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Hybrid Random Features


Oct 13, 2021
Krzysztof Choromanski, Haoxian Chen, Han Lin, Yuanzhe Ma, Arijit Sehanobish, Deepali Jain, Michael S Ryoo, Jake Varley, Andy Zeng, Valerii Likhosherstov, Dmitry Kalashnikov, Vikas Sindhwani, Adrian Weller


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Fine-tuning Vision Transformers for the Prediction of State Variables in Ising Models


Sep 28, 2021
Onur Kara, Arijit Sehanobish, Hector H Corzo

* 8 pages, 5 figures, workshop 

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Application of the Quantum Potential Neural Network to multi-electronic atoms


Jun 08, 2021
Hector H. Corzo, Arijit Sehanobish, Onur Kara

* Follow up of arXiv:2006.13297. First Draft. Work in progress. Comments welcome 

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Permutation invariant networks to learn Wasserstein metrics


Oct 16, 2020
Arijit Sehanobish, Neal Ravindra, David van Dijk

* Work in progress. Comments welcome. Some clarification added about the architecture 

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Self-supervised edge features for improved Graph Neural Network training


Jun 23, 2020
Arijit Sehanobish, Neal G. Ravindra, David van Dijk

* Comments welcome. arXiv admin note: substantial text overlap with arXiv:2006.12971 

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Learning Potentials of Quantum Systems using Deep Neural Networks


Jun 23, 2020
Arijit Sehanobish, Hector H. Corzo, Onur Kara, David van Dijk

* Comments welcome 

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Gaining insight into SARS-CoV-2 infection and COVID-19 severity using self-supervised edge features and Graph Neural Networks


Jun 23, 2020
Arijit Sehanobish, Neal G. Ravindra, David van Dijk

* Accepted as a contributed talk at ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020); Preliminary draft; Comments welcome 

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Disease State Prediction From Single-Cell Data Using Graph Attention Networks


Mar 12, 2020
Neal G. Ravindra, Arijit Sehanobish, Jenna L. Pappalardo, David A. Hafler, David van Dijk

* Incorporated suggestions from anonymous reviewers, Accepted at ACM CHIL 2020, comments welcome 

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Using Chinese Glyphs for Named Entity Recognition


Sep 22, 2019
Arijit Sehanobish, Chan Hee Song


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