229 citations · 442 across the 23 of their papers we have counts for
23 papers
An Unforgeable Publicly Verifiable Watermark for Large Language Models
Aiwei Liu, Leyi Pan, Xuming Hu +4
Recently, text watermarking algorithms for large language models (LLMs) have been proposed to mitigate the potential harms of text generated by LLMs, including fake news and copyri…
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Ming Jin, Huan Yee Koh, Qingsong Wen +5
Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time…
VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder
Yueen Ma, Dafeng Chi, Jingjing Li +3
The natural language generation domain has witnessed great success thanks to Transformer models. Although they have achieved state-of-the-art generative quality, they often neglect…
A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges
Ziqiao Meng, Peilin Zhao, Yang Yu +1
Reaction and retrosynthesis prediction are fundamental tasks in computational chemistry that have recently garnered attention from both the machine learning and drug discovery comm…
Hyperbolic Representation Learning: Revisiting and Advancing
Menglin Yang, Min Zhou, Rex Ying +2
The non-Euclidean geometry of hyperbolic spaces has recently garnered considerable attention in the realm of representation learning. Current endeavors in hyperbolic representation…
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks
Xuming Hu, Aiwei Liu, Zeqi Tan +4
Relation extraction (RE) tasks show promising performance in extracting relations from two entities mentioned in sentences, given sufficient annotations available during training.…
Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction
Ziqiao Meng, Peilin Zhao, Yang Yu +1
Organic reaction prediction is a critical task in drug discovery. Recently, researchers have achieved non-autoregressive reaction prediction by modeling the redistribution of elect…
FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks
Xinyu Fu, Irwin King
Heterogeneous graph neural networks (HGNNs) can learn from typed and relational graph data more effectively than conventional GNNs. With larger parameter spaces, HGNNs may require…
WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering
Yankai Chen, Yifei Zhang, Menglin Yang +3
Maximizing the user-item engagement based on vectorized embeddings is a standard procedure of recent recommender models. Despite the superior performance for item recommendations,…
Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction
Xuming Hu, Zhaochen Hong, Chenwei Zhang +2
Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role. Prev…
Bipartite Graph Convolutional Hashing for Effective and Efficient Top-N Search in Hamming Space
Yankai Chen, Yixiang Fang, Yifei Zhang +1
Searching on bipartite graphs is basal and versatile to many real-world Web applications, e.g., online recommendation, database retrieval, and query-document searching. Given a que…
TAKT: Target-Aware Knowledge Transfer for Whole Slide Image Classification
Conghao Xiong, Yi Lin, Hao Chen +5
Transferring knowledge from a source domain to a target domain can be crucial for whole slide image classification, since the number of samples in a dataset is often limited due to…
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy
Yifei Zhang, Dun Zeng, Jinglong Luo +2
Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society. Among various AI technologies, Federated Learning (FL) stands…
Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning
Zhihang Hu, Qinze Yu, Yucheng Guo +5
Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations th…
Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classification
Conghao Xiong, Hao Chen, Joseph J. Y. Sung +1
Multiple Instance Learning (MIL) and transformers are increasingly popular in histopathology Whole Slide Image (WSI) classification. However, unlike human pathologists who selectiv…
Spectral Feature Augmentation for Graph Contrastive Learning and Beyond
Yifei Zhang, Hao Zhu, Zixing Song +2
Although augmentations (e.g., perturbation of graph edges, image crops) boost the efficiency of Contrastive Learning (CL), feature level augmentation is another plausible, compleme…
Momentum Contrastive Pre-training for Question Answering
Minda Hu, Muzhi Li, Yasheng Wang +1
Existing pre-training methods for extractive Question Answering (QA) generate cloze-like queries different from natural questions in syntax structure, which could overfit pre-train…
kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning
Menglin Yang, Min Zhou, Lujia Pan +1
The prevalence of tree-like structures, encompassing hierarchical structures and power law distributions, exists extensively in real-world applications, including recommendation sy…
MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks
Xinyu Fu, Irwin King
Heterogeneous graph neural networks (HGNNs) were proposed for representation learning on structural data with multiple types of nodes and edges. To deal with the performance degrad…
Accurate RNA 3D structure prediction using a language model-based deep learning approach
Tao Shen, Zhihang Hu, Siqi Sun +14
Accurate prediction of RNA three-dimensional (3D) structure remains an unsolved challenge. Determining RNA 3D structures is crucial for understanding their functions and informing…
Graph Component Contrastive Learning for Concept Relatedness Estimation
Yueen Ma, Zixing Song, Xuming Hu +3
Concept relatedness estimation (CRE) aims to determine whether two given concepts are related. Existing methods only consider the pairwise relationship between concepts, while over…
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning
Dun Zeng, Shiyu Liu, Siqi Liang +4
Federated learning enables isolated clients to train a shared model collaboratively by aggregating the locally-computed gradient updates. However, privacy information could be leak…
Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions
Jiayang Chen, Zhihang Hu, Siqi Sun +9
Non-coding RNA structure and function are essential to understanding various biological processes, such as cell signaling, gene expression, and post-transcriptional regulations. Th…