14 citations · 18 across the 6 of their papers we have counts for
6 papers
Modelling Customer Trajectories with Reinforcement Learning for Practical Retail Insights
Ken Ming Lee, Paul Barde, Maxime C. Cohen +1
Understanding customer movement within retail spaces is essential for optimizing store layouts. Real-world trajectory data can provide highly accurate insights, but collecting it i…
Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes
Anthony Gosselin, Ge Ya Luo, Luis Lara +5
Video diffusion techniques have advanced significantly in recent years; however, they struggle to generate realistic imagery of car crashes due to the scarcity of accident events i…
Differentiable Visual Computing for Inverse Problems and Machine Learning
Andrew Spielberg, Fangcheng Zhong, Konstantinos Rematas +4
Originally designed for applications in computer graphics, visual computing (VC) methods synthesize information about physical and virtual worlds, using prescribed algorithms optim…
STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent
Yewon Lee, Andrew Z. Li, Philip Huang +7
Planning for sequential robotics tasks often requires integrated symbolic and geometric reasoning. TAMP algorithms typically solve these problems by performing a tree search over h…
Efficient Graphics Representation with Differentiable Indirection
Sayantan Datta, Carl Marshall, Derek Nowrouzezahrai +2
We introduce differentiable indirection -- a novel learned primitive that employs differentiable multi-scale lookup tables as an effective substitute for traditional compute and da…
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning
Julien Roy, Paul Barde, Félix G. Harvey +2
In multi-agent reinforcement learning, discovering successful collective behaviors is challenging as it requires exploring a joint action space that grows exponentially with the nu…