Xingchen Ma
Xingchen Ma
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Zitiert von
Zitiert von
Depaudionet: An efficient deep model for audio based depression classification
X Ma, H Yang, Q Chen, D Huang, Y Wang
Proceedings of the 6th international workshop on audio/visual emotion …, 2016
Meta-cal: Well-controlled post-hoc calibration by ranking
X Ma, MB Blaschko
International Conference on Machine Learning, 7235-7245, 2021
A Bayesian optimization framework for neural network compression
X Ma, AR Triki, M Berman, C Sagonas, J Cali, MB Blaschko
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
Cost-sensitive two-stage depression prediction using dynamic visual clues
X Ma, D Huang, Y Wang, Y Wang
Computer Vision–ACCV 2016: 13th Asian Conference on Computer Vision, Taipei …, 2017
Confidence-aware personalized federated learning via variational expectation maximization
J Zhu, X Ma, MB Blaschko
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
Additive tree-structured conditional parameter spaces in Bayesian optimization: A novel covariance function and a fast implementation
X Ma, MB Blaschko
IEEE Transactions on Pattern Analysis and Machine Intelligence 43 (9), 3024-3036, 2020
Additive tree-structured covariance function for conditional parameter spaces in Bayesian optimization
X Ma, M Blaschko
International Conference on Artificial Intelligence and Statistics, 1015-1025, 2020
Tackling personalized federated learning with label concept drift via hierarchical bayesian modeling
X Ma, J Zhu, M Blaschko
Online FL-NeurIPS 2022, 2022
A corrected expected improvement acquisition function under noisy observations
H Zhou, X Ma, MB Blaschko
Asian Conference on Machine Learning, 1747-1762, 2024
Uncertainty Estimation in Machine Learning: Applications in Neural Network Compression and Calibration
X Ma
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