GraViti introduces a graph-level VAE with relaxed permutation invariance that maps whole graphs to latent vectors, achieves strong reconstruction on large molecular datasets, and generates valid samples by learning constraints directly from graph-level representations.
Focal loss for dense object detection.IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2):318–327
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GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance
GraViti introduces a graph-level VAE with relaxed permutation invariance that maps whole graphs to latent vectors, achieves strong reconstruction on large molecular datasets, and generates valid samples by learning constraints directly from graph-level representations.