Skoltech

∙ Moscow , Russian Federation ∙ 0 followers

http://www.skoltech.ru/en The Skolkovo Institute of Science and Technology (Skoltech) is a private graduate research university in Skolkovo, Russia, a suburb of Moscow. Established in October 2011 in collaboration with MIT, Skoltech educates global lea...

Hyperbolic Image Embeddings

Computer vision tasks such as image classification, image retrieval and ...
04/03/2019 ∙ by Valentin Khrulkov, et al. ∙ 26 ∙ share

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Perceptually-based single-image depth super-resolution

RGBD images, combining high-resolution color and lower-resolution depth ...
12/24/2018 ∙ by O. Voinov, et al. ∙ 20 ∙ share

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Instance Segmentation of Biological Images Using Harmonic Embeddings

We present a new instance segmentation approach tailored to biological i...
04/10/2019 ∙ by Victor Kulikov, et al. ∙ 20 ∙ share

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Stochastic gradient algorithms from ODE splitting perspective

We present a different view on stochastic optimization, which goes back ...
04/19/2020 ∙ by Daniil Merkulov, et al. ∙ 19 ∙ share

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Tensor Completion via Gaussian Process Based Initialization

In this paper, we consider the tensor completion problem representing th...
12/11/2019 ∙ by Yermek Kapushev, et al. ∙ 17 ∙ share

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Real-world attack on MTCNN face detection system

Recent studies proved that deep learning approaches achieve remarkable r...
10/14/2019 ∙ by Edgar Kaziakhmedov, et al. ∙ 16 ∙ share

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Deep Neural Networks Predicting Oil Movement in a Development Unit

We present a novel technique for assessing the dynamics of multiphase fl...
01/08/2019 ∙ by Pavel Temirchev, et al. ∙ 12 ∙ share

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Interpolated Adjoint Method for Neural ODEs

In this paper, we propose a method, which allows us to alleviate or comp...
03/11/2020 ∙ by Talgat Daulbaev, et al. ∙ 12 ∙ share

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Adversarial point set registration

We present a novel approach to point set registration which is based on ...
11/20/2018 ∙ by Sergei Divakov, et al. ∙ 10 ∙ share

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Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds

Constructing high-quality generative models for 3D shapes is a fundament...
12/13/2019 ∙ by Vage Egiazarian, et al. ∙ 10 ∙ share

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Generalized Tensor Models for Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are very successful at solving challeng...
01/30/2019 ∙ by Valentin Khrulkov, et al. ∙ 8 ∙ share

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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...
08/15/2019 ∙ by Anna Kuzina, et al. ∙ 8 ∙ share

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Tumor Delineation For Brain Radiosurgery by a ConvNet and Non-Uniform Patch Generation

Deep learning methods are actively used for brain lesion segmentation. O...
08/01/2018 ∙ by Egor Krivov, et al. ∙ 6 ∙ share

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Image Manipulation with Perceptual Discriminators

Systems that perform image manipulation using deep convolutional network...
09/05/2018 ∙ by Diana Sungatullina, et al. ∙ 4 ∙ share

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Universality Theorems for Generative Models

Despite the fact that generative models are extremely successful in prac...
05/27/2019 ∙ by Valentin Khrulkov, et al. ∙ 4 ∙ share

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Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection

Midline shift (MLS) is a well-established factor used for outcome predic...
08/13/2019 ∙ by Maxim Pisov, et al. ∙ 3 ∙ share

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Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks

Traditional physics-based approaches to infer sub-surface properties suc...
05/22/2018 ∙ by Lukas Mosser, et al. ∙ 2 ∙ share

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Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

Data-driven methods such as convolutional neural networks (CNNs) are kno...
05/20/2019 ∙ by Maria Kolos, et al. ∙ 2 ∙ share

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Empirical study of extreme overfitting points of neural networks

In this paper we propose a method of obtaining points of extreme overfit...
06/14/2019 ∙ by Daniil Merkulov, et al. ∙ 2 ∙ share

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Bayesian aggregation improves traditional single image crop classification approaches

Machine learning (ML) methods and neural networks (NN) are widely implem...
04/07/2020 ∙ by Ivan Matvienko, et al. ∙ 2 ∙ share

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Towards Understanding Normalization in Neural ODEs

Normalization is an important and vastly investigated technique in deep ...
04/20/2020 ∙ by Julia Gusak, et al. ∙ 2 ∙ share

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Monocular 3D Object Detection via Geometric Reasoning on Keypoints

Monocular 3D object detection is well-known to be a challenging vision t...
05/14/2019 ∙ by Ivan Barabanau, et al. ∙ 1 ∙ share

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Boundary Loss for Remote Sensing Imagery Semantic Segmentation

In response to the growing importance of geospatial data, its analysis i...
05/20/2019 ∙ by Alexey Bokhovkin, et al. ∙ 1 ∙ share

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Stereo relative pose from line and point feature triplets

Stereo relative pose problem lies at the core of stereo visual odometry ...
06/29/2019 ∙ by Alexander Vakhitov, et al. ∙ 1 ∙ share

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Deep Image Prior

Deep convolutional networks have become a popular tool for image generat...
11/29/2017 ∙ by Dmitry Ulyanov, et al. ∙ 0 ∙ share

