Vitaly Kurin
Artificial Intelligence · 2018–2019
A researcher in the Science corpus: 4 papers; trusts 219 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 97 citations into it; #14,653 of 35,345 by trust here, #17,776 by citations
- Reinforcement Learning — 68 citations into it; #1,144 of 2,242 by trust here, #1,895 by citations
- Computer Vision and Pattern Recognition — 55 citations into it; #9,624 of 21,575 by trust here, #13,542 by citations
- Meta & Continual Learning — 20 citations into it; #638 of 1,041 by trust here, #369 by citations
- Representation & Self-Supervised Learning — 17 citations into it
- Optimization & Training — 14 citations into it; #1,785 of 2,601 by trust here, #1,134 by citations
- Model Architectures — 11 citations into it
- Management Science and Operations Research — 9 citations into it
Whom Vitaly Kurin trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Yoshua Bengio
- Chelsea Finn
- Diederik P. Kingma
- Alex Krizhevsky
- Pieter Abbeel
- Ross B. Girshick
- Sergey Levine
- Richard S. Sutton
Who trusts Vitaly Kurin overall
- Tabish Rashid
- Christian Schröder de Witt
- Annie Xie
- Georgios Papoudakis
- Chelsea Finn
- Shariq Iqbal
- Tianhe Yu
- Arrasy Rahman
- Filippos Christianos
- Shimon Whiteson
Papers from Vitaly Kurin's perspective
Ordered by how strongly the researchers Vitaly Kurin trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- CAML: Fast Context Adaptation via Meta-Learning (2018) — 38 citations
- Deep Coordination Graphs (2019) — 219 citations
- Learning From Demonstration in the Wild (2018) — 77 citations
- Fast Context Adaptation via Meta-Learning (2018) — 432 citations