Masataro Asai

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 147 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #16,599 of 35,345 by trust here, #27,551 by citations
• Computer Vision and Pattern Recognition — 25 citations into it; #15,557 of 21,575 by trust here, #18,782 by citations
• Generative Models — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Optimization & Training — 7 citations into it
• Model Architectures — 6 citations into it
• Reinforcement Learning — 6 citations into it; #1,711 of 2,242 by trust here, #2,063 by citations
• Natural Language Processing — 5 citations into it

Whom Masataro Asai trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Chris J. Maddison
4. Jimmy Ba
5. Yoshua Bengio
6. Alex Krizhevsky
7. Qiang Yang
8. Yann LeCun
9. Sergey Ioffe
10. Alex Graves

Who trusts Masataro Asai overall

1. Blai Bonet
2. Hector Geffner
3. Tom Silver
4. Toki Migimatsu
5. Mohit Shridhar

Papers from Masataro Asai's perspective

Ordered by how strongly the researchers Masataro Asai 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary (2017) — 191 citations
• Unsupervised Grounding of Plannable First-Order Logic Representation from Images (2019) — 61 citations