4 citations · 11 across the 10 of their papers we have counts for
10 papers
From Inference to Adaptation: A Unified Optimal Transport View of Vision Language Model
Qi Yu, Zhichen Zeng, Katherine Tieu +8
Vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities yet remain sensitive to real-world distribution shifts during inference. Although significant effo…
is Theoretically Large Enough for Embedding-based Top- Retrieval
Zihao Wang, Hang Yin, Lihui Liu +4
This paper studies the Minimal Embeddable Dimension (MED): the least dimension in which there exists a configuration of object vectors so that every subset of size at most …
EVINET: Towards Open-World Graph Learning via Evidential Reasoning Network
Weijie Guan, Haohui Wang, Jian Kang +2
Graph learning has been crucial to many real-world tasks, but they are often studied with a closed-world assumption, with all possible labels of data known a priori. To enable effe…
TransNet: Transfer Knowledge for Few-shot Knowledge Graph Completion
Lihui Liu, Zihao Wang, Dawei Zhou +6
Knowledge graphs (KGs) are ubiquitous and widely used in various applications. However, most real-world knowledge graphs are incomplete, which significantly degrades their performa…
TUCKET: A Tensor Time Series Data Structure for Efficient and Accurate Factor Analysis over Time Ranges
Ruizhong Qiu, Jun-Gi Jang, Xiao Lin +2
Tucker decomposition has been widely used in a variety of applications to obtain latent factors of tensor data. In these applications, a common need is to compute Tucker decomposit…
Neural-Symbolic Reasoning over Knowledge Graphs: A Survey from a Query Perspective
Lihui Liu, Zihao Wang, Hanghang Tong
Knowledge graph reasoning is pivotal in various domains such as data mining, artificial intelligence, the Web, and social sciences. These knowledge graphs function as comprehensive…
Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs
Lihui Liu, Zihao Wang, Ruizhong Qiu +5
Despite the superb performance in many tasks, large language models (LLMs) bear the risk of generating hallucination or even wrong answers when confronted with tasks that demand th…
Conversational Question Answering with Reformulations over Knowledge Graph
Lihui Liu, Blaine Hill, Boxin Du +2
Conversational question answering (convQA) over knowledge graphs (KGs) involves answering multi-turn natural language questions about information contained in a KG. State-of-the-ar…
Neural Multi-network Diffusion towards Social Recommendation
Boxin Du, Lihui Liu, Jiejun Xu +2
Graph Neural Networks (GNNs) have been widely applied on a variety of real-world applications, such as social recommendation. However, existing GNN-based models on social recommend…
KompaRe: A Knowledge Graph Comparative Reasoning System
Lihui Liu, Boxin Du, Heng Ji +1
Reasoning is a fundamental capability for harnessing valuable insight, knowledge and patterns from knowledge graphs. Existing work has primarily been focusing on point-wise reasoni…