
Machine learning for recovery factor estimation of an oil reservoir: a tool for derisking at a hydrocarbon asset evaluation
Well known oil recovery factor estimation techniques such as analogy, vo...
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Towards forecast techniques for business analysts of large commercial data sets using matrix factorization methods
This research article suggests that there are significant benefits in ex...
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Topologybased Clusterwise Regression for User Segmentation and Demand Forecasting
Topological Data Analysis (TDA) is a recent approach to analyze data set...
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Graph Neural Networks for Model Recommendation using Time Series Data
Time series prediction aims to predict future values to help stakeholder...
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Addressing Cold Start in Recommender Systems with Hierarchical Graph Neural Networks
Recommender systems have become an essential instrument in a wide range ...
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Topological Data Analysis for Portfolio Management of Cryptocurrencies
Portfolio management is essential for any investment decision. Yet, trad...
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An industry case of largescale demand forecasting of hierarchical components
Demand forecasting of hierarchical components is essential in manufactur...
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DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data
This work proposes DeepFolio, a new model for deep portfolio management ...
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Geometric Attention for Prediction of Differential Properties in 3D Point Clouds
Estimation of differential geometric quantities in discrete 3D data repr...
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Making DensePose fast and light
DensePose estimation task is a significant step forward for enhancing us...
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Latent Video Transformer
The video generation task can be formulated as a prediction of future vi...
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Multifidelity Neural Architecture Search with Knowledge Distillation
Neural architecture search (NAS) targets at finding the optimal architec...
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NASBenchNLP: Neural Architecture Search Benchmark for Natural Language Processing
Neural Architecture Search (NAS) is a promising and rapidly evolving res...
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Recurrent Convolutional Neural Networks help to predict location of Earthquakes
We examine the applicability of modern neural network architectures to t...
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Bayesian Sparsification Methods for Deep Complexvalued Networks
With continual miniaturization ever more applications of deep learning c...
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Datadriven models and computational tools for neurolinguistics: a language technology perspective
In this paper, our focus is the connection and influence of language tec...
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Software System for Road Condition Forecast Correction
In this paper, we present a monitoring system that allows increasing roa...
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Deep Vectorization of Technical Drawings
We present a new method for vectorization of technical line drawings, su...
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Gradientbased adversarial attacks on categorical sequence models via traversing an embedded world
An adversarial attack paradigm explores various scenarios for vulnerabil...
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Reinforcement Learning for Combinatorial Optimization: A Survey
Combinatorial optimization (CO) is the workhorse of numerous important a...
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Integral Mixabilty: a Tool for Efficient Online Aggregation of Functional and Probabilistic Forecasts
In this paper we extend the setting of the online prediction with expert...
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LatentSpace Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds
Constructing highquality generative models for 3D shapes is a fundament...
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Tensor Completion via Gaussian Process Based Initialization
In this paper, we consider the tensor completion problem representing th...
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Barcodes as summary of objective function's topology
We apply the canonical forms (barcodes) of gradient Morse complexes to e...
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3D Deformable Convolutions for MRI classification
Deep learning convolutional neural networks have proved to be a powerful...
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Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem
Segmentation of tumors in brain MRI images is a challenging task, where ...
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Understanding Isomorphism Bias in Graph Data Sets
In recent years there has been a rapid increase in classification method...
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Wasserstein2 Generative Networks
Modern generative learning is mainly associated with Generative Adversar...
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BooVAE: A scalable framework for continual VAE learning under boosting approach
Variational Auto Encoders (VAE) are capable of generating realistic imag...
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Understanding Cyber Athletes Behaviour Through a Smart Chair: CS:GO and Monolith Team Scenario
eSports is the rapidly developing multidisciplinary domain. However, res...
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Sensors and Game Synchronization for Data Analysis in eSports
eSports industry has greatly progressed within the last decade in terms ...
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Towards Understanding of eSports Athletes' Potentialities: The Sensing System for Data Collection and Analysis
eSports is a developing multidisciplinary research area. At present, the...
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eSports ProPlayers Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart Chair
Today's competition between the professional eSports teams is so strong ...
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Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems
Automatic segmentation methods based on deep learning have recently demo...
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Learning to Approximate Directional Fields Defined over 2D Planes
Reconstruction of directional fields is a need in many geometry processi...
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Visual Fixations Duration as an Indicator of Skill Level in eSports
Using highly interactive systems like computer games requires a lot of v...
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Rare Failure Prediction via Event Matching for Aerospace Applications
In this paper, we consider a problem of failure prediction in the contex...
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Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction
In this work, we aim at predicting children's fluid intelligence scores ...
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A Predictive Model for SteadyState Multiphase Pipe Flow: Machine Learning on Lab Data
Engineering simulators used for steadystate multiphase pipe flows are c...
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Demand forecasting techniques for buildtoorder lean manufacturing supply chains
Buildtoorder (BTO) supply chains have become commonplace in industrie...
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Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks
Datadriven methods such as convolutional neural networks (CNNs) are kno...
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Artificial Neural Network Surrogate Modeling of Oil Reservoir: a Case Study
We develop a datadriven model, introducing recent advances in machine l...
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MaxEntropy Pursuit Variational Inference
One of the core problems in variational inference is a choice of approxi...
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Boundary Loss for Remote Sensing Imagery Semantic Segmentation
In response to the growing importance of geospatial data, its analysis i...
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Monocular 3D Object Detection via Geometric Reasoning on Keypoints
Monocular 3D object detection is wellknown to be a challenging vision t...
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UserControllable MultiTexture Synthesis with Generative Adversarial Networks
We propose a novel multitexture synthesis model based on generative adv...
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Realtime datadriven detection of the rock type alteration during a directional drilling
During the directional drilling, a bit may sometimes go to a nonproducti...
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Adaptive Hedging under Delayed Feedback
The article is devoted to investigating the application of hedging strat...
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Gradient Boosting to Boost the Efficiency of Hydraulic Fracturing
In this paper we present a datadriven model for forecasting the product...
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Reconstruction of 3D Porous Media From 2D Slices
We propose a novel deep learning architecture for threedimensional poro...
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Evgeny Burnaev
verfied profile
Associate Professor for Center for Computational and DataIntensive Science and Engineering