106 citations · 377 across the 79 of their papers we have counts for
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Random Fourier Features for Asymmetric Kernels
Mingzhen He, Fan He, Fanghui Liu +1
The random Fourier features (RFFs) method is a powerful and popular technique in kernel approximation for scalability of kernel methods. The theoretical foundation of RFFs is based…
Piecewise Linear Neural Networks and Deep Learning
Qinghua Tao, Li Li, Xiaolin Huang +3
As a powerful modelling method, PieceWise Linear Neural Networks (PWLNNs) have proven successful in various fields, most recently in deep learning. To apply PWLNN methods, both the…
Trainable Weight Averaging: Accelerating Training and Improving Generalization
Tao Li, Zhehao Huang, Yingwen Wu +4
Weight averaging is a widely used technique for accelerating training and improving the generalization of deep neural networks (DNNs). While existing approaches like stochastic wei…
Learning with Asymmetric Kernels: Least Squares and Feature Interpretation
Mingzhen He, Fan He, Lei Shi +2
Asymmetric kernels naturally exist in real life, e.g., for conditional probability and directed graphs. However, most of the existing kernel-based learning methods require kernels…
Learning Perspective Deformation in X-Ray Transmission Imaging
Yixing Huang, Andreas Maier, Fuxin Fan +5
In cone-beam X-ray transmission imaging, perspective deformation causes difficulty in direct, accurate geometric assessments of anatomical structures. In this work, the perspective…