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Valentin Khrulkov

Diffusion models, low-rank tensor decompositions, unsupervised and semisupervised learning, theoretical analysis of neural networks. Especially, I am interested in applications of various geometrical (differential, algebraic, topological) ideas and techniques to neural networks to further our understanding of deep learning.

Publications

  • Hyperbolic Vision Transformers: Combining Improvements in Metric Learning

    RankingRepresentationsComputer vision
    Aleksandr Ermolov
    Leyla Mirvakhabova
    Valentin Khrulkov
    Nicu Sebe
    Ivan Oseledets
    CVPR,
    2022

    Metric learning aims to learn a highly discriminative model encouraging the embeddings of similar classes to be close in the chosen metrics and pushed apart for dissimilar ones. The common recipe is to use an encoder to extract embeddings and a distance-based loss function to match the representations – usually, the Euclidean distance is utilized. An emerging interest in learning hyperbolic data embeddings suggests that hyperbolic geometry can be beneficial for natural data. Following this line of work, we propose a new hyperbolic-based model for metric learning. At the core of our method is a vision transformer with output embeddings mapped to hyperbolic space. These embeddings are directly optimized using modified pairwise cross-entropy loss. We evaluate the proposed model with six different formulations on four datasets achieving the new state-of-the-art performance. The source code is available at https://github.com/htdt/hyp_metric

  • Label-Efficient Semantic Segmentation with Diffusion Models

    SegmentationComputer vision
    Dmitry Baranchuk
    Ivan Rubachev
    Andrey Voynov
    Valentin Khrulkov
    Artem Babenko
    ICLR,
    2022

    Denoising diffusion probabilistic models have recently received much research attention since they outperform alternative approaches, such as GANs, and currently provide state-of-the-art generative performance. The superior performance of diffusion models has made them an appealing tool in several applications, including inpainting, super-resolution, and semantic editing. In this paper, we demonstrate that diffusion models can also serve as an instrument for semantic segmentation, especially in the setup when labeled data is scarce. In particular, for several pretrained diffusion models, we investigate the intermediate activations from the networks that perform the Markov step of the reverse diffusion process. We show that these activations effectively capture the semantic information from an input image and appear to be excellent pixel-level representations for the segmentation problem. Based on these observations, we describe a simple segmentation method, which can work even if only a few training images are provided. Our approach significantly outperforms the existing alternatives on several datasets for the same amount of human supervision.

  • Revisiting Deep Learning Models for Tabular Data

    Tabular data
    Yury Gorishniy
    Ivan Rubachev
    Valentin Khrulkov
    Artem Babenko
    NeurIPS,
    2021

    The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports competitive results on various datasets. However, the proposed models are usually not properly compared to each other and existing works often use different benchmarks and experiment protocols. As a result, it is unclear for both researchers and practitioners what models perform best. Additionally, the field still lacks effective baselines, that is, the easy-to-use models that provide competitive performance across different problems. In this work, we perform an overview of the main families of DL architectures for tabular data and raise the bar of baselines in tabular DL by identifying two simple and powerful deep architectures. The first one is a ResNet-like architecture which turns out to be a strong baseline that is often missing in prior works. The second model is our simple adaptation of the Transformer architecture for tabular data, which outperforms other solutions on most tasks. Both models are compared to many existing architectures on a diverse set of tasks under the same training and tuning protocols. We also compare the best DL models with Gradient Boosted Decision Trees and conclude that there is still no universally superior solution. The source code is available at https://github.com/yandex-research/rtdl.

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