18 citations · 23 across the 8 of their papers we have counts for
9 papers
Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity
Shiyin Tan, Dongyuan Li, Renhe Jiang +3
Popularity bias occurs when popular items are recommended far more frequently than they should be, negatively impacting both user experience and recommendation accuracy. Existing d…
A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization
Shiyin Tan, Jaeeon Park, Dongyuan Li +2
In the field of multi-document summarization (MDS), transformer-based models have demonstrated remarkable success, yet they suffer an input length limitation. Current methods apply…
Revisiting Dynamic Graph Clustering via Matrix Factorization
Dongyuan Li, Satoshi Kosugi, Ying Zhang +3
Dynamic graph clustering aims to detect and track time-varying clusters in dynamic graphs, revealing the evolutionary mechanisms of complex real-world dynamic systems. Matrix facto…
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs
Dongyuan Li, Shiyin Tan, Ying Zhang +4
Dynamic graph modeling aims to uncover evolutionary patterns in real-world systems, enabling accurate social recommendation and early detection of cancer cells. Inspired by the suc…
Reconsidering Degeneration of Token Embeddings with Definitions for Encoder-based Pre-trained Language Models
Ying Zhang, Dongyuan Li, Manabu Okumura
Learning token embeddings based on token co-occurrence statistics has proven effective for both pre-training and fine-tuning in natural language processing. However, recent studies…
Advancing Cross-domain Discriminability in Continual Learning of Vision-Language Models
Yicheng Xu, Yuxin Chen, Jiahao Nie +3
Continual learning (CL) with Vision-Language Models (VLMs) has overcome the constraints of traditional CL, which only focuses on previously encountered classes. During the CL of VL…
Community-Invariant Graph Contrastive Learning
Shiyin Tan, Dongyuan Li, Renhe Jiang +2
Graph augmentation has received great attention in recent years for graph contrastive learning (GCL) to learn well-generalized node/graph representations. However, mainstream GCL m…
A Survey on Deep Active Learning: Recent Advances and New Frontiers
Dongyuan Li, Zhen Wang, Yankai Chen +3
Active learning seeks to achieve strong performance with fewer training samples. It does this by iteratively asking an oracle to label new selected samples in a human-in-the-loop m…
Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition
Dongyuan Li, Ying Zhang, Yusong Wang +2
Speech emotion recognition (SER) has garnered increasing attention due to its wide range of applications in various fields, including human-machine interaction, virtual assistants,…