1.1k citations · 1.2k across the 27 of their papers we have counts for
30 papers
Sparse Autoencoders for Sequential Recommendation Models: Interpretation and Flexible Control
Anton Klenitskiy, Konstantin Polev, Daria Denisova +3
Many current state-of-the-art models for sequential recommendations are based on transformer architectures. Interpretation and explanation of such black box models is an important…
Simplicial SMOTE: Oversampling Solution to the Imbalanced Learning Problem
Oleg Kachan, Andrey Savchenko, Gleb Gusev
SMOTE (Synthetic Minority Oversampling Technique) is the established geometric approach to random oversampling to balance classes in the imbalanced learning problem, followed by ma…
Multimodal Banking Dataset: Understanding Client Needs through Event Sequences
Dzhambulat Mollaev, Alexander Kostin, Maria Postnova +4
Financial organizations collect a huge amount of temporal (sequential) data about clients, which is typically collected from multiple sources (modalities). Despite the urgent pract…
TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds
Elona Dupont, Kseniya Cherenkova, Dimitrios Mallis +3
3D reverse engineering, in which a CAD model is inferred given a 3D scan of a physical object, is a research direction that offers many promising practical applications. This paper…
SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines
Dimitrios Mallis, Sk Aziz Ali, Elona Dupont +6
Recent breakthroughs in geometric Deep Learning (DL) and the availability of large Computer-Aided Design (CAD) datasets have advanced the research on learning CAD modeling processe…
SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes
Kseniya Cherenkova, Elona Dupont, Anis Kacem +3
3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas. Though the quality of 3D scans depends on the technical char…
CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations
Elona Dupont, Kseniya Cherenkova, Anis Kacem +4
3D reverse engineering is a long sought-after, yet not completely achieved goal in the Computer-Aided Design (CAD) industry. The objective is to recover the construction history of…
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…
CoLES: Contrastive Learning for Event Sequences with Self-Supervision
Dmitrii Babaev, Ivan Kireev, Nikita Ovsov +4
We address the problem of self-supervised learning on discrete event sequences generated by real-world users. Self-supervised learning incorporates complex information from the raw…
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…
Aggregation of pairwise comparisons with reduction of biases
Nadezhda Bugakova, Valentina Fedorova, Gleb Gusev +1
We study the problem of ranking from crowdsourced pairwise comparisons. Answers to pairwise tasks are known to be affected by the position of items on the screen, however, previous…
Online Evaluation for Effective Web Service Development
Roman Budylin, Alexey Drutsa, Gleb Gusev +2
Development of the majority of the leading web services and software products today is generally guided by data-driven decisions based on evaluation that ensures a steady stream of…
A survey on Deep Learning Advances on Different 3D Data Representations
Eman Ahmed, Alexandre Saint, Abd El Rahman Shabayek +5
3D data is a valuable asset the computer vision filed as it provides rich information about the full geometry of sensed objects and scenes. Recently, with the availability of both…
CatBoost: unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev +2
This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit. Their combination leads to CatBoost outperforming other publicly available boos…
Riemannian Optimization for Skip-Gram Negative Sampling
Alexander Fonarev, Oleksii Hrinchuk, Gleb Gusev +2
Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in "word2vec" software, is usually optimized by stochastic gradient descent. However, the…
User Model-Based Intent-Aware Metrics for Multilingual Search Evaluation
Alexey Drutsa, Andrey Shutovich, Philipp Pushnyakov +3
Despite the growing importance of multilingual aspect of web search, no appropriate offline metrics to evaluate its quality are proposed so far. At the same time, personal language…
Prediction of Video Popularity in the Absence of Reliable Data from Video Hosting Services: Utility of Traces Left by Users on the Web
Alexey Drutsa, Gleb Gusev, Pavel Serdyukov
With the growth of user-generated content, we observe the constant rise of the number of companies, such as search engines, content aggregators, etc., that operate with tremendous…
Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering
Alexander Fonarev, Alexander Mikhalev, Pavel Serdyukov +2
Cold start problem in Collaborative Filtering can be solved by asking new users to rate a small seed set of representative items or by asking representative users to rate a new ite…
Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods
Lev Bogolubsky, Pavel Dvurechensky, Alexander Gasnikov +5
In this paper, we consider a non-convex loss-minimization problem of learning Supervised PageRank models, which can account for some properties not considered by classical approach…
Lower Bounds for Multi-armed Bandit with Non-equivalent Multiple Plays
Aleksandr Vorobev, Gleb Gusev
We study the stochastic multi-armed bandit problem with non-equivalent multiple plays where, at each step, an agent chooses not only a set of arms, but also their order, which infl…