24 citations · 47 across the 13 of their papers we have counts for
13 papers
On the Generalization and Causal Explanation in Self-Supervised Learning
Wenwen Qiang, Zeen Song, Ziyin Gu +4
Self-supervised learning (SSL) methods learn from unlabeled data and achieve high generalization performance on downstream tasks. However, they may also suffer from overfitting to…
Teleporter Theory: A General and Simple Approach for Modeling Cross-World Counterfactual Causality
Jiangmeng Li, Bin Qin, Qirui Ji +4
Leveraging the development of structural causal model (SCM), researchers can establish graphical models for exploring the causal mechanisms behind machine learning techniques. As t…
Revisiting Spurious Correlation in Domain Generalization
Bin Qin, Jiangmeng Li, Yi Li +4
Without loss of generality, existing machine learning techniques may learn spurious correlation dependent on the domain, which exacerbates the generalization of models in out-of-di…
Meta-Auxiliary Learning for Micro-Expression Recognition
Jingyao Wang, Yunhan Tian, Yuxuan Yang +3
Micro-expressions (MEs) are involuntary movements revealing people's hidden feelings, which has attracted numerous interests for its objectivity in emotion detection. However, desp…
Self-Supervised Representation Learning with Meta Comprehensive Regularization
Huijie Guo, Ying Ba, Jie Hu +3
Self-Supervised Learning (SSL) methods harness the concept of semantic invariance by utilizing data augmentation strategies to produce similar representations for different deforma…
BayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction
Jiangmeng Li, Fei Song, Yifan Jin +4
As a novel and effective fine-tuning paradigm based on large-scale pre-trained language models (PLMs), prompt-tuning aims to reduce the gap between downstream tasks and pre-trainin…