1 code implementation • 28 Aug 2023 • Ran Liu, Sahil Khose, Jingyun Xiao, Lakshmi Sathidevi, Keerthan Ramnath, Zsolt Kira, Eva L. Dyer
To address this challenge, we propose a novel approach for distribution-aware latent augmentation that leverages the relationships across samples to guide the augmentation procedure.
1 code implementation • 17 Aug 2023 • Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Veličković, Eva L. Dyer
Message passing neural networks have shown a lot of success on graph-structured data.
Ranked #1 on Node Classification on AMZ Comp
1 code implementation • 1 Jan 2023 • Jorge Quesada, Lakshmi Sathidevi, Ran Liu, Nauman Ahad, Joy M. Jackson, Mehdi Azabou, Jingyun Xiao, Christopher Liding, Matthew Jin, Carolina Urzay, William Gray-Roncal, Erik C. Johnson, Eva L. Dyer
To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image.
1 code implementation • 27 Apr 2022 • Rishov Sarkar, Stefan Abi-Karam, Yuqi He, Lakshmi Sathidevi, Cong Hao
First, we propose a novel and scalable dataflow architecture, which generally supports a wide range of GNN models with message-passing mechanism.
1 code implementation • 20 Jan 2022 • Stefan Abi-Karam, Yuqi He, Rishov Sarkar, Lakshmi Sathidevi, Zihang Qiao, Cong Hao
Second, we aim to support a diverse set of GNN models with the extensibility to flexibly adapt to new models.