Kensho Hara

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 2 papers; trusts 484 researchers and is trusted by 345 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 47 citations into it; #2,202 of 21,575 by trust here, #5,205 by citations
• Artificial Intelligence — 35 citations into it; #4,407 of 35,345 by trust here, #7,241 by citations
• Model Architectures — 14 citations into it
• Endocrinology, Diabetes and Metabolism — 6 citations into it
• Optimization & Training — 5 citations into it

Whom Kensho Hara trusts overall

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

1. Kaiming He
2. Christoph Feichtenhofer
3. Andrew Zisserman
4. Karen Simonyan
5. Limin Wang
6. João Carreira
7. Geoffrey E. Hinton
8. Alex Krizhevsky
9. Christian Szegedy
10. X. Zhang

Who trusts Kensho Hara overall

1. Longlong Jing
2. Christoph Feichtenhofer
3. Thao Minh Le
4. Vivek Sharma
5. Yingli Tian
6. Jie Lei
7. Andrew Zisserman
8. Hanting Chen
9. Ali Diba
10. Dahun Kim

Papers from Kensho Hara's perspective

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

Their papers

• Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? (2017) — 2,232 citations
• Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition (2017) — 710 citations