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个人简介
My basic research interests are in machine learning. In recent years, I have been working on topics in the intersection of optimisation and machine learning, in particular in learning algorithms for deep neural nets and other "nested" systems, for decision trees and tree-based models, and for nonlinear embeddings; and algorithms for optimally compressing deep neural nets. Other topics of interest are dimensionality reduction/manifold learning, clustering, denoising and other unsupervised learning problems, and mean-shift algorithms. I often get inspiration from problems in speech processing (e.g. articulatory inversion and model adaptation), computer vision (e.g. segmentation, articulated pose tracking, image registration), sensor networks, robotics (e.g. inverse kinematics) and other application areas. In the past, I also worked on computational neuroscience, specifically on dimension reduction models of the maps of the visual cortex.
研究兴趣
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CVPR 2023pp.7589-7598, (2023)
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)pp.3373-3377, (2022)
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IEEE International Joint Conference on Neural Network (IJCNN)pp.1-8, (2022)
IEEE International Joint Conference on Neural Network (IJCNN)pp.1-8, (2022)
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