Tetsuya Ogata

Artificial Intelligence · 2005–2018

A researcher in the Science corpus: 6 papers; trusts 142 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 46 citations into it; #32,104 of 35,345 by trust here, #20,836 by citations
• Computer Vision and Pattern Recognition — 46 citations into it; #19,742 of 21,575 by trust here, #12,172 by citations
• Representation & Self-Supervised Learning — 18 citations into it
• Optimization & Training — 16 citations into it
• Natural Language Processing — 12 citations into it
• Signal Processing — 12 citations into it
• Control and Systems Engineering — 11 citations into it; #7,630 of 8,257 by trust here, #2,692 by citations
• Automotive Engineering — 6 citations into it; #2,582 of 3,081 by trust here, #2,769 by citations

Whom Tetsuya Ogata trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yann LeCun
4. Yoshua Bengio
5. Ilya Sutskever
6. Kaiming He
7. James Martens
8. Diederik P. Kingma
9. L. Eon Bottou
10. Ross B. Girshick

Who trusts Tetsuya Ogata overall

1. Dana Kulic
2. Wataru Takano
3. Yoshihiko Nakamura
4. Christian Ott
5. Felipe Codevilla

Papers from Tetsuya Ogata's perspective

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

Their papers

• Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability (2018) — 42 citations
• Audio-visual speech recognition using deep learning (2014) — 551 citations
• Repeatable Folding Task by Humanoid Robot Worker Using Deep Learning (2017) — 196 citations
• Tactile object recognition using deep learning and dropout (2014) — 98 citations
• Multimodal integration learning of robot behavior using deep neural networks (2014) — 183 citations
• Open-end human–robot interaction from the dynamical systems perspective: mutual adaptation and incremental learning (2005) — 46 citations