Masanori Suganuma

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 916 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 139 citations into it; #9,716 of 21,575 by trust here, #13,542 by citations
• Artificial Intelligence — 91 citations into it; #6,408 of 35,345 by trust here, #11,921 by citations
• Model Architectures — 35 citations into it; #551 of 2,278 by trust here, #1,145 by citations
• Multimodal Learning — 31 citations into it
• Media Technology — 22 citations into it; #2,225 of 3,621 by trust here, #2,389 by citations
• Optimization & Training — 22 citations into it
• Generative Models — 13 citations into it
• Neural Architecture Search & AutoML — 13 citations into it; #50 of 283 by trust here, #107 by citations

Whom Masanori Suganuma trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Yann LeCun
7. Ilya Sutskever
8. Ross B. Girshick
9. Jimmy Ba
10. Christian Szegedy

Who trusts Masanori Suganuma overall

1. Yanan Sun
2. Bing Xue
3. Thomas Elsken
4. Martin Wistuba
5. Mengjie Zhang
6. Zhaobai Zhong
7. Haokui Zhang
8. Ricardo H. R. Lima
9. Mohammad Mahdi Bejani
10. Bowen Baker

Papers from Masanori Suganuma's perspective

Ordered by how strongly the researchers Masanori Suganuma 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. 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

• Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search (2018) — 92 citations
• Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration (2019) — 266 citations
• A genetic programming approach to designing convolutional neural network architectures (2017) — 618 citations
• Efficient Attention Mechanism for Handling All the Interactions between Many Inputs with Application to Visual Dialog (2019) — 7 citations