Sosuke Kobayashi

Computer Vision and Pattern Recognition · 2016–2022

A researcher in the Science corpus: 5 papers; trusts 558 researchers and is trusted by 94 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 101 citations into it; #14,158 of 21,575 by trust here, #14,151 by citations
• Artificial Intelligence — 97 citations into it; #2,061 of 35,345 by trust here, #9,870 by citations
• Natural Language Processing — 49 citations into it; #542 of 5,688 by trust here, #2,333 by citations
• Computer Graphics and Computer-Aided Design — 30 citations into it
• Model Architectures — 26 citations into it
• Computational Mechanics — 23 citations into it; #6,317 of 10,001 by trust here, #5,350 by citations
• Reinforcement Learning — 23 citations into it
• Evaluation & Benchmarks — 16 citations into it

Whom Sosuke Kobayashi trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yann LeCun
4. Alex Krizhevsky
5. Yoshua Bengio
6. Ilya Sutskever
7. Kaiming He
8. Alex Graves
9. Jimmy Ba
10. Sepp Hochreiter

Who trusts Sosuke Kobayashi overall

1. Sergey I. Nikolenko
2. Ruibo Liu
3. Steven Y. Feng
4. Omid Kashefi
5. Shuyang Cao
6. Viktar Atliha
7. Samuel Louvan
8. Michał Jungiewicz
9. Vukosi Marivate
10. Irena Spasic

Papers from Sosuke Kobayashi's perspective

Ordered by how strongly the researchers Sosuke Kobayashi 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations (2018) — 682 citations
• Decomposing NeRF for Editing via Feature Field Distillation (2022) — 461 citations
• Dynamic Entity Representation with Max-pooling Improves Machine Reading (2016) — 41 citations
• SHAPE: Shifted Absolute Position Embedding for Transformers (2021) — 60 citations
• DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedback (2018) — 92 citations