Search Results for author:
Found 6 papers, 2 papers with code
Unsupervised Learning of Group Invariant and Equivariant Representations
In this work, we extend group invariant and equivariant representation learning to the field of unsupervised deep learning.
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
In this work we address this issue by proposing a permutation-invariant variational autoencoder for graph structured data.
Auto-Encoding Molecular Conformations
In this work we introduce an Autoencoder for molecular conformations.
DeepPCM: Predicting Protein-Ligand Binding using Unsupervised Learned Representations
We apply this reasoning to propose a novel proteochemometric modeling methodology which, for the first time, uses embeddings generated via unsupervised representation learning for both the protein and ligand descriptors.
Learning Continuous and Data-Driven Molecular Descriptors by Translating Equivalent Chemical Representations
In this work, we propose to exploit the powerful ability of deep neural networks to learn a feature representation from low-level encodings of a huge corpus of chemical structures.
IVE-GAN: Invariant Encoding Generative Adversarial Networks
Although GANs can learn a rich representation of the covered modes of the data in their latent space, the framework misses an inverse mapping from data to this latent space.
