Dmitry P. Vetrov

Orcid: 0000-0001-6863-9028

Affiliations:
  • Samsung AI Center Moscow, Russia
  • National Research University Higher School of Economics (HSE), Moscow, Russia
  • Skolkovo Institute of Science and Technology, Moscow Russia
  • Moscow State University (MSU), Department of Computational Mathematics and Cybernetics, Russia


According to our database1, Dmitry P. Vetrov authored at least 113 papers between 2003 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Where Do Large Learning Rates Lead Us?
CoRR, 2024

Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling.
CoRR, 2024

HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach.
CoRR, 2024

Diffusion on language model embeddings for protein sequence generation.
CoRR, 2024

TEncDM: Understanding the Properties of Diffusion Model in the Space of Language Model Encodings.
CoRR, 2024

Star-Shaped Denoising Diffusion Probabilistic Models (Extended Abstract).
Proceedings of the KI 2024: Advances in Artificial Intelligence, 2024

Neural Diffusion Models.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Gradual Optimization Learning for Conformational Energy Minimization.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Guide-and-Rescale: Self-guidance Mechanism for Effective Tuning-Free Real Image Editing.
Proceedings of the Computer Vision - ECCV 2024, 2024

Generative Flow Networks as Entropy-Regularized RL.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Differentiable Rendering with Reparameterized Volume Sampling.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Large Learning Rates Improve Generalization: But How Large Are We Talking About?
CoRR, 2023

Gradual Optimization Learning for Conformational Energy Minimization.
CoRR, 2023

Star-Shaped Denoising Diffusion Probabilistic Models.
CoRR, 2023

To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Star-Shaped Denoising Diffusion Probabilistic Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Entropic Neural Optimal Transport via Diffusion Processes.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model.
Proceedings of the 24th Annual Conference of the International Speech Communication Association, 2023

StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

HIFI++: A Unified Framework for Bandwidth Extension and Speech Enhancement.
Proceedings of the IEEE International Conference on Acoustics, 2023

MARS: Masked Automatic Ranks Selection in Tensor Decompositions.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
StyleDomain: Analysis of StyleSpace for Domain Adaptation of StyleGAN.
CoRR, 2022

HiFi++: a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement.
CoRR, 2022

Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

FFC-SE: Fast Fourier Convolution for Speech Enhancement.
Proceedings of the 23rd Annual Conference of the International Speech Communication Association, 2022

2021
Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems.
CoRR, 2021

Automating Control of Overestimation Bias for Continuous Reinforcement Learning.
CoRR, 2021

Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study.
CoRR, 2021

Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations.
CoRR, 2021

Towards Practical Credit Assignment for Deep Reinforcement Learning.
CoRR, 2021

Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks.
CoRR, 2020

Deep Ensembles on a Fixed Memory Budget: One Wide Network or Several Thinner Ones?
CoRR, 2020

Stochasticity in Neural ODEs: An Empirical Study.
CoRR, 2020

User-controllable Multi-texture Synthesis with Generative Adversarial Networks.
Proceedings of the 15th International Joint Conference on Computer Vision, 2020

Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

On Power Laws in Deep Ensembles.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Involutive MCMC: a Unifying Framework.
Proceedings of the 37th International Conference on Machine Learning, 2020

Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics.
Proceedings of the 37th International Conference on Machine Learning, 2020

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning.
Proceedings of the 8th International Conference on Learning Representations, 2020

Deterministic Decoding for Discrete Data in Variational Autoencoders.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Structured Sparsification of Gated Recurrent Neural Networks.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Low-Variance Black-Box Gradient Estimates for the Plackett-Luce Distribution.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
MLRG Deep Curvature.
CoRR, 2019

Semi-Conditional Normalizing Flows for Semi-Supervised Learning.
CoRR, 2019

Subspace Inference for Bayesian Deep Learning.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Efficient Language Modeling with Automatic Relevance Determination in Recurrent Neural Networks.
Proceedings of the 4th Workshop on Representation Learning for NLP, 2019

Importance Weighted Hierarchical Variational Inference.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The Implicit Metropolis-Hastings Algorithm.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Simple Baseline for Bayesian Uncertainty in Deep Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Variance Networks: When Expectation Does Not Meet Your Expectations.
Proceedings of the 7th International Conference on Learning Representations, 2019

Variational Autoencoder with Arbitrary Conditioning.
Proceedings of the 7th International Conference on Learning Representations, 2019

The Deep Weight Prior.
Proceedings of the 7th International Conference on Learning Representations, 2019

Doubly Semi-Implicit Variational Inference.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

A Simple Method to Evaluate Support Size and Non-uniformity of a Decoder-Based Generative Model.
Proceedings of the Analysis of Images, Social Networks and Texts, 2019

2018
Bayesian Sparsification of Gated Recurrent Neural Networks.
CoRR, 2018

Variational Dropout via Empirical Bayes.
CoRR, 2018

Metropolis-Hastings view on variational inference and adversarial training.
CoRR, 2018

The Deep Weight Prior. Modeling a prior distribution for CNNs using generative models.
CoRR, 2018

