6 papers
Autocorrelated Optimize-via-Estimate: Predict-then-Optimize versus Finite-sample Optimal
Zichun Wang, Gar Goei Loke, Ruiting Zuo
Models that directly optimize for out-of-sample performance in the finite-sample regime have emerged as a promising alternative to traditional estimate-then-optimize approaches in…
From Continuous to Discrete: a No-U-Turn Sampler for Permutations
Nawaf Bou-Rabee, Zichu Wang
We introduce a discrete-space analogue of the No-U-Turn sampler on the symmetric group , yielding a locally adaptive and reversible Markov chain Monte Carlo method for $\mathr…
Nonholonomic Narrow Dead-End Escape with Deep Reinforcement Learning
Denghan Xiong, Yanzhe Zhao, Yutong Chen +1
Nonholonomic constraints restrict feasible velocities without reducing configuration-space dimension, which makes collision-free geometric paths generally non-executable for car-li…
Optimal transport with a density-dependent cost function
Zichu Wang, Esteban G. Tabak
A new pairwise cost function is proposed for the optimal transport barycenter problem, adopting the form of the minimal action between two points, with a Lagrangian that takes into…
"I Can See Forever!": Evaluating Real-time VideoLLMs for Assisting Individuals with Visual Impairments
Ziyi Zhang, Zhen Sun, Zongmin Zhang +6
The visually impaired population faces significant challenges in daily activities. While prior works employ vision language models for assistance, most focus on static content and…
Technique Report of CVPR 2024 PBDL Challenges
Ying Fu, Yu Li, Shaodi You +96
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to info…