12 citations · 33 across the 12 of their papers we have counts for
15 papers
Towards Computationally Feasible Deep Active Learning
Akim Tsvigun, Artem Shelmanov, Gleb Kuzmin +5
Active learning (AL) is a prominent technique for reducing the annotation effort required for training machine learning models. Deep learning offers a solution for several essentia…
Adversarial Attacks on Deep Models for Financial Transaction Records
Ivan Fursov, Matvey Morozov, Nina Kaploukhaya +7
Machine learning models using transaction records as inputs are popular among financial institutions. The most efficient models use deep-learning architectures similar to those in…
PvDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3D
Kseniya Cherenkova, Djamila Aouada, Gleb Gusev
We propose a Point-Voxel DeConvolution (PVDeConv) module for 3D data autoencoder. To demonstrate its efficiency we learn to synthesize high-resolution point clouds of 10k points th…
SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results
Alexandre Saint, Anis Kacem, Kseniya Cherenkova +7
The SHApe Recovery from Partial textured 3D scans challenge, SHARP 2020, is the first edition of a challenge fostering and benchmarking methods for recovering complete textured 3D…
Minimal Variance Sampling in Stochastic Gradient Boosting
Bulat Ibragimov, Gleb Gusev
Stochastic Gradient Boosting (SGB) is a widely used approach to regularization of boosting models based on decision trees. It was shown that, in many cases, random sampling at each…
Latent Distribution Assumption for Unbiased and Consistent Consensus Modelling
Valentina Fedorova, Gleb Gusev, Pavel Serdyukov
We study the problem of aggregation noisy labels. Usually, it is solved by proposing a stochastic model for the process of generating noisy labels and then estimating the model par…