Eiichi Matsumoto

Computer Vision and Pattern Recognition · 2016–2022

A researcher in the Science corpus: 3 papers; trusts 819 researchers and is trusted by 83 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 131 citations into it; #3,512 of 21,575 by trust here, #5,053 by citations
• Artificial Intelligence — 61 citations into it; #27,826 of 35,345 by trust here, #17,412 by citations
• Computer Graphics and Computer-Aided Design — 32 citations into it
• Representation & Self-Supervised Learning — 29 citations into it; #2,638 of 3,145 by trust here, #2,094 by citations
• Computational Mechanics — 25 citations into it; #6,318 of 10,001 by trust here, #5,350 by citations
• Generative Models — 25 citations into it; #686 of 1,956 by trust here, #843 by citations
• Model Architectures — 21 citations into it
• Evaluation & Benchmarks — 17 citations into it

Whom Eiichi Matsumoto trusts overall

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

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

Who trusts Eiichi Matsumoto overall

1. Aliaksandr Siarohin
2. Sergey Tulyakov
3. Aidan Clark
4. Songwei Ge
5. Longlong Jing
6. Masaki Saito
7. Emmanuel Kahembwe
8. Jiawei Yang
9. Zhiwen Fan
10. Zhangyang Gao

Papers from Eiichi Matsumoto's perspective

Ordered by how strongly the researchers Eiichi Matsumoto 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

• Temporal Generative Adversarial Nets with Singular Value Clipping (2016) — 509 citations
• Learning Discrete Representations via Information Maximizing Self-Augmented Training (2017) — 495 citations
• Decomposing NeRF for Editing via Feature Field Distillation (2022) — 461 citations