Anton Osokin
Computer Vision and Pattern Recognition · 2010–2018
A researcher in the Science corpus: 9 papers; trusts 858 researchers and is trusted by 67 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 143 citations into it; #4,105 of 21,575 by trust here, #6,542 by citations
- Artificial Intelligence — 120 citations into it; #7,112 of 35,345 by trust here, #6,103 by citations
- Optimization & Training — 73 citations into it; #1,613 of 2,601 by trust here, #1,585 by citations
- Natural Language Processing — 38 citations into it; #5,488 of 5,688 by trust here, #4,361 by citations
- Evaluation & Benchmarks — 32 citations into it
- Generative Models — 31 citations into it
- Computational Mechanics — 26 citations into it
- Model Architectures — 24 citations into it
Whom Anton Osokin trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yann LeCun
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Kaiming He
- Vladimir Kolmogorov
- Karen Simonyan
- Andrew Zisserman
Who trusts Anton Osokin overall
- Arvid Lundervold
- Liang-Chieh Chen
- George Papandreou
- Jean-Baptiste Alayrac
- Simon Lacoste-Julien
- Anvita Gupta
- Luigi Carratino
- Kirill Struminsky
- Ivan Laptev
- Faisal Mahmood
Papers from Anton Osokin's perspective
Ordered by how strongly the researchers Anton Osokin trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Fast Approximate Energy Minimization with Label Costs (2010) — 603 citations
- SEARNN: Training RNNs with Global-Local Losses (2017) — 51 citations
- Tensorizing Neural Networks (2015) — 998 citations
- Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs (2016) — 70 citations
- Context-Aware CNNs for Person Head Detection (2015) — 146 citations
- Perceptually Inspired Layout-Aware Losses for Image Segmentation (2014) — 10 citations
- GANs for Biological Image Synthesis (2017) — 114 citations
- On Structured Prediction Theory with Calibrated Convex Surrogate Losses (2017) — 67 citations
- Quantifying Learning Guarantees for Convex but Inconsistent Surrogates (2018) — 6 citations