Semi-Supervised Graph Structure Learning on Neuromorphic Computers-Reference-Cited by-同舟云学术

Semi-Supervised Graph Structure Learning on Neuromorphic Computers

Published:2022-07-27 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the International Conference on Neuromorphic Systems 2022
language:
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Author:

Cong Guojing¹,Lim Seung-Hwan¹,Kulkarni Shruti¹,Date Prasanna¹,Potok Thomas¹,Snyder Shay²,Parsa Maryam³,Schuman Catherine⁴

Affiliation:

1. Oak Ridge National Laboratory, USA

2. East Tennessee State University, USA

3. George Mason University, USA

4. University of Tennessee, USA

Funder

US department of energy

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3546790.3546821

Reference21 articles.

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2. Provable Neuromorphic Advantages for Computing Shortest Paths

3. Graph Convolutional Networks with Kalman Filtering for Traffic Prediction

4. Versatile feature learning with graph convolutions and graph structures

5. Robin de Schepper Jochen Martin Eppler Anno Kurth Pooja Nagendra Babu Rajalekshmi Deepu Sebastian Spreizer Guido Trensch Jari Pronold Stine Brekke Vennemo Steffen Graber Aitor Morales-Gregorio Charl Linssen Mohamed Ayssar Benelhedi Håkon Mørk Abigail Morrison Dennis Terhorst Jessica Mitchell Sandra Diaz Itaru Kitayama Mahdi Enan Nilton Liuji Kamiji and Hans Ekkehard Plesser. 2022. NEST 3.2. https://doi.org/10.5281/zenodo.5886894 10.5281/zenodo.5886894

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