Xingjian Li
Orcid: 0000-0001-8073-7552Affiliations:
- Baidu, Inc., China
- University of Macau, Macau, SAR, China
- High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, China (former)
According to our database1,
Xingjian Li
authored at least 43 papers
between 2011 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
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Bibliography
2024
Towards accurate knowledge transfer via target-awareness representation disentanglement.
Mach. Learn., February, 2024
Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron Subtomogram Segmentation with Denoised Pseudo Labeling.
CoRR, 2024
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 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
Pattern Recognit., December, 2023
Trans. Mach. Learn. Res., 2023
Concurr. Comput. Pract. Exp., 2023
G-LIME: Statistical learning for local interpretations of deep neural networks using global priors.
Artif. Intell., 2023
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023
Proceedings of the IEEE International Conference on Acoustics, 2023
2022
GrOD: Deep Learning with Gradients Orthogonal Decomposition for Knowledge Transfer, Distillation, and Adversarial Training.
ACM Trans. Knowl. Discov. Data, 2022
Knowledge Distillation with Attention for Deep Transfer Learning of Convolutional Networks.
ACM Trans. Knowl. Discov. Data, 2022
COLAM: Co-Learning of Deep Neural Networks and Soft Labels via Alternating Minimization.
Neural Process. Lett., 2022
Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond.
Knowl. Inf. Syst., 2022
CoRR, 2022
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022
2021
"In-Network Ensemble": Deep Ensemble Learning with Diversified Knowledge Distillation.
ACM Trans. Intell. Syst. Technol., 2021
Frontiers Artif. Intell., 2021
Interpretable Deep Learning: Interpretations, Interpretability, Trustworthiness, and Beyond.
CoRR, 2021
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021
Elastic Deep Learning Using Knowledge Distillation with Heterogeneous Computing Resources.
Proceedings of the Euro-Par 2021: Parallel Processing Workshops, 2021
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
2020
Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement.
CoRR, 2020
CoRR, 2020
RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr.
Proceedings of the 37th International Conference on Machine Learning, 2020
Proceedings of the 8th International Conference on Learning Representations, 2020
Proceedings of the 31st British Machine Vision Conference 2020, 2020
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020
2019
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity.
CoRR, 2019
Delta: Deep Learning Transfer using Feature Map with Attention for Convolutional Networks.
Proceedings of the 7th International Conference on Learning Representations, 2019
Proceedings of the 2019 IEEE International Conference on Data Mining, 2019
2012
Proceedings of the International Conference on Supercomputing, 2012
2011
Proceedings of the 12th International Conference on Parallel and Distributed Computing, 2011
Experience of parallelizing cryo-EM 3D reconstruction on a CPU-GPU heterogeneous system.
Proceedings of the 20th ACM International Symposium on High Performance Distributed Computing, 2011
Floating-point mixed-radix FFT core generation for FPGA and comparison with GPU and CPU.
Proceedings of the 2011 International Conference on Field-Programmable Technology, 2011