Jun Saito

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 6 papers; trusts 245 researchers and is trusted by 50 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 106 citations into it; #3,808 of 21,575 by trust here, #5,754 by citations
• Control and Systems Engineering — 54 citations into it; #352 of 8,257 by trust here, #656 by citations
• Artificial Intelligence — 45 citations into it
• Model Architectures — 20 citations into it
• Signal Processing — 18 citations into it
• Computational Mechanics — 8 citations into it
• Optimization & Training — 8 citations into it
• Reinforcement Learning — 8 citations into it; #1,930 of 2,242 by trust here, #1,999 by citations

Whom Jun Saito trusts overall

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

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

Who trusts Jun Saito overall

1. Dario Pavllo
2. Xue Bin Peng
3. Michael J. Black
4. Daniel Holden
5. Hung Yu Ling
6. Mathis Petrovich
7. Omid Taheri
8. Emre Aksan
9. Taku Komura
10. Nantheera Anantrasirichai

Papers from Jun Saito's perspective

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

Their papers

• Phase-functioned neural networks for character control (2017) — 668 citations
• Neural state machine for character-scene interactions (2019) — 366 citations
• A Deep Learning Framework for Character Motion Synthesis and Editing (2016) — 738 citations
• Mode-adaptive neural networks for quadruped motion control (2018) — 370 citations
• Learning motion manifolds with convolutional autoencoders (2015) — 311 citations
• FaceFormer: Speech-Driven 3D Facial Animation with Transformers (2021) — 326 citations