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Dmitry Molchanov

8 accepted papers

2025

TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model Encodings

Alexander Shabalin, Viacheslav Meshchaninov, Egor Chimbulatov, Vladislav Lapikov, Roman Kim, Grigory Bartosh +3
AAAI 2025technical

This paper presents the Text Encoding Diffusion Model (TEncDM), a novel approach to diffusion modeling that operates in the space of pre-trained language model encodings. In contrast to traditionally used embeddings, encodings integrate contextual information. In our approach, we also employ a trans…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2023

Star-Shaped Denoising Diffusion Probabilistic Models

Andrey Okhotin, Dmitry Molchanov, Arkhipkin Sergeevich Vladimir, Grigory Bartosh, Viktor Ohanesian, Aibek Alanov +1
NeurIPS 2023poster

Denoising Diffusion Probabilistic Models (DDPMs) provide the foundation for the recent breakthroughs in generative modeling. Their Markovian structure makes it difficult to define DDPMs with distributions other than Gaussian or discrete. In this paper, we introduce Star-Shaped DDPM (SS-DDPM). Its *s…

Cited by 11Source ↗PDF ↗code ↗Scholar ↗
2020

Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation

Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Molchanov, Dmitry Vetrov
UAI 2020poster

Test-time data augmentation—averaging the predictions of a machine learning model across multiple augmented samples of data—is a widely used technique that improves the predictive performance. While many advanced learnable data augmentation techniques have emerged in recent years, they are focused o…

Cited by 109Source ↗PDF ↗code ↗Scholar ↗
2020

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning

Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry Vetrov
ICLR 2020poster

Uncertainty estimation and ensembling methods go hand-in-hand. Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance. At the same time, deep learning ensembles have provided state-of-the-art results in uncertainty estimation. In this work, we focus on in-domai…

Cited by 412Source ↗Scholar ↗
2019

Variance Networks: When Expectation Does Not Meet Your Expectations

Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov
ICLR 2019poster

Ordinary stochastic neural networks mostly rely on the expected values of their weights to make predictions, whereas the induced noise is mostly used to capture the uncertainty, prevent overfitting and slightly boost the performance through test-time averaging. In this paper, we introduce variance l…

Cited by 37Source ↗PDF ↗code ↗Scholar ↗
2017

Structured Bayesian Pruning via Log-Normal Multiplicative Noise

Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P Vetrov
NeurIPS 2017poster

Dropout-based regularization methods can be regarded as injecting random noise with pre-defined magnitude to different parts of the neural network during training. It was recently shown that Bayesian dropout procedure not only improves gener- alization but also leads to extremely sparse neural archi…

Cited by 243Source ↗PDF ↗code ↗Scholar ↗