no code implementations • 11 Nov 2022 • Md Vasimuddin, Ramanarayan Mohanty, Sanchit Misra, Sasikanth Avancha
DistGNN-MB trains GraphSAGE and GAT 10x and 17. 2x faster, respectively, as compute nodes scale from 2 to 32.
no code implementations • 14 Apr 2021 • Vasimuddin Md, Sanchit Misra, Guixiang Ma, Ramanarayan Mohanty, Evangelos Georganas, Alexander Heinecke, Dhiraj Kalamkar, Nesreen K. Ahmed, Sasikanth Avancha
Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible.
3 code implementations • 12 Apr 2021 • Evangelos Georganas, Dhiraj Kalamkar, Sasikanth Avancha, Menachem Adelman, Deepti Aggarwal, Cristina Anderson, Alexander Breuer, Jeremy Bruestle, Narendra Chaudhary, Abhisek Kundu, Denise Kutnick, Frank Laub, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Brian Retford, Barukh Ziv, Alexander Heinecke
The TPP specification is platform-agnostic, thus code expressed via TPPs is portable, whereas the TPP implementation is highly-optimized and platform-specific.
1 code implementation • 13 Jul 2020 • Sasikanth Avancha, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty
The Deep Graph Library (DGL) was designed as a tool to enable structure learning from graphs, by supporting a core abstraction for graphs, including the popular Graph Neural Networks (GNN).
no code implementations • 20 Nov 2018 • Ramanarayan Mohanty, SL Happy, Aurobinda Routray
The underlying idea of the proposed method is to exploit the limited labeled information from both the spectral and spatial domains along with the abundant unlabeled samples to facilitate the classification task by retaining the original distribution of the data.
no code implementations • 22 Jul 2018 • Ramanarayan Mohanty, S. L. Happy, Nilesh Suthar, Aurobinda Routray
The underlying idea of this method is to generate the optimal projection matrix by considering both the sparsity as well as the correlation of the data samples.
no code implementations • 7 Jul 2018 • Ramanarayan Mohanty, S. L. Happy, Aurobinda Routray
The underlying idea of this method is to obtain a linear projection matrix by solving a nonlinear objective function based on the intrinsic geometrical structure of the data.
no code implementations • 9 Sep 2017 • Ramanarayan Mohanty, S. L. Happy, Aurobinda Routray
The underlying idea of this method is to generate the optimal projection matrix by retaining the original distribution of the data.
Classification Of Hyperspectral Images Dimensionality Reduction +1
no code implementations • 8 Aug 2017 • S. L. Happy, Ramanarayan Mohanty, Aurobinda Routray
However, the efficacy of the selection criteria for low sample size applications is neglected in most cases.