Search Results for author: Alireza Nasiri

Found 6 papers, 4 papers with code

SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification

1 code implementation2 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.

Contrastive Learning Data Augmentation +3

Active learning based generative design for the discovery of wide bandgap materials

2 code implementations28 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.

Active Learning Band Gap

NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique

1 code implementation17 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.

Node Classification

NODE-SELECT: A FLEXIBLE GRAPH NEURAL NETWORK BASED ON REALISTIC PROPAGATION SCHEME

no code implementations1 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.

Node Classification

Machine Learning based prediction of noncentrosymmetric crystal materials

no code implementations26 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.

Machine Learning

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