39 citations · 97 across the 9 of their papers we have counts for
9 papers · 1 filter
SecureGBM: Secure Multi-Party Gradient Boosting
Zhi Fengy, Haoyi Xiong, Chuanyuan Song +7
Federated machine learning systems have been widely used to facilitate the joint data analytics across the distributed datasets owned by the different parties that do not trust eac…
Towards Making Deep Transfer Learning Never Hurt
Ruosi Wan, Haoyi Xiong, Xingjian Li +2
Transfer learning have been frequently used to improve deep neural network training through incorporating weights of pre-trained networks as the starting-point of optimization for…
NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks
Isaac Ahern, Adam Noack, Luis Guzman-Nateras +3
The problem of explaining deep learning models, and model predictions generally, has attracted intensive interest recently. Many successful approaches forgo global approximations i…
AGAN: Towards Automated Design of Generative Adversarial Networks
Hanchao Wang, Jun Huan
Recent progress in Generative Adversarial Networks (GANs) has shown promising signs of improving GAN training via architectural change. Despite some early success, at present the d…
On the Noisy Gradient Descent that Generalizes as SGD
Jingfeng Wu, Wenqing Hu, Haoyi Xiong +3
The gradient noise of SGD is considered to play a central role in the observed strong generalization abilities of deep learning. While past studies confirm that the magnitude and t…
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary
Yingzhen Yang, Jiahui Yu, Nebojsa Jojic +2
We present a novel method of compression of deep Convolutional Neural Networks (CNNs) by weight sharing through a new representation of convolutional filters. The proposed method r…