Yuya Yoshikawa

Artificial Intelligence · 2014–2017

A researcher in the Science corpus: 3 papers; trusts 155 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it; #22,038 of 35,345 by trust here, #20,836 by citations
• Computer Vision and Pattern Recognition — 20 citations into it; #13,676 of 21,575 by trust here, #14,478 by citations
• Natural Language Processing — 12 citations into it; #3,866 of 5,688 by trust here, #5,296 by citations
• Evaluation & Benchmarks — 9 citations into it; #2,467 of 3,147 by trust here, #2,482 by citations
• Probabilistic & Bayesian Methods — 9 citations into it
• Multimodal Learning — 7 citations into it; #776 of 1,211 by trust here, #984 by citations
• Optimization & Training — 7 citations into it
• Representation & Self-Supervised Learning — 6 citations into it; #2,270 of 3,145 by trust here, #3,009 by citations

Whom Yuya Yoshikawa trusts overall

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

1. Neil D. Lawrence
2. Bernhard Schölkopf
3. Alex Smola
4. Robert Tibshirani
5. Geoffrey E. Hinton
6. Arthur Gretton
7. Dong C. Liu
8. David M. Blei
9. Ilya Sutskever
10. Yoshua Bengio

Who trusts Yuya Yoshikawa overall

1. Danica J. Sutherland
2. Krikamol Muandet
3. Aditya Mogadala
4. Haoyang Huang
5. Jason Lee
6. Danna Gurari
7. Yu Nishiyama
8. Siqi Sun
9. Mingyang Zhou
10. Aashi Jain

Papers from Yuya Yoshikawa's perspective

Ordered by how strongly the researchers Yuya Yoshikawa 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset (2017) — 129 citations
• Non-Linear Regression for Bag-of-Words Data via Gaussian Process Latent Variable Set Model (2015) — 12 citations
• Latent Support Measure Machines for Bag-of-Words Data Classification (2014) — 32 citations