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11/11/2022
DistGNN-MB: Distributed Large-Scale Graph Neural Network Training on x86 via Minibatch Sampling
Training Graph Neural Networks, on graphs containing billions of vertice...
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04/14/2021
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks
Full-batch training on Graph Neural Networks (GNN) to learn the structur...
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04/12/2021
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning Workloads
During the past decade, novel Deep Learning (DL) algorithms/workloads an...
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07/13/2020
Deep Graph Library Optimizations for Intel(R) x86 Architecture
The Deep Graph Library (DGL) was designed as a tool to enable structure ...
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11/20/2018
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images
Hyperspectral images (HSI) contain a wealth of information over hundreds...
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07/22/2018
A Trace Lasso Regularized L1-norm Graph Cut for Highly Correlated Noisy Hyperspectral Image
This work proposes an adaptive trace lasso regularized L1-norm based gra...
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07/07/2018
A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images
The lack of proper class discrimination among the Hyperspectral (HS) dat...
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09/09/2017
Graph Scaling Cut with L1-Norm for Classification of Hyperspectral Images
In this paper, we propose an L1 normalized graph based dimensionality re...
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08/08/2017