5 accepted papers
Graph Neural Networks (GNNs) have been widely adopted for Protein Representation Learning (PRL), as residue interaction networks can be naturally represented as graphs. Current GNN-based PRL methods typically rely on single-perspective graph construction strategies, which capture partial properties
An informative, creative title prefix is memorable, capable of capturing the attention of readers, and significantly enhances the potential for increased citations. In this work, we pioneer the exploration of the significance of title prefixes in academic papers and propose a controllable title pref…
Graph Contrastive Learning (GCL) improves Graph Neural Network (GNN)-based protein representation learning by enhancing its generalization and robustness. Existing GCL approaches for protein representation learning rely on 2D topology, where graph augmentation is solely based on topological features…
Multi-modal emotion recognition aims to recognize different emotions precisely by utilizing different modality information. Current multi-modal methods always suffer the pain of the modality gap. Besides, many works use attention mechanisms to fuse different modalities features, which reduces the di…
Multi-modal machine translation (MMT) can reduce ambiguity and semantic distortion compared with traditional machine translation (MT) by utilizing auxiliary information such as images. However, current MMT methods face two primary challenges. The first is their underperformance compared to MT method…