42 citations · 148 across the 38 of their papers we have counts for
13 papers · 1 filter
Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective
Jiangmeng Li, Zehua Zang, Qirui Ji +6
Representations learned by self-supervised approaches are generally considered to possess sufficient generalizability and discriminability. However, we disclose a nontrivial mutual…
Neuromodulated Meta-Learning
Jingyao Wang, Huijie Guo, Wenwen Qiang +4
Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological…
Rethinking Misalignment in Vision-Language Model Adaptation from a Causal Perspective
Yanan Zhang, Jiangmeng Li, Lixiang Liu +1
Foundational Vision-Language models such as CLIP have exhibited impressive generalization in downstream tasks. However, CLIP suffers from a two-level misalignment issue, i.e., task…
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…
Rethinking Meta-Learning from a Learning Lens
Jingyao Wang, Wenwen Qiang, Changwen Zheng +2
Meta-learning seeks to learn a well-generalized model initialization from training tasks to solve unseen tasks. From the "learning to learn" perspective, the quality of the initial…
Towards the Causal Complete Cause of Multi-Modal Representation Learning
Jingyao Wang, Siyu Zhao, Wenwen Qiang +4
Multi-Modal Learning (MML) aims to learn effective representations across modalities for accurate predictions. Existing methods typically focus on modality consistency and specific…
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…
Interventional Imbalanced Multi-Modal Representation Learning via -Generalization Front-Door Criterion
Yi Li, Fei Song, Changwen Zheng +3
Multi-modal methods establish comprehensive superiority over uni-modal methods. However, the imbalanced contributions of different modalities to task-dependent predictions constant…
Learning Invariant Causal Mechanism from Vision-Language Models
Zeen Song, Siyu Zhao, Xingyu Zhang +3
Contrastive Language-Image Pretraining (CLIP) has achieved remarkable success, but its performance can degrade when fine-tuned in out-of-distribution (OOD) scenarios. We model the…
On the Universality of Self-Supervised Learning
Wenwen Qiang, Jingyao Wang, Changwen Zheng +2
In this paper, we investigate what constitutes a good representation or model in self-supervised learning (SSL). We argue that a good representation should exhibit universality, ch…
Graph Partial Label Learning with Potential Cause Discovering
Hang Gao, Jiaguo Yuan, Jiangmeng Li +4
Graph Neural Networks (GNNs) have garnered widespread attention for their potential to address the challenges posed by graph representation learning, which face complex graph-struc…
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…