3 citations · 3 across the 5 of their papers we have counts for
5 papers
DSS: Synthesizing long Digital Ink using Data augmentation, Style encoding and Split generation
Aleksandr Timofeev, Anastasiia Fadeeva, Andrei Afonin +2
As text generative models can give increasingly long answers, we tackle the problem of synthesizing long text in digital ink. We show that the commonly used models for this task fa…
Sampling and Ranking for Digital Ink Generation on a tight computational budget
Andrei Afonin, Andrii Maksai, Aleksandr Timofeev +1
Digital ink (online handwriting) generation has a number of potential applications for creating user-visible content, such as handwriting autocompletion, spelling correction, and b…
Can we learn gradients by Hamiltonian Neural Networks?
Aleksandr Timofeev, Andrei Afonin, Yehao Liu
In this work, we propose a meta-learner based on ODE neural networks that learns gradients. This approach makes the optimizer is more flexible inducing an automatic inductive bias…
Self-Supervised Neural Architecture Search for Imbalanced Datasets
Aleksandr Timofeev, Grigorios G. Chrysos, Volkan Cevher
Neural Architecture Search (NAS) provides state-of-the-art results when trained on well-curated datasets with annotated labels. However, annotating data or even having balanced num…
Manifold-based time series forecasting
Nikita Puchkin, Aleksandr Timofeev, Vladimir Spokoiny
Prediction for high dimensional time series is a challenging task due to the curse of dimensionality problem. Classical parametric models like ARIMA or VAR require strong modeling…