Timur Garipov

Artificial Intelligence · 2016–2025

A researcher in the Science corpus: 10 papers; trusts 261 researchers and is trusted by 59 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 156 citations into it; #4,032 of 35,345 by trust here, #3,523 by citations
• Computer Vision and Pattern Recognition — 68 citations into it; #4,405 of 21,575 by trust here, #3,141 by citations
• Optimization & Training — 68 citations into it; #691 of 2,601 by trust here, #443 by citations
• Probabilistic & Bayesian Methods — 55 citations into it; #1,239 of 1,525 by trust here, #618 by citations
• Model Architectures — 37 citations into it
• Statistics and Probability — 23 citations into it
• Efficiency & Compression — 18 citations into it
• Scaling & Learning Theory — 16 citations into it; #224 of 428 by trust here, #133 by citations

Whom Timur Garipov trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Diederik P. Kingma
5. David John Cameron MacKay
6. Karen Simonyan
7. Radford M. Neal
8. Yoshua Bengio
9. Yann LeCun
10. Max Welling

Who trusts Timur Garipov overall

1. Pavel Izmailov
2. Stanislav Fort
3. Andrew Gordon Wilson
4. Mitchell Wortsman
5. Sylvestre-Alvise Rebuffi
6. Wesley J. Maddox
7. Akhilesh Deepak Gotmare
8. Arsenii Ashukha
9. Samuel K. Ainsworth
10. Hong-You Chen

Papers from Timur Garipov's perspective

Ordered by how strongly the researchers Timur Garipov 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Averaging Weights Leads to Wider Optima and Better Generalization (2018) — 2,084 citations
• Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs (2018) — 951 citations
• A Simple Baseline for Bayesian Uncertainty in Deep Learning (2019) — 1,004 citations
• Ultimate tensorization: compressing convolutional and FC layers alike (2016) — 206 citations
• Subspace Inference for Bayesian Deep Learning (2019) — 164 citations
• gpt-oss-120b&gpt-oss-20b Model Card (2025) — 1,045 citations
• OpenAI GPT-5 System Card (2025) — 667 citations
• Improving Stability in Deep Reinforcement Learning with Weight Averaging (2018)
• Fast Uncertainty Estimates and Bayesian Model Averaging of DNNs (2018)
• Bayesian Incremental Learning for Deep Neural Networks (2018) — 24 citations