Hirotaka Hachiya

Artificial Intelligence · 2009–2012

A researcher in the Science corpus: 6 papers; trusts 42 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #32,239 of 35,345 by trust here, #20,377 by citations
• Reinforcement Learning — 36 citations into it; #1,587 of 2,242 by trust here, #1,519 by citations
• Statistics and Probability — 16 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it; #1,383 of 1,525 by trust here, #991 by citations
• Representation & Self-Supervised Learning — 12 citations into it
• Control and Systems Engineering — 10 citations into it; #8,077 of 8,257 by trust here, #6,764 by citations
• Computer Vision and Pattern Recognition — 8 citations into it; #15,421 of 21,575 by trust here, #18,266 by citations
• Computational Theory and Mathematics — 6 citations into it; #11,120 of 12,182 by trust here, #5,465 by citations

Whom Hirotaka Hachiya trusts overall

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

1. Richard S. Sutton
2. Masashi Sugiyama
3. Lotfi A. Zadeh
4. Sham M. Kakade
5. Andrew Barto
6. Jan Peters
7. Doina Precup
8. Ronald J. Williams
9. Dimitri P. Bertsekas
10. Satinder Singh

Who trusts Hirotaka Hachiya overall

1. A. Mahmood
2. Diane Joyce Cook
3. Masashi Sugiyama
4. Wouter M. Kouw
5. Matthew R. Fellows
6. H. Hua
7. Xue Bin Peng
8. Yash Chandak
9. Sergey Levine
10. Travis Mandel

Papers from Hirotaka Hachiya's perspective

Ordered by how strongly the researchers Hirotaka Hachiya 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Reinforcement Learning: An Introduction
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Parametric Return Density Estimation for Reinforcement Learning (2010) — 120 citations
• Efficient Sample Reuse in EM-Based Policy Search (2009) — 19 citations
• Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition (2012) — 80 citations
• Nonparametric Return Distribution Approximation for Reinforcement Learning (2010) — 125 citations
• Conditional Density Estimation via Least-Squares Density Ratio Estimation (2010) — 72 citations
• Adaptive importance sampling for value function approximation in off-policy reinforcement learning (2009) — 51 citations