47 citations · 124 across the 19 of their papers we have counts for
7 papers · 1 filter
Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks
Jie Ran, Rui Lin, Hayden K. H. So +2
Elasticities in depth, width, kernel size and resolution have been explored in compressing deep neural networks (DNNs). Recognizing that the kernels in a convolutional neural netwo…
Kernelized Support Tensor Train Machines
Cong Chen, Kim Batselier, Wenjian Yu +1
Tensor, a multi-dimensional data structure, has been exploited recently in the machine learning community. Traditional machine learning approaches are vector- or matrix-based, and…
Fastened CROWN: Tightened Neural Network Robustness Certificates
Zhaoyang Lyu, Ching-Yun Ko, Zhifeng Kong +3
The rapid growth of deep learning applications in real life is accompanied by severe safety concerns. To mitigate this uneasy phenomenon, much research has been done providing reli…
POPQORN: Quantifying Robustness of Recurrent Neural Networks
Ching-Yun Ko, Zhaoyang Lyu, Tsui-Wei Weng +3
The vulnerability to adversarial attacks has been a critical issue for deep neural networks. Addressing this issue requires a reliable way to evaluate the robustness of a network.…
Deep Compression of Sum-Product Networks on Tensor Networks
Ching-Yun Ko, Cong Chen, Yuke Zhang +2
Sum-product networks (SPNs) represent an emerging class of neural networks with clear probabilistic semantics and superior inference speed over graphical models. This work reveals…
A Support Tensor Train Machine
Cong Chen, Kim Batselier, Ching-Yun Ko +1
There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms. An example is the support tensor machine (STM) that utilize…
Parallelized Tensor Train Learning of Polynomial Classifiers
Zhongming Chen, Kim Batselier, Johan A. K. Suykens +1
In pattern classification, polynomial classifiers are well-studied methods as they are capable of generating complex decision surfaces. Unfortunately, the use of multivariate polyn…