Additive Quantization for Extreme Vector Compression. - Artem Babenko; Victor S. Lempitsky (CVPR, 2014)

Neural Codes for Image Retrieval. - Artem Babenko; Anton Slesarev; Alexander Chigorin; Victor S. Lempitsky (ECCV (1), 2014)

Improving Bilayer Product Quantization for Billion-Scale Approximate Nearest Neighbors in High Dimensions. - Artem Babenko; Victor S. Lempitsky (CoRR, 2014)

Tree quantization for large-scale similarity search and classification. - Artem Babenko; Victor S. Lempitsky (CVPR, 2015)

Aggregating Local Deep Features for Image Retrieval. - Artem Babenko; Victor S. Lempitsky (ICCV, 2015)

Aggregating Deep Convolutional Features for Image Retrieval. - Artem Babenko; Victor S. Lempitsky (CoRR, 2015)

The Inverted Multi-Index. - Artem Babenko; Victor S. Lempitsky (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Efficient Indexing of Billion-Scale Datasets of Deep Descriptors. - Artem Babenko; Victor S. Lempitsky (CVPR, 2016)

Pairwise Quantization. - Artem Babenko; Relja Arandjelovic; Victor S. Lempitsky (CoRR, 2016)

Product Split Trees. - Artem Babenko; Victor S. Lempitsky (CVPR, 2017)

AnnArbor: Approximate Nearest Neighbors Using Arborescence Coding. - Artem Babenko; Victor S. Lempitsky (ICCV, 2017)

Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors. - Dmitry Baranchuk; Artem Babenko; Yury Malkov (ECCV (12), 2018)

Non-metric Similarity Graphs for Maximum Inner Product Search. - Stanislav Morozov; Artem Babenko (NeurIPS, 2018)

Impostor Networks for Fast Fine-Grained Recognition. - Vadim Lebedev; Artem Babenko; Victor S. Lempitsky (CoRR, 2018)

Unsupervised Neural Quantization for Compressed-Domain Similarity Search. - Stanislav Morozov; Artem Babenko (ICCV, 2019)

Learning to Route in Similarity Graphs. - Dmitry Baranchuk; Dmitry Persiyanov; Anton Sinitsin; Artem Babenko (ICML, 2019)

Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs. - Denis Mazur; Vage Egiazarian; Stanislav Morozov; Artem Babenko (NeurIPS, 2019)

Relevance Proximity Graphs for Fast Relevance Retrieval. - Stanislav Morozov; Artem Babenko (CoRR, 2019)

Beyond Vector Spaces: Compact Data Representationas Differentiable Weighted Graphs. - Denis Mazur; Vage Egiazarian; Stanislav Morozov; Artem Babenko (CoRR, 2019)

Towards Similarity Graphs Constructed by Deep Reinforcement Learning. - Dmitry Baranchuk; Artem Babenko (CoRR, 2019)

RPGAN: GANs Interpretability via Random Routing. - Andrey Voynov; Artem Babenko (CoRR, 2019)

Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data. - Sergei Popov; Stanislav Morozov; Artem Babenko (ICLR, 2020)

Editable Neural Networks. - Anton Sinitsin; Vsevolod Plokhotnyuk; Dmitry V. Pyrkin; Sergei Popov; Artem Babenko (ICLR, 2020)

Unsupervised Discovery of Interpretable Directions in the GAN Latent Space. - Andrey Voynov; Artem Babenko (ICML, 2020)

Big GANs Are Watching You: Towards Unsupervised Object Segmentation with Off-the-Shelf Generative Models. - Andrey Voynov; Stanislav Morozov; Artem Babenko (CoRR, 2020)

Navigating the GAN Parameter Space for Semantic Image Editing. - Anton Cherepkov; Andrey Voynov; Artem Babenko (CVPR, 2021)

Neural Side-by-Side: Predicting Human Preferences for No-Reference Super-Resolution Evaluation. - Valentin Khrulkov; Artem Babenko (CVPR, 2021)

Latent Transformations via NeuralODEs for GAN-based Image Editing. - Valentin Khrulkov; Leyla Mirvakhabova; Ivan V. Oseledets; Artem Babenko (ICCV, 2021)

On Self-Supervised Image Representations for GAN Evaluation. - Stanislav Morozov; Andrey Voynov; Artem Babenko (ICLR, 2021)

Functional Space Analysis of Local GAN Convergence. - Valentin Khrulkov; Artem Babenko; Ivan V. Oseledets (ICML, 2021)

Object Segmentation Without Labels with Large-Scale Generative Models. - Andrey Voynov; Stanislav Morozov; Artem Babenko (ICML, 2021)

Revisiting Deep Learning Models for Tabular Data. - Yury Gorishniy; Ivan Rubachev; Valentin Khrulkov; Artem Babenko (NeurIPS, 2021)

Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search. - Harsha Vardhan Simhadri; George Williams; Martin Aumüller 001; Matthijs Douze; Artem Babenko; Dmitry Baranchuk; Qi Chen 009; Lucas Hosseini; Ravishankar Krishnaswamy; Gopal Srinivasa; Suhas Jayaram Subramanya; Jingdong Wang 001 (NeurIPS (Competition and Demos), 2021)

Disentangled Representations from Non-Disentangled Models. - Valentin Khrulkov; Leyla Mirvakhabova; Ivan V. Oseledets; Artem Babenko (CoRR, 2021)

Distilling the Knowledge from Normalizing Flows. - Dmitry Baranchuk; Vladimir Aliev; Artem Babenko (CoRR, 2021)