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Universal Denoising Networks : A Novel CNN-based Network Architecture for Image Denoising

We design a novel network architecture for learning discriminative image...
11/21/2017 ∙ by Stamatios Lefkimmiatis, et al. ∙ 0 ∙ share

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Long-Term Sequential Prediction Using Expert Advice

For the prediction with expert advice setting, we consider methods to co...
11/08/2017 ∙ by Eugeny Burnaev, et al. ∙ 0 ∙ share

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Quantified advantage of discontinuous weight selection in approximations with deep neural networks

We consider approximations of 1D Lipschitz functions by deep ReLU networ...
05/03/2017 ∙ by Dmitry Yarotsky, et al. ∙ 0 ∙ share

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Art of singular vectors and universal adversarial perturbations

Vulnerability of Deep Neural Networks (DNNs) to adversarial attacks has ...
09/11/2017 ∙ by Valentin Khrulkov, et al. ∙ 0 ∙ share

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Simultaneous Matrix Diagonalization for Structural Brain Networks Classification

This paper considers the problem of brain disease classification based o...
10/14/2017 ∙ by Nikita Mokrov, et al. ∙ 0 ∙ share

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Error bounds for approximations with deep ReLU networks

We study expressive power of shallow and deep neural networks with piece...
10/03/2016 ∙ by Dmitry Yarotsky, et al. ∙ 0 ∙ share

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Forecasting of commercial sales with large scale Gaussian Processes

This paper argues that there has not been enough discussion in the field...
09/16/2017 ∙ by Rodrigo Rivera, et al. ∙ 0 ∙ share

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Large Scale Variable Fidelity Surrogate Modeling

Engineers widely use Gaussian process regression framework to construct ...
07/12/2017 ∙ by Evgeny Burnaev, et al. ∙ 0 ∙ share

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Inductive Conformal Martingales for Change-Point Detection

We consider the problem of quickest change-point detection in data strea...
06/11/2017 ∙ by Denis Volkhonskiy, et al. ∙ 0 ∙ share

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Conformal k-NN Anomaly Detector for Univariate Data Streams

Anomalies in time-series data give essential and often actionable inform...
06/11/2017 ∙ by Vladislav Ishimtsev, et al. ∙ 0 ∙ share

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Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems

In this paper, we propose a novel ranking framework for collaborative fi...
04/29/2017 ∙ by Sumit Sidana, et al. ∙ 0 ∙ share

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Non-Local Color Image Denoising with Convolutional Neural Networks

We propose a novel deep network architecture for grayscale and color ima...
11/21/2016 ∙ by Stamatios Lefkimmiatis, et al. ∙ 0 ∙ share

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It Takes (Only) Two: Adversarial Generator-Encoder Networks

We present a new autoencoder-type architecture that is trainable in an u...
04/07/2017 ∙ by Dmitry Ulyanov, et al. ∙ 0 ∙ share

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Machine Learning in Appearance-based Robot Self-localization

An appearance-based robot self-localization problem is considered in the...
06/17/2017 ∙ by Alexander Kuleshov, et al. ∙ 0 ∙ share

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Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering

Cold start problem in Collaborative Filtering can be solved by asking ne...
10/16/2016 ∙ by Alexander Fonarev, et al. ∙ 0 ∙ share

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Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models

We present a new deep learning architecture (called Kd-network) that is ...
04/04/2017 ∙ by Roman Klokov, et al. ∙ 0 ∙ share

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Minimax Error of Interpolation and Optimal Design of Experiments for Variable Fidelity Data

Engineering problems often involve data sources of variable fidelity wit...
10/21/2016 ∙ by Alexey Zaytsev, et al. ∙ 0 ∙ share

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Steganographic Generative Adversarial Networks

Steganography is collection of methods to hide secret information ("payl...
03/16/2017 ∙ by Denis Volkhonskiy, et al. ∙ 0 ∙ share

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Conformalized density- and distance-based anomaly detection in time-series data

Anomalies (unusual patterns) in time-series data give essential, and oft...
08/16/2016 ∙ by Evgeny Burnaev, et al. ∙ 0 ∙ share

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Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks

Conventional collaborative filtering techniques treat a top-n recommenda...
07/14/2016 ∙ by Evgeny Frolov, et al. ∙ 0 ∙ share

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Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification

In the recent years there have been a number of studies that applied dee...
01/23/2017 ∙ by Sergey Korolev, et al. ∙ 0 ∙ share

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Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis

The recent work of Gatys et al., who characterized the style of an image...
01/09/2017 ∙ by Dmitry Ulyanov, et al. ∙ 0 ∙ share

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Tensor SimRank for Heterogeneous Information Networks

We propose a generalization of SimRank similarity measure for heterogene...
02/24/2015 ∙ by Ben Usman, et al. ∙ 0 ∙ share

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Set2Model Networks: Learning Discriminatively To Learn Generative Models

We present a new "learning-to-learn"-type approach that enables rapid le...
12/22/2016 ∙ by A. Vakhitov, et al. ∙ 0 ∙ share

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Parsing Images of Overlapping Organisms with Deep Singling-Out Networks

This work is motivated by the mostly unsolved task of parsing biological...
12/19/2016 ∙ by Victor Yurchenko, et al. ∙ 0 ∙ share

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