Sergey Bartunov

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 7 papers; trusts 233 researchers and is trusted by 82 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 106 citations into it; #6,799 of 35,345 by trust here, #3,878 by citations
• Computer Vision and Pattern Recognition — 47 citations into it; #4,706 of 21,575 by trust here, #5,375 by citations
• Reinforcement Learning — 32 citations into it
• Optimization & Training — 23 citations into it
• Representation & Self-Supervised Learning — 20 citations into it; #627 of 3,145 by trust here, #305 by citations
• Model Architectures — 19 citations into it
• Cognitive Neuroscience — 16 citations into it
• Electrical and Electronic Engineering — 13 citations into it

Whom Sergey Bartunov trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Ruslan Salakhutdinov
5. Kaiming He
6. Alex Krizhevsky
7. Oriol Vinyals
8. Alex Graves
9. J. Urgen Schmidhuber
10. Yann LeCun

Who trusts Sergey Bartunov overall

1. Chelsea Finn
2. Sergey Levine
3. Yoshua Bengio
4. Pieter Abbeel
5. Yaqing Wang
6. Oriol Vinyals
7. Scott E. Reed
8. Marta Garnelo
9. Ruslan Salakhutdinov
10. Trevor Darrell

Papers from Sergey Bartunov's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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

• One-shot Learning with Memory-Augmented Neural Networks (2016) — 540 citations
• StarCraft II: A New Challenge for Reinforcement Learning (2017) — 971 citations
• Fast Adaptation in Generative Models with Generative Matching Networks (2016) — 24 citations
• Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures (2018) — 281 citations
• Solving Mixed Integer Programs Using Neural Networks (2020) — 322 citations
• Few-shot Generative Modelling with Generative Matching Networks (2018) — 105 citations
• Meta-Learning with Memory-Augmented Neural Networks (2016) — 2,170 citations