape Reconstruction from Vision and Touch
Edward J. Smith, David Meger, Luis Pineda +4
Humans build 3D understandings of the world through active object exploration, using jointly their senses of vision and touch. However, in 3D shape reconstruction, most recent prog…
3D Shape Reconstruction from Vision and Touch
Edward J. Smith, Roberto Calandra, Adriana Romero +4
When a toddler is presented a new toy, their instinctual behaviour is to pick it upand inspect it with their hand and eyes in tandem, clearly searching over its surface to properly…
Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research
Krishna Murthy Jatavallabhula, Edward Smith, Jean-Francois Lafleche +6
We present Kaolin, a PyTorch library aiming to accelerate 3D deep learning research. Kaolin provides efficient implementations of differentiable 3D modules for use in deep learning…