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01/18/2023
ReFresh: Reducing Memory Access from Exploiting Stable Historical Embeddings for Graph Neural Network Training
A key performance bottleneck when training graph neural network (GNN) mo...
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11/28/2022
DGI: Easy and Efficient Inference for GNNs
While many systems have been developed to train Graph Neural Networks (G...
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07/29/2022
BiFeat: Supercharge GNN Training via Graph Feature Quantization
Graph Neural Networks (GNNs) is a promising approach for applications wi...
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05/21/2022
CEP3: Community Event Prediction with Neural Point Process on Graph
Many real world applications can be formulated as event forecasting on C...
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12/31/2021
Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs
Graph neural networks (GNN) have shown great success in learning from gr...
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10/22/2021
Gaussian Graphical Model Selection for Huge Data via Minipatch Learning
Gaussian graphical models are essential unsupervised learning techniques...
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04/13/2021
Thresholded Graphical Lasso Adjusts for Latent Variables: Application to Functional Neural Connectivity
In neuroscience, researchers seek to uncover the connectivity of neurons...
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10/11/2020
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
Graph neural networks (GNN) have shown great success in learning from gr...
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08/26/2020
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems
Graph neural networks (GNNs) are gaining increasing popularity as a prom...
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07/08/2020
Sparse Regression for Extreme Values
We study the problem of selecting features associated with extreme value...
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05/25/2020
Supervised Convex Clustering
Clustering has long been a popular unsupervised learning approach to ide...
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12/11/2019
Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data
In mixed multi-view data, multiple sets of diverse features are measured...
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02/02/2019
Learned Indexes for Dynamic Workloads
The recent proposal of learned index structures opens up a new perspecti...
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07/24/2018
Supporting Very Large Models using Automatic Dataflow Graph Partitioning
There is a trend towards using very large deep neural networks (DNN) to ...
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05/10/2018
Unifying Data, Model and Hybrid Parallelism in Deep Learning via Tensor Tiling
Deep learning systems have become vital tools across many fields, but th...
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12/03/2015
