
Generation of the NIR spectral Band for Satellite Images with Convolutional Neural Networks
The nearinfrared (NIR) spectral range (from 780 to 2500 nm) of the mult...
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ObjectBased Augmentation Improves Quality of Remote SensingSemantic Segmentation
Today deep convolutional neural networks (CNNs) push the limits for most...
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Dynamic Modeling of User Preferences for Stable Recommendations
In domains where users tend to develop longterm preferences that do not...
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Automatic differentiation for Riemannian optimization on lowrank matrix and tensortrain manifolds
In scientific computing and machine learning applications, matrices and ...
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Optimal soil sampling design based on the maxvol algorithm
Spatial soil sampling is an integral part of a soil survey aimed at crea...
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MetaSolver for Neural Ordinary Differential Equations
A conventional approach to train neural ordinary differential equations ...
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Solution of the FokkerPlanck equation by cross approximation method in the tensor train format
We propose the novel numerical scheme for solution of the multidimension...
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Disentangled Representations from NonDisentangled Models
Constructing disentangled representations is known to be a difficult tas...
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Functional Space Analysis of Local GAN Convergence
Recent work demonstrated the benefits of studying continuoustime dynami...
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Denoising Score Matching with Random Fourier Features
The density estimation is one of the core problems in statistics. Despit...
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Robustness Threats of Differential Privacy
Differential privacy is a powerful and goldstandard concept of measurin...
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Adversarial Turing Patterns from Cellular Automata
Stateoftheart deep classifiers are intriguingly vulnerable to univers...
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LETI: Latency Estimation Tool and Investigation of Neural Networks inference on Mobile GPU
A lot of deep learning applications are desired to be run on mobile devi...
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Performance of Hyperbolic Geometry Models on TopN Recommendation Tasks
We introduce a simple autoencoder based on hyperbolic geometry for solvi...
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Stable Lowrank Tensor Decomposition for Compression of Convolutional Neural Network
Most state of the art deep neural networks are overparameterized and exh...
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Follow the bisector: a simple method for multiobjective optimization
This study presents a novel Equiangular Direction Method (EDM) to solve ...
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GeometryInspired Topk Adversarial Perturbations
Stateoftheart deep learning models are untrustworthy due to their vul...
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FREDE: LinearSpace Anytime Graph Embeddings
Lowdimensional representations, or embeddings, of a graph's nodes facil...
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Tensorized Transformer for Dynamical Systems Modeling
The identification of nonlinear dynamics from observations is essential ...
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Simple heuristics for efficient parallel tensor contraction and quantum circuit simulation
Tensor networks are the main building blocks in a wide variety of comput...
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Towards Understanding Normalization in Neural ODEs
Normalization is an important and vastly investigated technique in deep ...
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Stochastic gradient algorithms from ODE splitting perspective
We present a different view on stochastic optimization, which goes back ...
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Bayesian aggregation improves traditional single image crop classification approaches
Machine learning (ML) methods and neural networks (NN) are widely implem...
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Interpolated Adjoint Method for Neural ODEs
In this paper, we propose a method, which allows us to alleviate or comp...
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Using Reinforcement Learning in the Algorithmic Trading Problem
The development of reinforced learning methods has extended application ...
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Deep Representation Learning for Dynamical Systems Modeling
Proper states' representations are the key to the successful dynamics mo...
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Randomized Algorithms for Computation of Tucker decomposition and Higher Order SVD (HOSVD)
Big data analysis has become a crucial part of new emerging technologies...
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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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An adaptive algorithm for quantum circuit simulation
Efficient simulation of quantum computers is essential for the developme...
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Active Subspace of Neural Networks: Structural Analysis and Universal Attacks
Active subspace is a model reduction method widely used in the uncertain...
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Growing axons: greedy learning of neural networks with application to function approximation
We propose a new method for learning deep neural network models that is ...
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Graph Convolutional Policy for Solving Tree Decomposition via Reinforcement Learning Heuristics
We propose a Reinforcement Learning based approach to approximately solv...
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ReducedOrder Modeling of Deep Neural Networks
We introduce a new method for speeding up the inference of deep neural n...
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Predicting dynamical system evolution with residual neural networks
Forecasting time series and timedependent data is a common problem in m...
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Practical shift choice in the shiftandinvert Krylov subspace evaluations of the matrix exponential
We propose two methods to find a proper shift parameter in the shiftand...
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Microstructure synthesis using stylebased generative adversarial network
Work considers the usage of StyleGAN architecture for the task of micros...
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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...
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Universality Theorems for Generative Models
Despite the fact that generative models are extremely successful in prac...
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Intrinsic Multiscale Evaluation of Generative Models
Generative models are often used to sample highdimensional data points ...
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Hyperbolic Image Embeddings
Computer vision tasks such as image classification, image retrieval and ...
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One time is not enough: iterative tensor decomposition for neural network compression
The lowrank tensor approximation is very promising for the compression ...
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Preconditioning Kaczmarz method by sketching
We propose a new method for preconditioning Kaczmarz method by sketching...
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Generalized Tensor Models for Recurrent Neural Networks
Recurrent Neural Networks (RNNs) are very successful at solving challeng...
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Tensorized Embedding Layers for Efficient Model Compression
The embedding layers transforming input words into real vectors are the ...
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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...
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PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
With deep neural networks providing stateoftheart machine learning mo...
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Adversarial point set registration
We present a novel approach to point set registration which is based on ...
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Tensor Networks for Latent Variable Analysis: Higher Order Canonical Polyadic Decomposition
The Canonical Polyadic decomposition (CPD) is a convenient and intuitive...
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Modelling hidden structure of signals in group data analysis with modified (Lr, 1) and blockterm decompositions
This work is devoted to elaboration on the idea to use block term decomp...
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Revealing the Unobserved by Linking Collaborative Behavior and Side Knowledge
We propose a tensorbased model that fuses a more granular representatio...
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Ivan Oseledets
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Associate Professor at Skolkovo Institute of Science and Technology