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20222026
most citedA Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language

42 citations · 78 across the 21 of their papers we have counts for

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18 papers · 1 filter

cs.LG2025

HTG-GCL: Leveraging Hierarchical Topological Granularity from Cellular Complexes for Graph Contrastive Learning

Qirui Ji, Bin Qin, Yifan Jin +5

Graph contrastive learning (GCL) aims to learn discriminative semantic invariance by contrasting different views of the same graph that share critical topological patterns. However…

cs.LG2025

On the Out-of-Distribution Generalization of Self-Supervised Learning

Wenwen Qiang, Jingyao Wang, Zeen Song +2

In this paper, we focus on the out-of-distribution (OOD) generalization of self-supervised learning (SSL). By analyzing the mini-batch construction during the SSL training phase, w…

cs.LG2025

Rethinking Multi-Modal Learning from Gradient Uncertainty

Peizheng Guo, Jingyao Wang, Wenwen Qiang +3

Multi-Modal Learning (MML) integrates information from diverse modalities to improve predictive accuracy. While existing optimization strategies have made significant strides by mi…

cs.LG2025

Beyond All-to-All: Causal-Aligned Transformer with Dynamic Structure Learning for Multivariate Time Series Forecasting

Xingyu Zhang, Hanyun Du, Zeen Song +3

Most existing multivariate time series forecasting methods adopt an all-to-all paradigm that feeds all variable histories into a unified model to predict their future values withou…

cs.LG2025

Rethinking the Bias of Foundation Model under Long-tailed Distribution

Jiahao Chen, Bin Qin, Jiangmeng Li +2

Long-tailed learning has garnered increasing attention due to its practical significance. Among the various approaches, the fine-tuning paradigm has gained considerable interest wi…

cs.LG2024

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…