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10/12/2021
Relative Molecule Self-Attention Transformer
Self-supervised learning holds promise to revolutionize molecule propert...
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10/07/2021
On the relationship between disentanglement and multi-task learning
One of the main arguments behind studying disentangled representations i...
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09/18/2021
PluGeN: Multi-Label Conditional Generation From Pre-Trained Models
Modern generative models achieve excellent quality in a variety of tasks...
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08/10/2021
Flow-based SVDD for anomaly detection
We propose FlowSVDD – a flow-based one-class classifier for anomaly/outl...
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11/23/2020
Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction
Graph neural networks have recently become a standard method for analysi...
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10/26/2020
Processing of incomplete images by (graph) convolutional neural networks
We investigate the problem of training neural networks from incomplete i...
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10/06/2020
Flow-based anomaly detection
We propose OneFlow - a flow-based one-class classifier for anomaly (outl...
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02/19/2020
Molecule Attention Transformer
Designing a single neural network architecture that performs competitive...
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09/11/2019
Geometric Graph Convolutional Neural Networks
Graph Convolutional Networks (GCNs) have recently become the primary cho...
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03/01/2019
Non-linear ICA based on Cramer-Wold metric
Non-linear source separation is a challenging open problem with many app...
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02/27/2019
Multi-task hypernetworks
Hypernetworks mechanism allows to generate and train neural networks (ta...
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02/20/2019
LOSSGRAD: automatic learning rate in gradient descent
In this paper, we propose a simple, fast and easy to implement algorithm...
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02/06/2019
Mol-CycleGAN - a generative model for molecular optimization
Designing a molecule with desired properties is one of the biggest chall...
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10/03/2018