2024
-
papercode
Guide-and-Rescale: Self-Guidance Mechanism for Effective Tuning-Free Real Image Editing
by
Vadim Titov,
Madina Khalmatova,
Alexandra Ivanova,
Dmitry Vetrov,
Aibek Alanov.
Eighteenth European Conference on Computer Vision (ECCV 2024)
-
paper
Neural Diffusion Models
by
Grigory Bartosh,
Dmitry Vetrov,
Christian A. Naesseth.
Forty-first International Conference on Machine Learning (ICML 2024)
-
papercode
Gradual Optimization Learning for Conformational Energy Minimization
by
Artem Tsypin,
Leonid Ugadiarov,
Kuzma Khrabrov,
Alexander Telepov,
Egor Rumiantsev,
Alexey Skrynnik,
Aleksandr Panov,
Dmitry Vetrov,
Elena Tutubalina,
Artur Kadurin.
The Twelfth International Conference on Learning Representations (ICLR 2024)
-
paper
The Devil is in the Details: StyleFeatureEditor for Detail-Rich StyleGAN Inversion and High Quality Image Editing
by
Denis Bobkov,
Vadim Titov,
Aibek Alanov,
Dmitry Vetrov.
The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (CVPR 2024)
-
papercode
Differentiable Rendering with Reparameterized Volume Sampling
by
Nikita Morozov,
Denis Rakitin,
Oleg Desheulin,
Dmitry Vetrov,
Kirill Struminsky.
Twenty-seventh International Conference on Artificial Intelligence and Statistics (AISTATS 2024)
-
papercode
Generative Flow Networks as Entropy-Regularized RL
by
Daniil Tiapkin,
Nikita Morozov,
Alexey Naumov,
Dmitry Vetrov.
Twenty-seventh International Conference on Artificial Intelligence and Statistics (AISTATS 2024)
-
paper
Regularized Distribution Matching Distillation for One-step Unpaired Image-to-Image Translation
by
Denis Rakitin,
Ivan Shchekotov,
Dmitry Vetrov.
ICML 2024 Workshop on Structured Probabilistic Inference & Generative Modeling (ICML Workshop 2024)
-
paper
Improving GFlowNets with Monte Carlo Tree Search
by
Nikita Morozov,
Daniil Tiapkin,
Sergey Samsonov,
Alexey Naumov,
Dmitry Vetrov.
ICML 2024 Workshop on Structured Probabilistic Inference & Generative Modeling (ICML Workshop 2024)
2023
-
paper
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning
by
Ildus Sadrtdinov,
Dmitrii Pozdeev,
Dmitry Vetrov,
Ekaterina Lobacheva.
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023)
-
papercode
Star-Shaped Denoising Diffusion Probabilistic Models
by
Andrey Okhotin,
Dmitry Molchanov,
Vladimir Arkhipkin,
Grigory Bartosh,
Victor Oganesyan,
Aibek Alanov,
Dmitry Vetrov.
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023)
-
papercode
Entropic Neural Optimal Transport via Diffusion Processes
by
Nikita Gushchin,
Alexander Kolesov,
Alexander Korotin,
Dmitry Vetrov,
Evgeny Burnaev.
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023)
-
papercode
StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation
by
Aibek Alanov,
Vadim Titov,
Maksim Nakhodnov,
Dmitry Vetrov.
International Conference on Computer Vision (ICCV 2023)
-
papercode
UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model
by
Anastasiia Iashchenko,
Pavel Andreev,
Ivan Shchekotov,
Nicholas Babaev,
Dmitry Vetrov.
24th INTERSPEECH Conference (Interspeech 2023)
-
paper
HIFI++: A Unified Framework for Bandwidth Extension and Speech Enhancement
by
Pavel Andreev,
Aibek Alanov,
Oleg Ivanov,
Dmitry Vetrov.
2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023)
-
paper
Iterative autoregression: a novel trick to improve your low-latency speech enhancement model
by
Pavel Andreev,
Nicholas Babaev,
Ivan Shchekotov,
Azat Saginbaev,
Aibek Alanov.
