6 citations · 10 across the 2 of their papers we have counts for
4 papers
Deterministic Decoding for Discrete Data in Variational Autoencoders
Daniil Polykovskiy, Dmitry Vetrov
Variational autoencoders are prominent generative models for modeling discrete data. However, with flexible decoders, they tend to ignore the latent codes. In this paper, we study…
A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models
Maksim Kuznetsov, Daniil Polykovskiy, Dmitry Vetrov +1
Generative models produce realistic objects in many domains, including text, image, video, and audio synthesis. Most popular models---Generative Adversarial Networks (GANs) and Var…
ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks
Iurii Kemaev, Daniil Polykovskiy, Dmitry Vetrov
Neural Network is a powerful Machine Learning tool that shows outstanding performance in Computer Vision, Natural Language Processing, and Artificial Intelligence. In particular, r…
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling +13
Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with simila…