Volodymyr Kuleshov

Artificial Intelligence · 2014–2025

A researcher in the Science corpus: 12 papers; trusts 251 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 152 citations into it; #9,321 of 35,345 by trust here, #5,252 by citations
• Computer Vision and Pattern Recognition — 78 citations into it; #17,177 of 21,575 by trust here, #15,529 by citations
• Natural Language Processing — 35 citations into it; #4,953 of 5,688 by trust here, #5,092 by citations
• Generative Models — 32 citations into it
• Model Architectures — 27 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Reinforcement Learning — 23 citations into it; #2,205 of 2,242 by trust here, #2,160 by citations
• Probabilistic & Bayesian Methods — 22 citations into it

Whom Volodymyr Kuleshov trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yarin Gal
2. Alex Krizhevsky
3. J. Platt
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Diederik P. Kingma
7. Alexandru Niculescu-Mizil
8. Yann LeCun
9. Kaiming He
10. David John Cameron MacKay

Who trusts Volodymyr Kuleshov overall

1. Ananya Kumar
2. Dan Hendrycks
3. Kavosh Asadi
4. Percy Liang
5. Sebastian Schulze
6. Yonatan Belinkov
7. Owain Evans
8. Stephanie C. Lin
9. Heinrich Jiang
10. Myle Ott

Papers from Volodymyr Kuleshov's perspective

Ordered by how strongly the researchers Volodymyr Kuleshov trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Accurate Uncertainties for Deep Learning Using Calibrated Regression (2018) — 863 citations
• A guide to deep learning in healthcare (2019) — 4,405 citations
• Audio Super Resolution using Neural Networks (2017) — 160 citations
• Algorithms for multi-armed bandit problems (2014) — 400 citations
• Calibrated Model-Based Deep Reinforcement Learning (2019) — 62 citations
• BLOCK DIFFUSION: INTERPOLATING BETWEEN AU-TOREGRESSIVE AND DIFFUSION LANGUAGE MODELS (2025) — 334 citations
• QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks (2024) — 366 citations
• Simple and Effective Masked Diffusion Language Models (2024) — 763 citations
• QuIP: 2-Bit Quantization of Large Language Models With Guarantees (2023) — 433 citations
• Adversarial Examples for Natural Language Classification Problems (2018) — 114 citations
• Tensor Factorization via Matrix Factorization (2015) — 86 citations
• Calibrated Structured Prediction (2015) — 133 citations