24th INTERSPEECH Conference (Interspeech 2023)
-
papercode
MARS: Masked Automatic Ranks Selection in Tensor Decompositions
by
Maxim Kodryan,
Dmitry Kropotov,
Dmitry Vetrov.
The 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023)
-
paper
Differentiable Rendering with Reparameterized Volume Sampling
by
Nikita Morozov,
Denis Rakitin,
Oleg Desheulin,
Dmitry Vetrov,
Kirill Struminsky.
Neural Fields, ICLR 2023 Workshop (ICLR Workshop 2023)
-
paper
Weight Averaging Improves Knowledge Distillation under Domain Shift
by
Valeriy Berezovskiy and
Nikita Morozov.
The 2nd Workshop and Challenges for Out-of-Distribution Generalization in Computer Vision, ICCV 2023 (ICCV Workshop 2023)
-
paper
Large Learning Rates Improve Generalization: But How Large Are We Talking About?
by
Ekaterina Lobacheva,
Eduard Pockonechnyy,
Maxim Kodryan,
Dmitry Vetrov.
NeurIPS Workshop Mathematics of Modern Machine Learning (M3L) (NeurIPS 2023 Workshop M3L)
2022
-
papercode
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Network
by
Aibek Alanov,
Vadim Titov,
Dmitry Vetrov.
Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
-
paper
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes
by
Maxim Kodryan,
Ekaterina Lobacheva,
Maksim Nakhodnov,
Dmitry Vetrov.
Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
-
paper
Study on precoding optimization algorithms in massive MIMO system with multi-antenna users
by
Evgeny Bobrov,
Dmitry Kropotov,
Sergey Troshin,
Danila Zaev.
Optimization Methods and Software 2022
-
paper
FFC-SE: Fast Fourier Convolution for Speech Enhancement
by
Ivan Shchekotov,
Pavel Andreev,
Oleg Ivanov,
Aibek Alanov,
Dmitry Vetrov.
23rd INTERSPEECH Conference (INTERSPEECH 2022)
-
paper
Variational Autoencoders for Precoding Matrices with High Spectral Efficiency
by
Evgeny Bobrov,
Alexander Markov,
Sviatoslav Panchenko,
Dmitry Vetrov.
21st International Conference Mathematical Optimization Theory and Operations Research: Recent Trends (MOTOR 2022)
-
paper
Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning Algorithm
by
Evgeny Bobrov,
Dmitry Kropotov,
Hao Lu,
Danila Zaev.
IEEE Communications Letters (Volume: 26, Issue: 4, April 2022)
2021
2020
-
papervideocode
On Power Laws in Deep Ensembles
by
Ekaterina Lobacheva,
Nadezhda Chirkova,
Maxim Kodryan,
Dmitry Vetrov.
Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020)
-
paper
A Randomized Coordinate Descent Method with Volume Sampling
by
Anton Rodomanov and
Dmitry Kropotov.
SIAM Journal on Optimization
-
paper
On Power Laws in Deep Ensembles
by
Ekaterina Lobacheva,
Nadezhda Chirkova,
Maxim Kodryan,
Dmitry Vetrov.
Workshop on Uncertainty and Robustness in Deep Learning, ICML 2020
-
paper
Involutive MCMC: One Way to Derive Them All
by
Kirill Neklyudov,
Max Welling,
Evgenii Egorov,
Dmitry Vetrov.
Thirty-seventh International Conference on Machine Learning (ICML 2020)
-
papervideocode
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
by
Arseny Kuznetsov,
Pavel Shvechikov,
Alexander Grishin,
Dmitry Vetrov.
Thirty-seventh International Conference on Machine Learning (ICML 2020)
-
paper
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
by
Dmitry Molchanov,
Alexander Lyzhov,
Yuliya Molchanova,
Arsenii Ashukha,
Dmitry Vetrov.
Conference on Uncertainty in Artificial Intelligence (UAI 2020)
-
papercode
Deterministic Decoding for Discrete Data in Variational Autoencoders
by
Daniil Polykovskiy and
Dmitry Vetrov.
