5 citations · 13 across the 5 of their papers we have counts for
8 papers
Conditional Contrastive Learning with Kernel
Yao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma +4
Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables. Fair cont…
Learning Weakly-Supervised Contrastive Representations
Yao-Hung Hubert Tsai, Tianqin Li, Weixin Liu +3
We argue that a form of the valuable information provided by the auxiliary information is its implied data clustering information. For instance, considering hashtags as auxiliary i…
Towards Adversarial Robustness via Transductive Learning
Jiefeng Chen, Yang Guo, Xi Wu +4
There has been emerging interest to use transductive learning for adversarial robustness (Goldwasser et al., NeurIPS 2020; Wu et al., ICML 2020). Compared to traditional "test-time…
Integrating Auxiliary Information in Self-supervised Learning
Yao-Hung Hubert Tsai, Tianqin Li, Weixin Liu +3
This paper presents to integrate the auxiliary information (e.g., additional attributes for data such as the hashtags for Instagram images) in the self-supervised learning process.…
An efficient algorithm for -dimensional (persistent) path homology
Tamal K. Dey, Tianqi Li, Yusu Wang
This paper focuses on developing an efficient algorithm for analyzing a directed network (graph) from a topological viewpoint. A prevalent technique for such topological analysis i…
Improving Multi-Person Pose Estimation using Label Correction
Naoki Kato, Tianqi Li, Kohei Nishino +1
Significant attention is being paid to multi-person pose estimation methods recently, as there has been rapid progress in the field owing to convolutional neural networks. Especial…