Akisato Kimura

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 56 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it
• Computer Vision and Pattern Recognition — 43 citations into it
• Probabilistic & Bayesian Methods — 18 citations into it
• Signal Processing — 18 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Efficiency & Compression — 12 citations into it
• Optimization & Training — 12 citations into it
• Safety, Robustness & Interpretability — 12 citations into it

Whom Akisato Kimura trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Jimmy Ba
4. Ian J. Goodfellow
5. Yoshua Bengio
6. Yann LeCun
7. Zoubin Ghahramani
8. Alex Krizhevsky
9. R. Caruana
10. Timothy Dozat

Who trusts Akisato Kimura overall

1. Paul Micaelli
2. Lin Wang
3. Amos J. Storkey
4. Mary Phuong
5. Iulia Turc
6. Jianping Gou
7. Tianhong Li
8. Jaemin Yoo
9. Gaurav Kumar Nayak
10. Kuk-Jin Yoon

Papers from Akisato Kimura's perspective

Ordered by how strongly the researchers Akisato Kimura 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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

• Seeing through Sounds: Predicting Visual Semantic Segmentation Results from Multichannel Audio Signals (2019) — 16 citations
• Few-shot learning of neural networks from scratch by pseudo example optimization (2018) — 55 citations
• Imitation networks: Few-shot learning of neural networks from scratch (2018) — 6 citations