35 citations · 137 across the 14 of their papers we have counts for
14 papers
Information Theory-Guided Heuristic Progressive Multi-View Coding
Jiangmeng Li, Hang Gao, Wenwen Qiang +1
Multi-view representation learning aims to capture comprehensive information from multiple views of a shared context. Recent works intuitively apply contrastive learning to differe…
NerfAcc: Efficient Sampling Accelerates NeRFs
Ruilong Li, Hang Gao, Matthew Tancik +1
Optimizing and rendering Neural Radiance Fields is computationally expensive due to the vast number of samples required by volume rendering. Recent works have included alternative…
Robust Causal Graph Representation Learning against Confounding Effects
Hang Gao, Jiangmeng Li, Wenwen Qiang +4
The prevailing graph neural network models have achieved significant progress in graph representation learning. However, in this paper, we uncover an ever-overlooked phenomenon: th…
Bootstrapping Informative Graph Augmentation via A Meta Learning Approach
Hang Gao, Jiangmeng Li, Wenwen Qiang +3
Recent works explore learning graph representations in a self-supervised manner. In graph contrastive learning, benchmark methods apply various graph augmentation approaches. Howev…
Information Theory-Guided Heuristic Progressive Multi-View Coding
Jiangmeng Li, Wenwen Qiang, Hang Gao +5
Multi-view representation learning captures comprehensive information from multiple views of a shared context. Recent works intuitively apply contrastive learning (CL) to learn rep…
Learning with Holographic Reduced Representations
Ashwinkumar Ganesan, Hang Gao, Sunil Gandhi +4
Holographic Reduced Representations (HRR) are a method for performing symbolic AI on top of real-valued vectors by associating each vector with an abstract concept, and providing m…
Long-term Human Motion Prediction with Scene Context
Zhe Cao, Hang Gao, Karttikeya Mangalam +3
Human movement is goal-directed and influenced by the spatial layout of the objects in the scene. To plan future human motion, it is crucial to perceive the environment -- imagine…
Deep Learning on Knowledge Graph for Recommender System: A Survey
Yang Gao, Yi-Fan Li, Yu Lin +2
Recent advances in research have demonstrated the effectiveness of knowledge graphs (KG) in providing valuable external knowledge to improve recommendation systems (RS). A knowledg…
Using Neural Networks for Programming by Demonstration
Karan K. Budhraja, Hang Gao, Tim Oates
Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents t…
Universal Adversarial Perturbation for Text Classification
Hang Gao, Tim Oates
Given a state-of-the-art deep neural network text classifier, we show the existence of a universal and very small perturbation vector (in the embedding space) that causes natural t…
Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation
Hang Gao, Xizhou Zhu, Steve Lin +1
Convolutional networks are not aware of an object's geometric variations, which leads to inefficient utilization of model and data capacity. To overcome this issue, recent works on…
Disentangling Propagation and Generation for Video Prediction
Hang Gao, Huazhe Xu, Qi-Zhi Cai +3
A dynamic scene has two types of elements: those that move fluidly and can be predicted from previous frames, and those which are disoccluded (exposed) and cannot be extrapolated.…
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
Hang Gao, Zheng Shou, Alireza Zareian +2
Deep neural networks suffer from over-fitting and catastrophic forgetting when trained with small data. One natural remedy for this problem is data augmentation, which has been rec…
AutoLoc: Weakly-supervised Temporal Action Localization
Zheng Shou, Hang Gao, Lei Zhang +2
Temporal Action Localization (TAL) in untrimmed video is important for many applications. But it is very expensive to annotate the segment-level ground truth (action class and temp…