activity
20192022
most citedAdaptive Hierarchical Down-Sampling for Point Cloud Classification

7 citations · 17 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2022

Label Alignment Regularization for Distribution Shift

Ehsan Imani, Guojun Zhang, Runjia Li +4

Recent work has highlighted the label alignment property (LAP) in supervised learning, where the vector of all labels in the dataset is mostly in the span of the top few singular v…

cs.LG2022★ 4 cited

Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation

Qingfeng Lan, Yangchen Pan, Jun Luo +1

Artificial neural networks are promising for general function approximation but challenging to train on non-independent or non-identically distributed data due to catastrophic forg…

cs.CV2021

Probing the Effect of Selection Bias on Generalization: A Thought Experiment

John K. Tsotsos, Jun Luo

Learned systems in the domain of visual recognition and cognition impress in part because even though they are trained with datasets many orders of magnitude smaller than the full…

cs.AI2020★ 6 cited

Understanding and Mitigating the Limitations of Prioritized Experience Replay

Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand +4

Prioritized Experience Replay (ER) has been empirically shown to improve sample efficiency across many domains and attracted great attention; however, there is little theoretical u…

cs.CV2019★ 7 cited

Adaptive Hierarchical Down-Sampling for Point Cloud Classification

Ehsan Nezhadarya, Ehsan Taghavi, Ryan Razani +2

While several convolution-like operators have recently been proposed for extracting features out of point clouds, down-sampling an unordered point cloud in a deep neural network ha…