Atsuto Maki

Computer Vision and Pattern Recognition · 2014–2017

A researcher in the Science corpus: 6 papers; trusts 647 researchers and is trusted by 267 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 171 citations into it; #2,424 of 21,575 by trust here, #3,186 by citations
• Artificial Intelligence — 115 citations into it; #5,448 of 35,345 by trust here, #4,063 by citations
• Evaluation & Benchmarks — 28 citations into it
• Aerospace Engineering — 20 citations into it
• Model Architectures — 20 citations into it
• Representation & Self-Supervised Learning — 20 citations into it; #1,581 of 3,145 by trust here, #1,052 by citations
• Reinforcement Learning — 19 citations into it; #1,529 of 2,242 by trust here, #1,519 by citations
• Media Technology — 18 citations into it

Whom Atsuto Maki trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Yann LeCun
5. Karen Simonyan
6. Kaiming He
7. Andrew Zisserman
8. Diederik P. Kingma
9. Ross B. Girshick
10. Ilya Sutskever

Who trusts Atsuto Maki overall

1. Eden Belouadah
2. Shiori Sagawa
3. Filip Radenovic
4. Yu-Xiong Wang
5. Artem Babenko
6. Giorgos Tolias
7. Sergey Levine
8. Damien Teney
9. Liang Zheng
10. Yuzhe Yang

Papers from Atsuto Maki's perspective

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

Their papers

• From generic to specific deep representations for visual recognition (2014) — 433 citations
• Factors of Transferability for a Generic ConvNet Representation (2014) — 355 citations
• A systematic study of the class imbalance problem in convolutional neural networks (2017) — 3,026 citations
• Deep predictive policy training using reinforcement learning (2017) — 138 citations
• Visual Instance Retrieval with Deep Convolutional Networks (2014) — 383 citations
• Artificial intelligence for analyzing orthopedic trauma radiographs (2017) — 421 citations