The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020)
-
paper
Stochasticity in Neural ODEs: An Empirical Study
by
Victor Oganesyan,
Alexandra Volokhova,
Dmitry Vetrov.
Integration of Deep Neural Models and Differential Equations, ICLR 2020 Workshop
-
papercode
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
by
Arsenii Ashukha,
Alexander Lyzhov,
Dmitry Molchanov,
Dmitry Vetrov.
Eighth International Conference on Learning Representations (ICLR 2020)
-
papercode
Structured Sparsification of Gated Recurrent Neural Networks
by
Ekaterina Lobacheva,
Nadezhda Chirkova,
Aleksandr Markovich,
Dmitry Vetrov.
Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)
-
papercode
Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution
by
Artyom Gadetsky,
Kirill Struminsky,
Christopher Robinson,
Novi Quadrianto,
Dmitry Vetrov.
Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)
2019
-
Structured Sparsification of Gated Recurrent Neural Networks
by
Ekaterina Lobacheva,
Nadezhda Chirkova,
Alexander Markovich,
Dmitry Vetrov.
Context and Compositionality in Biological and Artificial Neural Systems, NeurIPS 2019 Workshop
-
paper
Unsupervised Domain Adaptation with Shared Latent Dynamics for Reinforcement Learning
by
Evgenii Nikishin,
Arsenii Ashukha,
Dmitry Vetrov.
Bayesian Deep Learning, NeurIPS 2019 Workshop
-
Low-variance Gradient Estimates for the Plackett-Luce Distribution
by
Artyom Gadetsky,
Kirill Struminsky,
Christopher Robinson,
Novi Quadrianto,
Dmitry Vetrov.
Bayesian Deep Learning, NeurIPS 2019 Workshop
-
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
by
Arsenii Ashukha,
Alexander Lyzhov,
Dmitry Molchanov,
Dmitry Vetrov.
Bayesian Deep Learning, NeurIPS 2019 Workshop
-
paper
A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models
by
Maxim Kuznetsov,
Daniil Polykovskiy,
Dmitry Vetrov,
Alexander Zhebrak.
Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019)
-
papervideo
A Simple Baseline for Bayesian Uncertainty in Deep Learning
by
Wesley Maddox,
Timur Garipov,
Pavel Izmailov,
Dmitry Vetrov,
Andrew Gordon Wilson.
Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019)
-
paper
The Implicit Metropolis-Hastings Algorithm
by
Kirill Neklyudov,
Evgenii Egorov,
Dmitry Vetrov.
Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019)
-
papervideocode
Importance Weighted Hierarchical Variational Inference
by
Artem Sobolev and
Dmitry Vetrov.
Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019)
-
papercode
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
by
Alex Zhavoronkov,
Yan Ivanenkov,
Alex Aliper,
Mark Veselov,
Vladimir Aladinskiy,
Anastasiya Aladinskaya,
Victor Terentiev,
Daniil Polykovskiy,
Maxim Kuznetsov,
Arip Asadulaev,
Yury Volkov,
Artem Zholus,
Rim Shayakhmetov,
Alexander Zhebrak,
Lidiya Minaeva,
Bogdan Zagribelnyy,
Lennart H. Lee,
Richard Soll,
David Madge,
Li Xing,
Tao Guo,
Alán Aspuru-Guzik.
Nature Biotechnology
-
papercode
Subspace Inference for Bayesian Deep Learning
by
Pavel Izmailov,
Wesley J. Maddox,
Polina Kirichenko,
Timur Garipov,
Dmitry Vetrov,
Andrew Gordon Wilson.
The 35th Uncertainty in Artificial Intelligence Conference (UAI 2019)
-
paper
Semi-Conditional Normalizing Flows for Semi-Supervised Learning
by
Andrei Atanov,
Alexandra Volokhova,
Arsenii Ashukha,
Ivan Sosnovik,
Dmitry Vetrov.
Workshop on Invertible Neural Nets and Normalizing Flows, International Conference on Machine Learning (ICML) 2019
-
papervideo
Variational Autoencoder with Arbitrary Conditioning
by
Oleg Ivanov,
Michael Figurnov,
Dmitry Vetrov.