Pairwise Augmented GANs with Adversarial Reconstruction Loss.
CoRR, 2018

Universal Conditional Machine.
CoRR, 2018

Averaging Weights Leads to Wider Optima and Better Generalization.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Bayesian Incremental Learning for Deep Neural Networks.
Proceedings of the 6th International Conference on Learning Representations, 2018

Uncertainty Estimation via Stochastic Batch Normalization.
Proceedings of the 6th International Conference on Learning Representations, 2018

Bayesian Compression for Natural Language Processing.
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

Few-shot Generative Modelling with Generative Matching Networks.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks.
Proceedings of The 10th Asian Conference on Machine Learning, 2018

Conditional Generators of Words Definitions.
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018

2017
Probabilistic Adaptive Computation Time.
CoRR, 2017

Bayesian Sparsification of Recurrent Neural Networks.
CoRR, 2017

Structured Bayesian Pruning via Log-Normal Multiplicative Noise.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variational Dropout Sparsifies Deep Neural Networks.
Proceedings of the 34th International Conference on Machine Learning, 2017

Fast Adaptation in Generative Models with Generative Matching Networks.
Proceedings of the 5th International Conference on Learning Representations, 2017

Spatially Adaptive Computation Time for Residual Networks.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016
Ultimate tensorization: compressing convolutional and FC layers alike.
CoRR, 2016

Robust Variational Inference.
CoRR, 2016

GTApprox: Surrogate modeling for industrial design.
Adv. Eng. Softw., 2016

A new approach for sparse Bayesian channel estimation in SCMA uplink systems.
Proceedings of the 8th International Conference on Wireless Communications & Signal Processing, 2016

PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Deep Part-Based Generative Shape Model with Latent Variables.
Proceedings of the British Machine Vision Conference 2016, 2016

Breaking Sticks and Ambiguities with Adaptive Skip-gram.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Submodular Relaxation for Inference in Markov Random Fields.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions.
CoRR, 2015

Tensorizing Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

M-Best-Diverse Labelings for Submodular Energies and Beyond.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Inferring M-Best Diverse Labelings in a Single One.
Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

2014
Putting MRFs on a Tensor Train.
Proceedings of the 31th International Conference on Machine Learning, 2014

Variational Inference for Sequential Distance Dependent Chinese Restaurant Process.
Proceedings of the 31th International Conference on Machine Learning, 2014

Multi-utility Learning: Structured-Output Learning with Multiple Annotation-Specific Loss Functions.
Proceedings of the Energy Minimization Methods in Computer Vision and Pattern Recognition, 2014

2013
Машинное обучение - состояние и перспективы (Machine Learning: State of the Art and Perspectives).
Proceedings of the Selected Papers of the 15th All-Russian Scientific Conference "Digital libraries: Advanced Methods and Technologies, 2013

Learning a Model for Shape-Constrained Image Segmentation from Weakly Labeled Data.
Proceedings of the Energy Minimization Methods in Computer Vision and Pattern Recognition, 2013

Spatial Inference Machines.
Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

2012
Submodular Relaxation for MRFs with High-Order Potentials.
Proceedings of the Computer Vision - ECCV 2012. Workshops and Demonstrations, 2012

2011
Automated atlas-based segmentation of NISSL-stained mouse brain sections using supervised learning.
Program. Comput. Softw., 2011

Image Segmentation with a Shape Prior Based on Simplified Skeleton.
Proceedings of the Energy Minimazation Methods in Computer Vision and Pattern Recognition, 2011

Submodular decomposition framework for inference in associative Markov networks with global constraints.
Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 2011

2010
Variational Relevance Vector Machine for Tabular Data.
Proceedings of the 2nd Asian Conference on Machine Learning, 2010

An Interactive Method of Anatomical Segmentation and Gene Expression Estimation for an Experimental Mouse Brain Slice.
Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2010

2009
ODDboost: Incorporating Posterior Estimates into AdaBoost.
Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2009

3-D Mouse Brain Model Reconstruction from a Sequence of 2-D Slices in Application to Allen Brain Atlas.
Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2009

2007
Fuzzy Rules Generation Method for Pattern Recognition Problems.
Proceedings of the Applications of Fuzzy Sets Theory, 2007

On one method of non-diagonal regularization in sparse Bayesian learning.
Proceedings of the Machine Learning, 2007

2006
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization.
IEEE Trans. Pattern Anal. Mach. Intell., 2006

On Kernel Selection in Relevance Vector Machines Using Stability Principle.
Proceedings of the 18th International Conference on Pattern Recognition (ICPR 2006), 2006

The Use of Stability Principle for Kernel Determination in Relevance Vector Machines.
Proceedings of the Neural Information Processing, 13th International Conference, 2006

2005
The Use of Bayesian Framework for Kernel Selection in Vector Machines Classifiers.
Proceedings of the Progress in Pattern Recognition, 2005

2004
An Algorithm for Rule Generation in Fuzzy Expert Systems.
Proceedings of the 17th International Conference on Pattern Recognition, 2004

Data Dependent Classifier Fusion for Construction of Stable Effective Algorithms.
Proceedings of the 17th International Conference on Pattern Recognition, 2004

2003
One Approach to Fuzzy Expert Systems Construction.
Proceedings of the ICEIS 2003, 2003


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