Xuhong Li
Orcid: 0000-0002-2582-8256Affiliations:
- University of Technology of Compiègne, Sorbonne University, France
According to our database1,
Xuhong Li
authored at least 45 papers
between 2018 and 2024.
Collaborative distances:
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Bibliography
2024
IEEE Trans. Artif. Intell., September, 2024
P<sup>2</sup>ANet: A Large-Scale Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos.
ACM Trans. Multim. Comput. Commun. Appl., April, 2024
Stochastic gradient descent with random label noises: doubly stochastic models and inference stabilizer.
Mach. Learn. Sci. Technol., March, 2024
Towards accurate knowledge transfer via target-awareness representation disentanglement.
Mach. Learn., February, 2024
IEEE Trans. Serv. Comput., 2024
Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning.
Nat. Mac. Intell., 2024
CoRR, 2024
Towards Automated Data Sciences with Natural Language and SageCopilot: Practices and Lessons Learned.
CoRR, 2024
Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents.
CoRR, 2024
CoRR, 2024
Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training.
CoRR, 2024
CoRR, 2024
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
HumanEval-XL: A Multilingual Code Generation Benchmark for Cross-lingual Natural Language Generalization.
Proceedings of the 2024 Joint International Conference on Computational Linguistics, 2024
G-LIME: Statistical Learning for Local Interpretations of Deep Neural Networks Using Global Priors (Abstract Reprint).
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
2023
Distilling ensemble of explanations for weakly-supervised pre-training of image segmentation models.
Mach. Learn., June, 2023
Cross-model consensus of explanations and beyond for image classification models: an empirical study.
Mach. Learn., May, 2023
Feynman: Federated Learning-Based Advertising for Ecosystems-Oriented Mobile Apps Recommendation.
IEEE Trans. Serv. Comput., 2023
Trans. Mach. Learn. Res., 2023
CoRR, 2023
Doubly Stochastic Models: Learning with Unbiased Label Noises and Inference Stability.
CoRR, 2023
G-LIME: Statistical learning for local interpretations of deep neural networks using global priors.
Artif. Intell., 2023
M<sup>4</sup>: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Context Matters: Cross-Domain Cell Detection in Histopathology Images via Contextual Regularization.
Proceedings of the Medical Image Understanding and Analysis - 27th Annual Conference, 2023
ContRE: A Complementary Measure for Robustness Evaluation of Deep Networks via Contrastive Examples.
Proceedings of the IEEE International Conference on Data Mining, 2023
Rare Codes Count: Mining Inter-code Relations for Long-tail Clinical Text Classification.
Proceedings of the 5th Clinical Natural Language Processing Workshop, 2023
Learning from Training Dynamics: Identifying Mislabeled Data beyond Manually Designed Features.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023
2022
Knowl. Inf. Syst., 2022
Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond.
Knowl. Inf. Syst., 2022
P<sup>2</sup>A: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos.
CoRR, 2022
MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-Ray Images of Multiple Body Parts.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022
2021
Practical Assessment of Generalization Performance Robustness for Deep Networks via Contrastive Examples.
CoRR, 2021
Interpretable Deep Learning: Interpretations, Interpretability, Trustworthiness, and Beyond.
CoRR, 2021
2020
A baseline regularization scheme for transfer learning with convolutional neural networks.
Pattern Recognit., 2020
Image Vis. Comput., 2020
Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement.
CoRR, 2020
Proceedings of the Computer Vision - ECCV 2020, 2020
2019
Regularization schemes for transfer learning with convolutional networks. (Stratégies de régularisation pour l'apprentissage par transfert des réseaux de neurones à convolution).
PhD thesis, 2019
2018
Proceedings of the 2018 IEEE Intelligent Vehicles Symposium, 2018
Proceedings of the 35th International Conference on Machine Learning, 2018