1 code implementation • 24 Oct 2022 • Alireza Nasiri, Tristan Bepler
Here, we consider the problem of learning semantic representations of objects that are invariant to pose and location in a fully unsupervised manner.
1 code implementation • 2 Mar 2021 • Alireza Nasiri, Jianjun Hu
Our extensive benchmark experiments show that our hybrid deep network models trained with combined contrastive and cross-entropy loss achieved the state-of-the-art performance on three benchmark datasets ESC-10, ESC-50, and US8K with validation accuracies of 99. 75\%, 93. 4\%, and 86. 49\% respectively.
2 code implementations • 28 Feb 2021 • Rui Xin, Edirisuriya M. D. Siriwardane, Yuqi Song, Yong Zhao, Steph-Yves Louis, Alireza Nasiri, Jianjun Hu
Our experiments show that while active learning itself may sample chemically infeasible candidates, these samples help to train effective screening models for filtering out materials with desired properties from the hypothetical materials created by the generative model.
1 code implementation • 17 Feb 2021 • Steph-Yves Louis, Alireza Nasiri, Fatima J. Rolland, Cameron Mitro, Jianjun Hu
While there exists a wide variety of graph neural networks (GNN) for node classification, only a minority of them adopt mechanisms that effectively target noise propagation during the message-passing procedure.
no code implementations • 1 Jan 2021 • Steph-Yves Louis, Alireza Nasiri, Fatima Christina Rolland, Cameron Mitro, Jianjun Hu
In this paper we propose the NODE-SELECT graph neural network (NSGNN): a novel and flexible graph neural network that uses subsetting filters to learn the contribution from the nodes selected to share their information.
1 code implementation • 11 Mar 2020 • Steph-Yves Louis, Yong Zhao, Alireza Nasiri, Xiran Wong, Yuqi Song, Fei Liu, Jianjun Hu
Machine learning (ML) methods have gained increasing popularity in exploring and developing new materials.
no code implementations • 26 Feb 2020 • Yuqi Song, Joseph Lindsay, Yong Zhao, Alireza Nasiri, Steph-Yves Louis, Jie Ling, Ming Hu, Jianjun Hu
Noncentrosymmetric materials play a critical role in many important applications such as laser technology, communication systems, quantum computing, cybersecurity, and etc.