5 citations · 7 across the 6 of their papers we have counts for
6 papers
Peregrine: One-Shot Fine-Tuning for FHE Inference of General Deep CNNs
Huaming Ling, Ying Wang, Si Chen +1
We address two fundamental challenges in adapting general deep CNNs for FHE-based inference: approximating non-linear activations such as ReLU with low-degree polynomials while min…
Masked Subspace Clustering Methods
Jiebo Song, Huaming Ling
To further utilize the unsupervised features and pairwise information, we propose a general Bilevel Clustering Optimization (BCO) framework to improve the performance of clustering…
Deep Learning with Data Privacy via Residual Perturbation
Wenqi Tao, Huaming Ling, Zuoqiang Shi +1
Protecting data privacy in deep learning (DL) is of crucial importance. Several celebrated privacy notions have been established and used for privacy-preserving DL. However, many e…
Fast and Scalable Semi-Supervised Learning for Multi-View Subspace Clustering
Huaming Ling, Chenglong Bao, Jiebo Song +1
In this paper, we introduce a Fast and Scalable Semi-supervised Multi-view Subspace Clustering (FSSMSC) method, a novel solution to the high computational complexity commonly found…
Semi-Supervised Clustering via Dynamic Graph Structure Learning
Huaming Ling, Chenglong Bao, Xin Liang +1
Most existing semi-supervised graph-based clustering methods exploit the supervisory information by either refining the affinity matrix or directly constraining the low-dimensional…
Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization
Tao Sun, Huaming Ling, Zuoqiang Shi +2
Heavy ball momentum is crucial in accelerating (stochastic) gradient-based optimization algorithms for machine learning. Existing heavy ball momentum is usually weighted by a unifo…