Seventh International Conference on Learning Representations (ICLR 2019)
-
papervideocode
Variance Networks: When Expectation Does Not Meet Your Expectations
by
Kirill Neklyudov,
Dmitry Molchanov,
Arsenii Ashukha,
Dmitry Vetrov.
Seventh International Conference on Learning Representations (ICLR 2019)
-
papervideo
The Deep Weight Prior
by
Andrei Atanov,
Arsenii Ashukha,
Kirill Struminsky,
Dmitry Vetrov,
Max Welling.
Seventh International Conference on Learning Representations (ICLR 2019)
-
papervideo
Doubly Semi-Implicit Variational Inference
by
Dmitry Molchanov,
Valery Kharitonov,
Artem Sobolev,
Dmitry Vetrov.
The 22st International Conference on Artificial Intelligence and Statistics (AISTATS 2019)
2018
-
paper
Probabilistic Adaptive Computation Time
by
Michael Figurnov,
Artem Sobolev,
Dmitry Vetrov.
Bulletin of the Polish Academy of Sciences: Technical Sciences 2018
-
paper
Bayesian Sparsification of Gated Recurrent Neural Networks
by
Ekaterina Lobacheva,
Nadezhda Chirkova,
Dmitry Vetrov.
Compact Deep Neural Network Representation with Industrial Applications NIPS 2018 Workshop
-
paper
Joint Belief Tracking and Reward Optimization through Approximate Inference
by
Pavel Shvechikov,
Alexander Grishin,
Arseny Kuznetsov,
Alexander Fritzler,
Dmitry Vetrov.
Reinforcement Learning under Partial Observability NeurIPS 2018 Workshop
-
paper
Importance Weighted Hierarchical Variational Inference
by
Artem Sobolev and
Dmitry Vetrov.
Bayesian Deep Learning NIPS 2018 Workshop
-
paper
Variational Dropout via Empirical Bayes
by
Valery Kharitonov,
Dmitry Molchanov,
Dmitry Vetrov.
Bayesian Deep Learning NIPS 2018 Workshop
-
paper
Subset-Conditioned Generation Using Variational Autoencoder With A Learnable Tensor-Train Induced Prior
by
Maksim Kuznetsov,
Daniil Polykovskiy,
Dmitry Vetrov,
Alexander Zhebrak.
Bayesian Deep Learning NIPS 2018 Workshop
-
paper
Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery
by
Daniil Polykovskiy,
Alexander Zhebrak,
Dmitry Vetrov,
Yan Ivanenkov,
Vladimir Aladinskiy,
Polina Mamoshina,
Marine Bozdaganyan,
Alexander Aliper,
Alex Zhavoronkov,
Artur Kadurin.
Molecular Pharmaceutics Journal.
-
papervideo
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
by
Kirill Struminsky,
Simon Lacoste-Julien,
Anton Osokin.
Thirty-second Conference on Neural Information Processing Systems (NeurIPS 2018)
-
papervideocode
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
by
Timur Garipov,
Pavel Izmailov,
Dmitry Podoprikhin,
Dmitry Vetrov,
Andrew Gordon Wilson.
Thirty-second Conference on Neural Information Processing Systems (NeurIPS 2018)
-
paper
Bayesian Compression for Natural Language Processing
by
Nadezhda Chirkova,
Ekaterina Lobacheva,
Dmitry Vetrov.
2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018)
-
paper
Fast Uncertainty Estimates and Bayesian Model Averaging of DNNs
by
Wesley Maddox,
Timur Garipov,
Pavel Izmailov,
Dmitry Vetrov,
Andrew Gordon Wilson.
Uncertainty in Artificial Intelligence Workshop (UAI Workshop) 2018
-
paper
Improving Stability in Deep Reinforcement Learning with Weight Averaging
by
Evgenii Nikishin,
Pavel Izmailov,
Ben Athiwaratkun,
Dmitrii Podoprikhin,
Timur Garipov,
Pavel Shvechikov,
Dmitry Vetrov,
Andrew Gordon Wilson.
