no code implementations • 17 May 2024 • Zhanguang Zhang, Didier Chetelat, Joseph Cotnareanu, Amur Ghose, Wenyi Xiao, Hui-Ling Zhen, Yingxue Zhang, Jianye Hao, Mark Coates, Mingxuan Yuan
In this paper we present GraSS, a novel approach for automatic SAT solver selection based on tripartite graph representations of instances and a heterogeneous graph neural network (GNN) model.
no code implementations • 6 Feb 2023 • Amur Ghose, Yingxue Zhang, Jianye Hao, Mark Coates
Contrastive learning has emerged as a premier method for learning representations with or without supervision.
no code implementations • 10 Nov 2021 • Amur Ghose, Vincent Zhang, Yingxue Zhang, Dong Li, Wulong Liu, Mark Coates
To address this limitation, we propose a framework that can directly learn embeddings for the given netlist to enhance the quality of our node features.
no code implementations • 25 Feb 2020 • Amur Ghose, Abdullah Rashwan, Pascal Poupart
The variational autoencoder is a well defined deep generative model that utilizes an encoder-decoder framework where an encoding neural network outputs a non-deterministic code for reconstructing an input.