L
Lianyang Ma
Researcher at Shanghai Jiao Tong University
Publications - 11
Citations - 4930
Lianyang Ma is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Deep learning & Kullback–Leibler divergence. The author has an hindex of 7, co-authored 10 publications receiving 3171 citations. Previous affiliations of Lianyang Ma include Chinese Ministry of Education & Nanyang Technological University.
Papers
More filters
Journal ArticleDOI
Recent advances in convolutional neural networks
Jiuxiang Gu,Zhenhua Wang,Jason Kuen,Lianyang Ma,Amir Shahroudy,Bing Shuai,Ting Liu,Xingxing Wang,Gang Wang,Jianfei Cai,Tsuhan Chen +10 more
TL;DR: A broad survey of the recent advances in convolutional neural networks can be found in this article, where the authors discuss the improvements of CNN on different aspects, namely, layer design, activation function, loss function, regularization, optimization and fast computation.
Posted Content
Recent Advances in Convolutional Neural Networks
Jiuxiang Gu,Zhenhua Wang,Jason Kuen,Lianyang Ma,Amir Shahroudy,Bing Shuai,Ting Liu,Xingxing Wang,Li Wang,Gang Wang,Jianfei Cai,Tsuhan Chen +11 more
TL;DR: This paper details the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation, and introduces various applications of convolutional neural networks in computer vision, speech and natural language processing.
Journal ArticleDOI
Person Re-Identification Over Camera Networks Using Multi-Task Distance Metric Learning
TL;DR: This paper reformulates person reidentification in a camera network as a multitask distance metric learning problem, and presents a novel multitask maximally collapsing metric learning (MtMCML) model that works substantially better than other current state-of-the-art person reIdentification methods.
Journal ArticleDOI
Early Action Prediction by Soft Regression
TL;DR: The proposed regression-based early action prediction model outperforms existing models significantly and is more accurate than that on RGB channel and a new RGB-D feature called “local accumulative frame feature (LAFF)”, which can be computed efficiently by constructing an integral feature map.
Book ChapterDOI
Real-Time RGB-D Activity Prediction by Soft Regression
TL;DR: The proposed regression-based activity prediction model outperforms existing models significantly and also shows that the activity prediction on RGB-D sequence is more accurate than that on RGB channel.