Uncertainty in Artificial Intelligence Workshop (UAI Workshop) 2018
-
paper
Averaging Weights Leads to Wider Optima and Better Generalization
by
Pavel Izmailov,
Dmitry Podoprikhin,
Timur Garipov,
Dmitry Vetrov,
Andrew Gordon Wilson.
Conference on Uncertainty in Artificial Intelligence 2018 (UAI 2018)
-
paper
Conditional Generators of Words Definitions
by
Artyom Gadetsky,
Ilya Yakubovskiy,
Dmitry Vetrov.
56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)
-
paper
SEARNN: Training RNNs with global-local losses
by
Rémi Leblond,
Jean-Baptiste Alayrac,
Anton Osokin,
Simon Lacoste-Julien.
International Conference on Learning Representations (ICLR) 2018
-
paper
Concorde: Morphological Agreement in Conversational Models
by
Daniil Polykovskiy,
Dmitry Soloviev,
Sergey Nikolenko.
10th Asian Conference on Machine Learning (ACML 2018)
-
paper
ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks
by
Iurii Kemaev,
Daniil Polykovskiy,
Dmitry Vetrov.
10th Asian Conference on Machine Learning (ACML 2018)
-
paper
Expressive power of recurrent neural networks
by
Valentin Khrulkov,
Alexander Novikov,
Ivan Oseledets.
Sixth International Conference on Learning Representations (ICLR 2018)
-
paper
Fast Adaptation in Generative Models with Generative Matching Networks
by
Sergey Bartunov and
Dmitry P. Vetrov.
The 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018
-
paper
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition
by
Pavel Izmailov,
Alexander Novikov,
Dmitry Kropotov.
The 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018
-
paper
Predictive model for bottomhole pressure based on machine learning
by
Pavel Spesivtsev,
Konstantin Sinkov,
Ivan Sofronov,
Anna Zimina,
Alexey Umnov,
Ramil Yarullin,
Dmitry Vetrov.
Journal of Petroleum Science and Engineering
-
papervideo
Uncertainty Estimation via Stochastic Batch Normalization
by
Andrei Atanov,
Arsenii Ashukha,
Dmitry Molchanov,
Kirill Neklyudov,
Dmitry Vetrov.
International Conference on Learning Representations Workshop (ICLR Workshop) 2018
-
paper
Bayesian Incremental Learning for Deep Neural Networks
by
Max Kochurov,
Timur Garipov,
Dmitry Podoprikhin,
Dmitry Molchanov,
Arsenii Ashukha,
Dmitry Vetrov.
International Conference on Learning Representations Workshop (ICLR Workshop) 2018
-
paper
Monotonic models for real-time dynamic malware detection
by
Alexander Chistyakov,
Ekaterina Lobacheva,
Alexander Shevelev,
Alexey Romanenko.
International Conference on Learning Representations Workshop (ICLR Workshop) 2018
2017
-
paper
Exponential Machines
by
Alexander Novikov,
Mikhail Trofimov,
Ivan Oseledets.
Bulletin of the Polish Academy of Sciences: Technical Sciences 2018
-
papervideocode
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
by
Kirill Neklyudov,
Dmitry Molchanov,
Arsenii Ashukha,
Dmitry Vetrov.
Thirty-first Conference on Neural Information Processing Systems (NIPS 2017)
-
papervideo
Spatially Adaptive Computation Time for Residual Networks
by
Michael Figurnov,
Maxwell Collins,
Yukun Zhu,
Li Zhang,
Jonathan Huang,
Dmitry Vetrov,
Ruslan Salakhutdinov.
Conference on Computer Vision and Pattern Recognition 2017 (CVPR 2017)
-
papervideocode
Variational Dropout Sparsifies Deep Neural Networks
by
Dmitry Molchanov,
Arsenii Ashukha,
Dmitry Vetrov.
International Conference on Machine Learning (ICML 2017)
-
paper
Bayesian Sparsification of Recurrent Neural Networks
by
Ekaterina Lobacheva,
Nadezhda Chirkova,
Dmitry Vetrov.
International Conference on Machine Learning (ICML) Workshop 2017
-
paper
Fast Adaptation in Generative Models with Generative Matching Networks
by
Sergey Bartunov and
Dmitry P. Vetrov.
