Satoshi Hara
Artificial Intelligence · 2004–2020
A researcher in the Science corpus: 9 papers; trusts 72 researchers and is trusted by 25 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 58 citations into it; #7,221 of 35,345 by trust here, #13,353 by citations
- Safety, Robustness & Interpretability — 42 citations into it; #669 of 2,251 by trust here, #1,907 by citations
- Optimization & Training — 17 citations into it
- Safety Research — 14 citations into it
- Statistics and Probability — 12 citations into it; #11,792 of 12,932 by trust here, #11,487 by citations
- Information Systems — 10 citations into it
- AI Ethics, Fairness & Society — 9 citations into it
- Computational Theory and Mathematics — 9 citations into it
Whom Satoshi Hara trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Robert Tibshirani
- Niko Speybroeck
- Jerome H. Friedman
- Been Kim
- Leo Breiman
- Tianqi Chen
- Houtao Deng
- Rakesh Agrawal
- Scott Shaobing Chen
- Trevor J. Hastie
Who trusts Satoshi Hara overall
- Atoosa Kasirzadeh
- Riccardo Guidotti
- Giulia Vilone
- Nadia Burkart
- Houtao Deng
- Thibault Laugel
- Dino Pedreschi
- Ulrich Matchi Aïvodji
- Dang Lien Minh
- Anna Monreale
Papers from Satoshi Hara's perspective
Ordered by how strongly the researchers Satoshi Hara trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Enumerate Lasso Solutions for Feature Selection (2017) — 41 citations
- Finding Alternate Features in Lasso (2016) — 5 citations
- Interpretable Companions for Black-Box Models (2020) — 10 citations
- Fairwashing: the risk of rationalization (2019) — 184 citations
- Pretending Fair Decisions via Stealthily Biased Sampling (2019) — 3 citations
- Approximate and Exact Enumeration of Rule Models (2018) — 15 citations
- Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach (2016) — 100 citations
- Making Tree Ensembles Interpretable (2016) — 73 citations
- Detection of Sub-TeV Gamma Rays from the Galactic Center Direction by CANGAROO-II (2004) — 157 citations