Takahisa Imagawa

Reinforcement Learning · 2017

A researcher in the Science corpus: 1 paper; trusts 7 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 5 citations into it

Whom Takahisa Imagawa trusts overall

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

1. Hado van Hasselt
2. Levente Kocsis
3. Peter Auer
4. Richard S. Sutton
5. Csaba Szepesvari
6. Nicolò Cesa-Bianchi
7. Carlo D'Eramo
8. Andrew Barto
9. Nicholas I. Fisher
10. David Silver

Who trusts Takahisa Imagawa overall

1. Pingli Lv
2. Xuesong Wang
3. Emilie Kaufmann
4. Yuhu Cheng

Papers from Takahisa Imagawa's perspective

Ordered by how strongly the researchers Takahisa Imagawa trusts endorse each paper. Same corpus, a different order for every researcher.

1. Reinforcement Learning: An Introduction
2. ImageNet classification with deep convolutional neural networks
3. Finite-time Analysis of the Multiarmed Bandit Problem
4. Mastering the game of Go with deep neural networks and tree search
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. Gradient-based learning applied to document recognition
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. Long Short-Term Memory

Their papers

• Estimating the Maximum Expected Value through Upper Confidence Bound of Likelihood (2017) — 5 citations