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Spectral Augmentations for Graph Contrastive Learning
Contrastive learning has emerged as a premier method for learning representations with or without supervision.
Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction
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.
Batch norm with entropic regularization turns deterministic autoencoders into generative models
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.
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