359 citations · 437 across the 17 of their papers we have counts for
18 papers
SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning
Tianhao Peng, Xuhong Li, Haitao Yuan +2
Graph contrastive learning has emerged as a powerful technique for learning graph representations that are robust and discriminative. However, traditional approaches often neglect…
Pre-trained Molecular Language Models with Random Functional Group Masking
Tianhao Peng, Yuchen Li, Xuhong Li +7
Recent advancements in computational chemistry have leveraged the power of trans-former-based language models, such as MoLFormer, pre-trained using a vast amount of simplified mole…
Towards Automated Data Sciences with Natural Language and SageCopilot: Practices and Lessons Learned
Yuan Liao, Jiang Bian, Yuhui Yun +9
While the field of NL2SQL has made significant advancements in translating natural language instructions into executable SQL scripts for data querying and processing, achieving ful…
Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents
Haoyi Xiong, Zhiyuan Wang, Xuhong Li +5
This article explores the convergence of connectionist and symbolic artificial intelligence (AI), from historical debates to contemporary advancements. Traditionally considered dis…
When Search Engine Services meet Large Language Models: Visions and Challenges
Haoyi Xiong, Jiang Bian, Yuchen Li +5
Combining Large Language Models (LLMs) with search engine services marks a significant shift in the field of services computing, opening up new possibilities to enhance how we sear…
Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training
Jiamin Chen, Xuhong Li, Yanwu Xu +2
Medical image segmentation aims to identify and locate abnormal structures in medical images, such as chest radiographs, using deep neural networks. These networks require a large…
Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective
Haoyi Xiong, Xuhong Li, Xiaofei Zhang +5
Given the complexity and lack of transparency in deep neural networks (DNNs), extensive efforts have been made to make these systems more interpretable or explain their behaviors i…
CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation
Weibin Liao, Xuhong Li, Qingzhong Wang +3
While pre-training on object detection tasks, such as Common Objects in Contexts (COCO) [1], could significantly boost the performance of cell segmentation, it still consumes on ma…
MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-ray Images of Multiple Body Parts
Weibin Liao, Haoyi Xiong, Qingzhong Wang +6
While self-supervised learning (SSL) algorithms have been widely used to pre-train deep models, few efforts [11] have been done to improve representation learning of X-ray image an…
Doubly Stochastic Models: Learning with Unbiased Label Noises and Inference Stability
Haoyi Xiong, Xuhong Li, Boyang Yu +3
Random label noises (or observational noises) widely exist in practical machine learning settings. While previous studies primarily focus on the affects of label noises to the perf…
Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features
Qingrui Jia, Xuhong Li, Lei Yu +5
While mislabeled or ambiguously-labeled samples in the training set could negatively affect the performance of deep models, diagnosing the dataset and identifying mislabeled sample…
Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation Models
Xuhong Li, Haoyi Xiong, Yi Liu +4
While fine-tuning pre-trained networks has become a popular way to train image segmentation models, such backbone networks for image segmentation are frequently pre-trained using i…
P2ANet: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos
Jiang Bian, Xuhong Li, Tao Wang +7
While deep learning has been widely used for video analytics, such as video classification and action detection, dense action detection with fast-moving subjects from sports videos…
Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study
Xuhong Li, Haoyi Xiong, Siyu Huang +2
Existing interpretation algorithms have found that, even deep models make the same and right predictions on the same image, they might rely on different sets of input features for…
Practical Assessment of Generalization Performance Robustness for Deep Networks via Contrastive Examples
Xuanyu Wu, Xuhong Li, Haoyi Xiong +3
Training images with data transformations have been suggested as contrastive examples to complement the testing set for generalization performance evaluation of deep neural network…
From Distributed Machine Learning to Federated Learning: A Survey
Ji Liu, Jizhou Huang, Yang Zhou +4
In recent years, data and computing resources are typically distributed in the devices of end users, various regions or organizations. Because of laws or regulations, the distribut…
Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond
Xuhong Li, Haoyi Xiong, Xingjian Li +5
Deep neural networks have been well-known for their superb handling of various machine learning and artificial intelligence tasks. However, due to their over-parameterized black-bo…
Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement
Xingjian Li, Di Hu, Xuhong Li +5
Fine-tuning deep neural networks pre-trained on large scale datasets is one of the most practical transfer learning paradigm given limited quantity of training samples. To obtain b…