International Conference on Learning Representations (ICLR) Workshop 2017
-
paper
Semantic embeddings for program behaviour patterns
by
Alexander Chistyakov,
Ekaterina Lobacheva,
Arseny Kuznetsov,
Alexey Romanenko.
International Conference on Learning Representations (ICLR) Workshop 2017
2016
-
paper
A Superlinearly-Convergent Proximal Newton-Type Method for the Optimization of Finite Sums
by
Anton Rodomanov and
Dmitry Kropotov.
International Conference on Machine Learning (ICML) 2016
-
paper
One-shot Learning with Memory-Augmented Neural Networks
by
Adam Santoro,
Sergey Bartunov,
Matthew Botvinick,
Daan Wierstra,
Timothy Lillicrap.
International Conference on Machine Learning (ICML) 2016
-
paper
Breaking Sticks and Ambiguities with Adaptive Skip-gram
by
Sergey Bartunov,
Dmitry Kondrashkin,
Anton Osokin,
Dmitry Vetrov.
International Conference on Artificial Intelligence and Statistics (AISTATS) 2016
-
paper
Deep Part-Based Generative Shape Model with Latent Variables
by
Alexander Kirillov,
Mikhail Gavrikov,
Ekaterina Lobacheva,
Anton Osokin,
Dmitry Vetrov.
British Machine Vision Conference 2016 (BMVC 2016)
-
paper
A New Approach for Sparse Bayesian Channel Estimation in SCMA Uplink Systems
by
Kirill Struminsky,
Stanislav Kruglik,
Dmitry Vetrov,
Ivan Oseledets.
International Conference on Wireless Communications and Signal Processing (WCSP) 2016
-
paper
Tensor Train polynomial model via Riemannian optimization
by
Alexander Novikov,
Mikhail Trofimov,
Ivan Oseledets.
(ICML) 2016. Advances in non-convex analysis and optimization Workshop
-
paper
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
by
Michael Figurnov,
Dmitry Vetrov,
Pushmeet Kohli.
International Conference on Learning Representations (ICLR) 2016 Workshop track
-
paper
Dropout-based Automatic Relevance Determination
by
Dmitry Molchanov,
Arseniy Ashuha,
Dmitry Vetrov.
Advances in Neural Information Processing Systems (NIPS) 2016
-
paper
Robust Variational Inference
by
Michael Figurnov,
Kirill Struminsky,
Dmitry Vetrov.
Advances in Neural Information Processing Systems (NIPS) 2016
-
paper
Ultimate tensorization: convolutions and FC alike
by
Timur Garipov,
Dmitry Podoprikhin,
Alexander Novikov,
Dmitry Vetrov.
Advances in Neural Information Processing Systems (NIPS) 2016
2015
-
paper
A Newton-type Incremental Method with a Superlinear Convergence Rate
by
A. Rodomanov and
D. Kropotov.
NIPS 2015 Workshop on Optimization for Machine Learning
-
paper
Inferring M-Best Diverse Labelings in a Single One
by
A. Kirillov,
B. Savchynskyy,
D. Schlesinger,
D. Vetrov,
C. Rother.
Proceedings of the International Conference on Computer Vision (ICCV). 2015
-
paper
Joint Optimization of Segmentation and Color Clustering
by
Ekaterina Lobacheva,
Olga Veksler,
Yuri Boykov.
2015 International Conference on Computer Vision (ICCV 2015)
-
paper
Tensorizing Neural Networks
by
Alexander Novikov,
Dmitry Podoprikhin,
Anton Osokin,
Dmitry Vetrov.
In Advances in Neural Information Processing Systems 28 (NIPS). 2015
-
paper
M-Best-Diverse Labelings for Submodular Energies and Beyond
by
A. Kirillov,
D. Schlesinger,
D. Vetrov,
C. Rother,
B. Savchynskyy.
In Advances in Neural Information Processing Systems 28 (NIPS). 2015
-
paper
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions
by
R. Shapovalov,
A. Osokin,
D. Vetrov,
P. Kohli.
In Proceedings of International Workhop on Energy Minimization Methods (EMMCVPR2015), 2015
-
paper
Learning representations in directed networks
by
Oleg Ivanov and
Sergey Bartunov.
