25 citations · 88 across the 12 of their papers we have counts for
12 papers
Efficient Tuning and Inference for Large Language Models on Textual Graphs
Yun Zhu, Yaoke Wang, Haizhou Shi +1
Rich textual and topological information of textual graphs need to be modeled in real-world applications such as webpages, e-commerce, and academic articles. Practitioners have bee…
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning
Yun Zhu, Yaoke Wang, Haizhou Shi +3
Graph-structured data is ubiquitous in the world which models complex relationships between objects, enabling various Web applications. Daily influxes of unlabeled graph data on th…
MARIO: Model Agnostic Recipe for Improving OOD Generalization of Graph Contrastive Learning
Yun Zhu, Haizhou Shi, Zhenshuo Zhang +1
In this work, we investigate the problem of out-of-distribution (OOD) generalization for unsupervised learning methods on graph data. This scenario is particularly challenging beca…
Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework
Zhenshuo Zhang, Yun Zhu, Haizhou Shi +1
Albeit having gained significant progress lately, large-scale graph representation learning remains expensive to train and deploy for two main reasons: (i) the repetitive computati…
Relational Graph Learning for Grounded Video Description Generation
Wenqiao Zhang, Xin Eric Wang, Siliang Tang +5
Grounded video description (GVD) encourages captioning models to attend to appropriate video regions (e.g., objects) dynamically and generate a description. Such a setting can help…
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning
Haizhou Shi, Youcai Zhang, Zijin Shen +4
This paper investigates the feasibility of federated representation learning under the constraints of communication cost and privacy protection. Existing works either conduct annot…
On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signals
Haizhou Shi, Youcai Zhang, Siliang Tang +4
It is a consensus that small models perform quite poorly under the paradigm of self-supervised contrastive learning. Existing methods usually adopt a large off-the-shelf model to t…
Revisiting Catastrophic Forgetting in Class Incremental Learning
Zixuan Ni, Haizhou Shi, Siliang Tang +3
Although the concept of catastrophic forgetting is straightforward, there is a lack of study on its causes. In this paper, we systematically explore and reveal three causes for cat…
CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction
Tao Chen, Haizhou Shi, Siliang Tang +3
The journey of reducing noise from distant supervision (DS) generated training data has been started since the DS was first introduced into the relation extraction (RE) task. For t…
Run Away From your Teacher: Understanding BYOL by a Novel Self-Supervised Approach
Haizhou Shi, Dongliang Luo, Siliang Tang +2
Recently, a newly proposed self-supervised framework Bootstrap Your Own Latent (BYOL) seriously challenges the necessity of negative samples in contrastive learning frameworks. BYO…
Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation
Juncheng Li, Xin Wang, Siliang Tang +4
Visual navigation is a task of training an embodied agent by intelligently navigating to a target object (e.g., television) using only visual observations. A key challenge for curr…
Informative Visual Storytelling with Cross-modal Rules
Jiacheng Li, Haizhou Shi, Siliang Tang +2
Existing methods in the Visual Storytelling field often suffer from the problem of generating general descriptions, while the image contains a lot of meaningful contents remaining…