94 citations · 203 across the 30 of their papers we have counts for
30 papers
Bayesian Cross-Modal Alignment Learning for Few-Shot Out-of-Distribution Generalization
Lin Zhu, Xinbing Wang, Chenghu Zhou +1
Recent advances in large pre-trained models showed promising results in few-shot learning. However, their generalization ability on two-dimensional Out-of-Distribution (OoD) data,…
Domain Invariant Learning for Gaussian Processes and Bayesian Exploration
Xilong Zhao, Siyuan Bian, Yaoyun Zhang +5
Out-of-distribution (OOD) generalization has long been a challenging problem that remains largely unsolved. Gaussian processes (GP), as popular probabilistic model classes, especia…
Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus
Tianhang Zhang, Lin Qiu, Qipeng Guo +6
Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields. However, LLMs are prone to hallucinate untruthful or nonsens…
Exploring the Limits of Historical Information for Temporal Knowledge Graph Extrapolation
Yi Xu, Junjie Ou, Hui Xu +4
Temporal knowledge graphs, representing the dynamic relationships and interactions between entities over time, have been identified as a promising approach for event forecasting. H…
Graph Out-of-Distribution Generalization with Controllable Data Augmentation
Bin Lu, Xiaoying Gan, Ze Zhao +4
Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying graph properties. However, due to the selection bias of training and testing data (e.g., traini…
Exploring and Verbalizing Academic Ideas by Concept Co-occurrence
Yi Xu, Shuqian Sheng, Bo Xue +3
Researchers usually come up with new ideas only after thoroughly comprehending vast quantities of literature. The difficulty of this procedure is exacerbated by the fact that the n…
K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization
Cheng Deng, Tianhang Zhang, Zhongmou He +9
Large language models (LLMs) have achieved great success in general domains of natural language processing. In this paper, we bring LLMs to the realm of geoscience with the objecti…
Tackling Cooperative Incompatibility for Zero-Shot Human-AI Coordination
Yang Li, Shao Zhang, Jichen Sun +5
Securing coordination between AI agent and teammates (human players or AI agents) in contexts involving unfamiliar humans continues to pose a significant challenge in Zero-Shot Coo…
Towards Tracing Code Provenance with Code Watermarking
Wei Li, Borui Yang, Yujie Sun +5
Recent advances in large language models have raised wide concern in generating abundant plausible source code without scrutiny, and thus tracing the provenance of code emerges as…
Prediction with Incomplete Data under Agnostic Mask Distribution Shift
Yichen Zhu, Jian Yuan, Bo Jiang +4
Data with missing values is ubiquitous in many applications. Recent years have witnessed increasing attention on prediction with only incomplete data consisting of observed feature…
Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator
Ziwei He, Meng Yang, Minwei Feng +4
The transformer model is known to be computationally demanding, and prohibitively costly for long sequences, as the self-attention module uses a quadratic time and space complexity…
ONCache: A Cache-Based Low-Overhead Container Overlay Network
Shengkai Lin, Shizhen Zhao, Peirui Cao +6
Recent years have witnessed a widespread adoption of containers. While containers simplify and accelerate application development, existing container network technologies either in…
Revisiting Network Value: Sublinear Knowledge Law
Xinbing Wang, Luoyi Fu, Huquan Kang +3
Three influential laws, namely Sarnoff's Law, Metcalfe's Law, and Reed's Law, have been established to describe network value in terms of the number of neighbors, edges, and subgra…
Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph
Cheng Deng, Jiaxin Ding, Luoyi Fu +3
The pandemic of COVID-19 has inspired extensive works across different research fields. Existing literature and knowledge platforms on COVID-19 only focus on collecting papers on b…
Asymmetric Polynomial Loss For Multi-Label Classification
Yusheng Huang, Jiexing Qi, Xinbing Wang +1
Various tasks are reformulated as multi-label classification problems, in which the binary cross-entropy (BCE) loss is frequently utilized for optimizing well-designed models. Howe…
Text Classification in the Wild: a Large-scale Long-tailed Name Normalization Dataset
Jiexing Qi, Shuhao Li, Zhixin Guo +5
Real-world data usually exhibits a long-tailed distribution,with a few frequent labels and a lot of few-shot labels. The study of institution name normalization is a perfect applic…
Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing
Yunchong Song, Chenghu Zhou, Xinbing Wang +1
Most graph neural networks follow the message passing mechanism. However, it faces the over-smoothing problem when multiple times of message passing is applied to a graph, causing…
Adapting Knowledge for Few-shot Table-to-Text Generation
Zhixin Guo, Minyxuan Yan, Jiexing Qi +4
Pretrained language models (PLMs) have made remarkable progress in table-to-text generation tasks. However, the lack of domain-specific knowledge makes it challenging to bridge the…
Cooperative Open-ended Learning Framework for Zero-shot Coordination
Yang Li, Shao Zhang, Jichen Sun +4
Zero-shot coordination in cooperative artificial intelligence (AI) remains a significant challenge, which means effectively coordinating with a wide range of unseen partners. Previ…
Few-Shot Table-to-Text Generation with Prompt Planning and Knowledge Memorization
Zhixin Guo, Minyxuan Yan, Jiexing Qi +5
Pre-trained language models (PLM) have achieved remarkable advancement in table-to-text generation tasks. However, the lack of labeled domain-specific knowledge and the topology ga…
Syntax-guided Localized Self-attention by Constituency Syntactic Distance
Shengyuan Hou, Jushi Kai, Haotian Xue +5
Recent works have revealed that Transformers are implicitly learning the syntactic information in its lower layers from data, albeit is highly dependent on the quality and scale of…
Melody Infilling with User-Provided Structural Context
Chih-Pin Tan, Alvin W. Y. Su, Yi-Hsuan Yang
This paper proposes a novel Transformer-based model for music score infilling, to generate a music passage that fills in the gap between given past and future contexts. While exist…
INFINITY: A Simple Yet Effective Unsupervised Framework for Graph-Text Mutual Conversion
Yi Xu, Luoyi Fu, Zhouhan Lin +2
Graph-to-text (G2T) generation and text-to-graph (T2G) triple extraction are two essential tasks for constructing and applying knowledge graphs. Existing unsupervised approaches tu…
Disentangled Graph Contrastive Learning for Review-based Recommendation
Yuyang Ren, Haonan Zhang, Qi Li +5
User review data is helpful in alleviating the data sparsity problem in many recommender systems. In review-based recommendation methods, review data is considered as auxiliary inf…