7 code implementations • 17 Feb 2018 • Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, Vijay Ramaseshan Chandrasekhar
However, few works have explored the use of GANs for the anomaly detection task.
4 code implementations • 6 Dec 2018 • Houssam Zenati, Manon Romain, Chuan Sheng Foo, Bruno Lecouat, Vijay Ramaseshan Chandrasekhar
Anomaly detection is a significant and hence well-studied problem.
no code implementations • 2 Nov 2018 • Ahmad Al Badawi, Jin Chao, Jie Lin, Chan Fook Mun, Jun Jie Sim, Benjamin Hong Meng Tan, Xiao Nan, Khin Mi Mi Aung, Vijay Ramaseshan Chandrasekhar
In this paper, we show how to accelerate the performance of running CNNs on encrypted data with GPUs.
no code implementations • 15 Nov 2018 • Minggang Zeng, Jatin Nitin Kumar, Zeng Zeng, Ramasamy Savitha, Vijay Ramaseshan Chandrasekhar, Kedar Hippalgaonkar
A fast and accurate predictive tool for polymer properties is demanding and will pave the way to iterative inverse design.
no code implementations • 29 Nov 2018 • Lile Cai, Anne-Maelle Barneche, Arthur Herbout, Chuan Sheng Foo, Jie Lin, Vijay Ramaseshan Chandrasekhar, Mohamed M. Sabry
To this end, we introduce TEA-DNN, a NAS algorithm targeting multi-objective optimization of execution time, energy consumption, and classification accuracy of CNN workloads on embedded architectures.
no code implementations • 9 Nov 2019 • Ramanpreet Singh Pahwa, Jin Chao, Jestine Paul, Yiqun Li, Ma Tin Lay Nwe, Shudong Xie, Ashish James, ArulMurugan Ambikapathi, Zeng Zeng, Vijay Ramaseshan Chandrasekhar
In this paper, a multi-phase deep learning based technique is proposed to perform accurate fault detection of rail-valves.