4th Conference on Analysis of Images, Social Networks, and Texts (AIST), 2015. Best conference paper award
2014
-
paper
IEEE Transactions on Pattern Analysis and Machine Intelligence
by
A. Osokin and
D. Vetrov. Submodular relaxation for inference in Markov random fields.
(TPAMI). Accepted. 2014
-
paper
Perceptually Inspired Layout-aware Losses for Image Segmentation
by
A. Osokin and
P. Kohli.
European Conference on Computer Vision (ECCV), 2014
-
paper
Variational Inference for Sequential Distance Dependent Chinese Restaurant Process
by
S. Bartunov and
D. Vetrov.
Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 2014. JMLR: W&CP volume 32
-
paper
Putting MRFs on a Tensor Train
by
A. Novikov,
A. Rodomanov,
A. Osokin,
D. Vetrov.
Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 2014. JMLR: W&CP volume 32
2013
-
paper
Learning a Model for Shape-Constrained Image Segmentation from Weakly Labeled Data
by
B. Yangel and
D. Vetrov.
In Proceedings of International Workhop on Energy Minimization Methods (EMMCVPR2013), 2013
-
paper
In Computer Vision and Pattern Recognition
by
R. Shapovalov,
D. Vetrov,
P. Kohli. Spatial Inference Machines.
(CVPR), 2013
-
paper
A Principled Deep Random Field Model for Image Segmentation
by
P. Kohli,
A. Osokin,
S. Jegelka.
In Computer Vision and Pattern Recognition (CVPR), 2013
-
paper
Automatic Determination of Cell Division Rate Using Microscope Images
by
K. Nekrasov,
D. Laptev,
D. Vetrov.
Pattern Recognition and Image Analysis, 23(1):1–6, 2013
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paper
An Approach to Segmentation of Mouse Brain Imagesvia Intermodal Registration
by
P. Voronin,
D. Vetrov,
K. Ismailov.
Pattern Recognition and Image Analysis, 23(2):335–339, 2013
2012
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paper
Fast Approximate Energy Minimization with Label Costs
by
A. Delong,
A. Osokin,
H. Isack,
Y. Boykov.
International Journal of Computer Vision (IJCV), 96(1):1–27, 2012
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paper
Submodular Relaxation for MRFs with High-Order Potentials
by
A. Osokin and
D. Vetrov.
HiPot: ECCV 2012 Workshop on Higher-Order Models and Global Constraints in Computer Vision, 2012
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paper
Minimizing Sparse High-Order Energies by Submodular Vertex-Cover
by
A. Delong,
O. Veksler,
A. Osokin,
Y. Boykov.
Advances in Neural Information Processing Systems (NIPS), 2012
2011
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paper
MRF Energy Minimization Approach with Epitomic Textural Global Term for Image Segmentation Problems
by
D. Elshin and
D. Kropotov.
In Proceedings of Bilateral Russian-Indian Workshop on Emerging Applications of Computer Vision, 2011
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paper
Submodular Decomposition Framework for Inference in Associative Markov Networks with Global Constraints
by
A. Osokin,
D. Vetrov,
V. Kolmogorov.
Proceedings ofInternational Conference on Computer Vision and Pattern Recognition (CVPR), 2011
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paper
Image Segmentation with a Shape Prior Based on Simplified Skeleton
by
B. Yangel and
D. Vetrov.
Proceedings of International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), LNCS 6819, 2011
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paper
Graph Preserving Label Decomposition in Discrete MRFs with Selfish Potentials
by
D. Vetrov and
A. Osokin.
Proceedings of NIPS Workshop on Discrete Optimization in Machine learning (DISCML NIPS), 2011
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paper
An Interactive Method of Anatomical Segmentation and Gene Expression Estimation for an Experimental Mouse Brain Slice
