no code implementations • 14 Jan 2022 • Zhuoyi Lin, Sheng Zang, Rundong Wang, Zhu Sun, J. Senthilnath, Chi Xu, Chee-Keong Kwoh
We then introduce a dynamic transformer encoder (DTE) to capture user-specific inter-item relationships among item candidates by seamlessly accommodating the learned latent user intentions via IDM.
no code implementations • 13 Feb 2022 • Jinraj V Pushpangathan, Harikumar Kandath, Rajdeep Dutta, Rajarshi Bardhan, J. Senthilnath
To solve this RAI consensus problem, first, the sufficient condition for the existence of the RAIDD protocol is obtained using the $\nu$-gap metric-based simultaneous stabilization approach.
no code implementations • 20 Apr 2022 • Mahindra Rautela, J. Senthilnath, Ernesto Monaco, S. Gopalakrishnan
In this paper, we have proposed two different unsupervised-feature learning approaches where the algorithms are trained only on the baseline scenarios to learn the distribution of baseline signals.
no code implementations • 22 Apr 2022 • Mahindra Rautela, Armin Huber, J. Senthilnath, S. Gopalakrishnan
In this work, ultrasonic guided waves and a dual-branch version of convolutional neural networks are used to solve two different but related inverse problems, i. e., finding layup sequence type and identifying material properties.
no code implementations • 13 May 2022 • J. Senthilnath, Nagaraj G, Sumanth Simha C, Sushant Kulkarni, Meenakumari Thapa, Indiramma M, Jón Atli Benediktsson
A Bayesian Deep Restricted Boltzmann-Kohonen architecture for data clustering termed as DRBM-ClustNet is proposed.
no code implementations • 14 Jun 2022 • Siyu Isaac Parker Tian, Zekun Ren, Selvaraj Venkataraj, Yuanhang Cheng, Daniil Bash, Felipe Oviedo, J. Senthilnath, Vijila Chellappan, Yee-Fun Lim, Armin G. Aberle, Benjamin P MacLeod, Fraser G. L. Parlane, Curtis P. Berlinguette, Qianxiao Li, Tonio Buonassisi, Zhe Liu
Transfer learning increasingly becomes an important tool in handling data scarcity often encountered in machine learning.
1 code implementation • 13 Dec 2022 • Mahindra Rautela, J. Senthilnath, Armin Huber, S. Gopalakrishnan
The forward physics-based models are utilized to map from elastic properties space to wave propagation behavior in a laminated composite material.
no code implementations • 13 Mar 2023 • Bangjian Zhou, Pan Jieming, Maheswari Sivan, Aaron Voon-Yew Thean, J. Senthilnath
Our proposed method achieved an overall accuracy of 86. 66% and compared with the second-best existing method it improves 15. 50% on the GAA-FET dislocation defect dataset.
1 code implementation • 21 Dec 2023 • Mahindra Rautela, S. Gopalakrishnan, J. Senthilnath
The inverse estimation capability of the proposed approach is tested in three different isotropic media with different wave velocities.
no code implementations • 14 Feb 2024 • J. Senthilnath, Adithya Bhattiprolu, Ankur Singh, Bangjian Zhou, Min Wu, Jón Atli Benediktsson, XiaoLi Li
A novel online clustering algorithm is presented where an Evolving Restricted Boltzmann Machine (ERBM) is embedded with a Kohonen Network called ERBM-KNet.
no code implementations • 18 Feb 2024 • J. Senthilnath, Bangjian Zhou, Zhen Wei Ng, Deeksha Aggarwal, Rajdeep Dutta, Ji Wei Yoon, Aye Phyu Phyu Aung, Keyu Wu, Min Wu, XiaoLi Li
During the evolution of the autoencoder architecture, a bias-variance regulatory strategy is employed to elicit the optimal response from the RL agent.