115 citations · 383 across the 27 of their papers we have counts for
8 papers · 1 filter
Revisiting Deep Learning Models for Tabular Data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov +1
The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports competitive results on various datasets. However, the proposed mo…
Disentangled Representations from Non-Disentangled Models
Valentin Khrulkov, Leyla Mirvakhabova, Ivan Oseledets +1
Constructing disentangled representations is known to be a difficult task, especially in the unsupervised scenario. The dominating paradigm of unsupervised disentanglement is curre…
Functional Space Analysis of Local GAN Convergence
Valentin Khrulkov, Artem Babenko, Ivan Oseledets
Recent work demonstrated the benefits of studying continuous-time dynamics governing the GAN training. However, this dynamics is analyzed in the model parameter space, which result…
Sample Efficient Ensemble Learning with Catalyst.RL
Sergey Kolesnikov, Valentin Khrulkov
We present Catalyst.RL, an open-source PyTorch framework for reproducible and sample efficient reinforcement learning (RL) research. Main features of Catalyst.RL include large-scal…
Universality Theorems for Generative Models
Valentin Khrulkov, Ivan Oseledets
Despite the fact that generative models are extremely successful in practice, the theory underlying this phenomenon is only starting to catch up with practice. In this work we addr…
Generalized Tensor Models for Recurrent Neural Networks
Valentin Khrulkov, Oleksii Hrinchuk, Ivan Oseledets
Recurrent Neural Networks (RNNs) are very successful at solving challenging problems with sequential data. However, this observed efficiency is not yet entirely explained by theory…
Geometry Score: A Method For Comparing Generative Adversarial Networks
Valentin Khrulkov, Ivan Oseledets
One of the biggest challenges in the research of generative adversarial networks (GANs) is assessing the quality of generated samples and detecting various levels of mode collapse.…
Expressive power of recurrent neural networks
Valentin Khrulkov, Alexander Novikov, Ivan Oseledets
Deep neural networks are surprisingly efficient at solving practical tasks, but the theory behind this phenomenon is only starting to catch up with the practice. Numerous works sho…