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07/21/2023
Transferability of Convolutional Neural Networks in Stationary Learning Tasks
Recent advances in hardware and big data acquisition have accelerated th...
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06/14/2023
Solving Large-scale Spatial Problems with Convolutional Neural Networks
Over the past decade, deep learning research has been accelerated by inc...
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05/19/2022
Graph Neural Networks Are More Powerful Than we Think
Graph Neural Networks (GNNs) are powerful convolutional architectures th...
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10/06/2021
Space-Time Graph Neural Networks
We introduce space-time graph neural network (ST-GNN), a novel GNN archi...
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11/03/2020
GAGE: Geometry Preserving Attributed Graph Embeddings
Node representation learning is the task of extracting concise and infor...
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10/22/2020
TeX-Graph: Coupled tensor-matrix knowledge-graph embedding for COVID-19 drug repurposing
Knowledge graphs (KGs) are powerful tools that codify relational behavio...
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03/25/2020
Generalized Canonical Correlation Analysis: A Subspace Intersection Approach
Generalized Canonical Correlation Analysis (GCCA) is an important tool t...
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10/26/2019
PREMA: Principled Tensor Data Recovery from Multiple Aggregated Views
Multidimensional data have become ubiquitous and are frequently involved...
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04/24/2018
