Seiichi Uchida

Artificial Intelligence · 2013–2020

A researcher in the Science corpus: 4 papers; trusts 170 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 58 citations into it; #18,350 of 35,345 by trust here, #17,110 by citations
• Computer Vision and Pattern Recognition — 43 citations into it; #9,340 of 21,575 by trust here, #3,538 by citations
• Signal Processing — 18 citations into it; #3,751 of 7,572 by trust here, #2,816 by citations
• Computer Networks and Communications — 13 citations into it; #6,630 of 9,886 by trust here, #4,128 by citations
• Optimization & Training — 12 citations into it; #2,015 of 2,601 by trust here, #2,428 by citations
• Statistics and Probability — 11 citations into it
• Model Architectures — 8 citations into it
• Biomedical Engineering — 7 citations into it

Whom Seiichi Uchida trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Karen Simonyan
4. Matthew D. Zeiler
5. Yoshua Bengio
6. Yann LeCun
7. Sepp Hochreiter
8. Sergey Ioffe
9. Kaiming He
10. Christian Szegedy

Who trusts Seiichi Uchida overall

1. Minghui Liao
2. Baoguang Shi
3. Azal Ahmad Khan
4. Xiaoyi Gu
5. Xiang Bai
6. Cong Yao
7. Pengyuan Lyu
8. Jia-Nan Wu
9. Tong He
10. Zheng Zhang

Papers from Seiichi Uchida's perspective

Ordered by how strongly the researchers Seiichi Uchida 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• ICDAR 2013 Robust Reading Competition (2013) — 1,473 citations
• ICDAR 2015 competition on Robust Reading (2015) — 1,578 citations
• An empirical survey of data augmentation for time series classification with neural networks (2020) — 667 citations
• A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data (2016) — 983 citations