Label-Efficient Semantic Segmentation with Diffusion Models. - Dmitry Baranchuk; Andrey Voynov; Ivan Rubachev; Valentin Khrulkov; Artem Babenko (ICLR, 2022)

When, Why, and Which Pretrained GANs Are Useful? - Timofey Grigoryev; Andrey Voynov; Artem Babenko (ICLR, 2022)

On Embeddings for Numerical Features in Tabular Deep Learning. - Yury Gorishniy; Ivan Rubachev; Artem Babenko (NeurIPS, 2022)

Revisiting Pretraining Objectives for Tabular Deep Learning. - Ivan Rubachev; Artem Alekberov; Yury Gorishniy; Artem Babenko (CoRR, 2022)

Characterizing Graph Datasets for Node Classification: Beyond Homophily-Heterophily Dichotomy. - Oleg Platonov; Denis Kuznedelev; Artem Babenko; Liudmila Prokhorenkova (CoRR, 2022)

A critical look at the evaluation of GNNs under heterophily: Are we really making progress? - Oleg Platonov; Denis Kuznedelev; Michael Diskin; Artem Babenko; Liudmila Prokhorenkova (ICLR, 2023)

TabDDPM: Modelling Tabular Data with Diffusion Models. - Akim Kotelnikov; Dmitry Baranchuk; Ivan Rubachev; Artem Babenko (ICML, 2023)

Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts. - Gleb Bazhenov; Denis Kuznedelev; Andrey Malinin; Artem Babenko; Liudmila Prokhorenkova (NeurIPS, 2023)

Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond. - Oleg Platonov; Denis Kuznedelev; Artem Babenko; Liudmila Prokhorenkova (NeurIPS, 2023)

Is This Loss Informative? Faster Text-to-Image Customization by Tracking Objective Dynamics. - Anton Voronov; Mikhail Khoroshikh; Artem Babenko; Max Ryabinin (NeurIPS, 2023)

Is This Loss Informative? Speeding Up Textual Inversion with Deterministic Objective Evaluation. - Anton Voronov; Mikhail Khoroshikh; Artem Babenko; Max Ryabinin (CoRR, 2023)

Towards Real-time Text-driven Image Manipulation with Unconditional Diffusion Models. - Nikita Starodubcev; Dmitry Baranchuk; Valentin Khrulkov; Artem Babenko (CoRR, 2023)

TabR: Unlocking the Power of Retrieval-Augmented Tabular Deep Learning. - Yury Gorishniy; Ivan Rubachev; Nikolay Kartashev; Daniil Shlenskii; Akim Kotelnikov; Artem Babenko (CoRR, 2023)

Your Student is Better than Expected: Adaptive Teacher-Student Collaboration for Text-Conditional Diffusion Models. - Nikita Starodubcev; Dmitry Baranchuk; Artem Fedorov; Artem Babenko (CVPR, 2024)

TabR: Tabular Deep Learning Meets Nearest Neighbors. - Yury Gorishniy; Ivan Rubachev; Nikolay Kartashev; Daniil Shlenskii; Akim Kotelnikov; Artem Babenko (ICLR, 2024)

Extreme Compression of Large Language Models via Additive Quantization. - Vage Egiazarian; Andrei Panferov; Denis Kuznedelev; Elias Frantar; Artem Babenko; Dan Alistarh (ICML, 2024)

Invertible Consistency Distillation for Text-Guided Image Editing in Around 7 Steps. - Nikita Starodubcev; Mikhail Khoroshikh; Artem Babenko; Dmitry Baranchuk (NeurIPS, 2024)

QUASAR: QUality and Aesthetics Scoring With Advanced Representations. - Sergey Kastryulin; Denis Prokopenko; Artem Babenko; Dmitry V. Dylov (IEEE Access, 2024)

YaART: Yet Another ART Rendering Technology. - Sergey Kastryulin; Artem Konev; Alexander Shishenya; Eugene Lyapustin; Artem Khurshudov; Alexander Tselousov; Nikita Vinokurov; Denis Kuznedelev; Alexander Markovich; Grigoriy Livshits; Alexey Kirillov; Anastasiia Tabisheva; et al. (CoRR, 2024)

TabReD: A Benchmark of Tabular Machine Learning in-the-Wild. - Ivan Rubachev; Nikolay Kartashev; Yury Gorishniy; Artem Babenko (CoRR, 2024)

TabM: Advancing tabular deep learning with parameter-efficient ensembling. - Yury Gorishniy; Akim Kotelnikov; Artem Babenko (ICLR, 2025)

TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks. - Ivan Rubachev; Nikolay Kartashev; Yury Gorishniy; Artem Babenko (ICLR, 2025)

YaART: Yet Another ART Rendering Technology. - Sergey Kastryulin; Artem Konev; Alexander Shishenya; Eugene Lyapustin; Artem Khurshudov; Alexander Tselousov; Nikita Vinokurov; Denis Kuznedelev; Alexander Markovich; Grigoriy Livshits; Alexey Kirillov; Anastasiia Tabisheva; et al. (KDD (1), 2025)

Scale-wise Distillation of Diffusion Models. - Nikita Starodubcev; Denis Kuznedelev; Artem Babenko; Dmitry Baranchuk (CoRR, 2025)

On Finetuning Tabular Foundation Models. - Ivan Rubachev; Akim Kotelnikov; Nikolay Kartashev; Artem Babenko (CoRR, 2025)