Deep Graph Library Optimizations for Intel(R) x86 Architecture

13 July 2020

Papers citing "Deep Graph Library Optimizations for Intel(R) x86 Architecture"

5 / 5 papers shown

Title
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science Santiago Miret Kin Long Kelvin Lee Carmelo Gonzales Marcel Nassar Matthew Spellings 38 19 0 31 Oct 2022
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks Vasimuddin Sanchit Misra Guixiang Ma Ramanarayan Mohanty E. Georganas A. Heinecke Dhiraj D. Kalamkar Nesreen Ahmed Sasikanth Avancha GNN 33 119 0 14 Apr 2021
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads E. Georganas Dhiraj D. Kalamkar Sasikanth Avancha Menachem Adelman Deepti Aggarwal ... Ramanarayan Mohanty Hans Pabst Brian Retford Barukh Ziv A. Heinecke 26 17 0 12 Apr 2021
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks Minjie Wang Da Zheng Zihao Ye Quan Gan Mufei Li ... J. Zhao Haotong Zhang Alex Smola Jinyang Li Zheng-Wei Zhang AI4CE GNN 206 746 0 03 Sep 2019
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 251 1,811 0 25 Nov 2016