by
A. Osokin,
D. Vetrov,
A. Lebedev,
V. Galatenko,
D. Kropotov,
K. Anokhin.
Lecture Notes in Computer Science (LNCS), vol. 6685, pp. 86–97, 2011
2010
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paper
Fast Approximate Energy Minimization with Label Costs
by
A. Delong,
A. Osokin,
H. Isack,
Y. Boykov.
Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR), 2010
-
paper
Variational Relevance Vector Machine for Tabular Data
by
D. Kropotov,
D. Vetrov,
L. Wolf,
T. Hassner.
Proceedings of Asian Conference on Machine Learning (ACML), JMLR Workshop & Conference Proceedings, vol. 13, pp. 79-94, 2010
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paper
The Algorithm for Detection of Fuzzy Behavioral Patterns
by
V. Vishnevsky and
D. Vetrov.
Proceedings of International Conference on Methods and Techniques in Behavioral Research, ISBN 978-90-74821-86-5, 2010
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paper
3D Reconstruction of Mouse Brain from a Sequence of 2D Brain Slices in Application to Allen Brain Atlas
by
A. Osokin,
D. Vetrov,
D. Kropotov.
Lecture Notes in Computer Science (LNCS), vol. 6160, pp. 291-303, 2010
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paper
Variational Segmentation Algorithms with Label Frequency Constraints
by
D. Kropotov,
D. Laptev,
A. Osokin,
D. Vetrov.
Pattern Recognition and Image Analysis, vol. 20, no. 3, pp. 324-334, 2010
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paper
Intermodal Registration Algorithm for Segmentation of Mouse Brain Images
by
P. Voronin and
D. Vetrov.
Proceedings of International Conference on Pattern Recognition and Image Analysis (PRIA), vol. 2, pp. 377-381, 2010
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paper
Automatic Detection of Cell Division Intensity in Budding Yeast
by
K. Nekrasov,
D. Laptev,
D. Vetrov.
Proceedings of International Conference on Pattern Recognition and Image Analysis (PRIA), vol. 2, pp. 335-339, 2010
2009
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paper
Relevant Regressors Selection by Continuous AIC
by
D. Kropotov,
N. Ptashko,
D. Vetrov.
Pattern Recognition and Image Analysis, vol. 19, no. 3, pp. 456-464, 2009
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paper
General Solutions for Information-Based and Bayesian Approaches to Model Selection in Linear Regression and Their Equivalence
by
D. Kropotov and
D. Vetrov.
Pattern Recognition and Image Analysis, vol. 19, no. 3, pp. 447-455, 2009
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paper
Video Tracking and Behaviour Segmentation of Laboratory Rodents
by
E. Lomakina-Rumyantseva,
P. Voronin,
D. Kropotov,
D. Vetrov,
A. Konushin.
Pattern Recognition and Image Analysis, vol. 19, no. 4, pp. 616-622, 2009
2008
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paper
An Automatic Relevance Determination Procedure Based on Akaike Information Criterion for Linear Regression Problems
by
D. Kropotov and
D. Vetrov.
Proceedings of ICML Workshop on Sparse Optimization and Variable Selection, 2008
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paper
Automatic segmentation of mouse behavior using hidden markov models
by
D. Vetrov,
D. Kropotov,
A. Konushin,
E. Lomakina-Rumyantseva,
I. Zarayskaya,
K. Anokhin.
Proceedings of International Conference on Methods and Techniques in Behavioral Research, 2008
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paper
Automated distinguishing of mouse behavior in new environment and under amphetamine using decision trees
by
A. Konushin,
E. Lomakina-Rumyantseva,
D. Kropotov,
D. Vetrov,
A. Cherepov,
K. Anokhin.
Proceedings of International Conference on Methods and Techniques in Behavioral Research, 2008
2007
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paper
On One Method of Non-Diagonal Regularization in Sparse Bayesian Learning
by
D. Kropotov and
D. Vetrov.
Proceedings of International Conference on Machine Learning (ICML), pp. 457-464, 2007
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paper
Fuzzy Rules Generation Method for Pattern Recognition Problems
by
D. Kropotov and
D. Vetrov.
Lecture Notes in Computer Science (LNCS), vol. 4578, pp. 203–210, 2007