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Found 7 papers, 5 papers with code
Global Attention based Graph Convolutional Neural Networks for Improved Materials Property Prediction
Machine learning (ML) methods have gained increasing popularity in exploring and developing new materials.
Active learning based generative design for the discovery of wide bandgap materials
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.
NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique
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.
SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification
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.
Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE
Here, we consider the problem of learning semantic representations of objects that are invariant to pose and location in a fully unsupervised manner.
Machine Learning based prediction of noncentrosymmetric crystal materials
Noncentrosymmetric materials play a critical role in many important applications such as laser technology, communication systems, quantum computing, cybersecurity, and etc.
NODE-SELECT: A FLEXIBLE GRAPH NEURAL NETWORK BASED ON REALISTIC PROPAGATION SCHEME
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.
