346 citations · 434 across the 19 of their papers we have counts for
5 papers · 1 filter
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis
Jianwen Xie, Zilong Zheng, Ruiqi Gao +3
3D data that contains rich geometry information of objects and scenes is valuable for understanding 3D physical world. With the recent emergence of large-scale 3D datasets, it beco…
Learning Energy-Based Models by Diffusion Recovery Likelihood
Ruiqi Gao, Yang Song, Ben Poole +2
While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging. Inspired by recent progress on di…
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot
Jingtong Su, Yihang Chen, Tianle Cai +4
Network pruning is a method for reducing test-time computational resource requirements with minimal performance degradation. Conventional wisdom of pruning algorithms suggests that…
On Path Integration of Grid Cells: Group Representation and Isotropic Scaling
Ruiqi Gao, Jianwen Xie, Xue-Xin Wei +2
Understanding how grid cells perform path integration calculations remains a fundamental problem. In this paper, we conduct theoretical analysis of a general representation model o…
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC
Erik Nijkamp, Ruiqi Gao, Pavel Sountsov +4
Learning energy-based model (EBM) requires MCMC sampling of the learned model as an inner loop of the learning algorithm. However, MCMC sampling of EBMs in high-dimensional data sp…