Shohei Shimizu

Artificial Intelligence · 2004–2018

A researcher in the Science corpus: 10 papers; trusts 48 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #6,385 of 35,345 by trust here, #13,353 by citations
• Causal Inference & Discovery — 39 citations into it; #45 of 364 by trust here, #42 by citations
• Statistics and Probability — 35 citations into it; #3,318 of 12,932 by trust here, #5,126 by citations
• Signal Processing — 31 citations into it; #2,425 of 7,572 by trust here, #2,911 by citations
• Probabilistic & Bayesian Methods — 25 citations into it
• Analytical Chemistry — 18 citations into it; #297 of 1,254 by trust here, #424 by citations
• Cognitive Neuroscience — 7 citations into it; #3,211 of 10,852 by trust here, #5,677 by citations
• Statistical Learning Theory — 7 citations into it

Whom Shohei Shimizu trusts overall

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

1. Aapo Hyvärinen
2. Peter Spirtes
3. Peter M. Bentler
4. Patrik O. Hoyer
5. Tom L. Burr
6. J. Gerring
7. Paul Holland
8. Clark Glymour
9. Hui Zou
10. Richard Scheines

Who trusts Shohei Shimizu overall

1. Kun Zhang
2. Dominik Janzing
3. Jan Peters
4. Bernhard Schölkopf
5. Aapo Hyvärinen
6. Joris M. Mooij
7. Peter Buhlmann
8. Biwei Huang
9. David Lopez-Paz
10. Patrik O. Hoyer

Papers from Shohei Shimizu's perspective

Ordered by how strongly the researchers Shohei Shimizu 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. GENERATIVE ADVERSARIAL NETS
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. Long Short-Term Memory
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Error asymmetry in causal and anticausal regression (2016) — 13 citations
• Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity (2008) — 102 citations
• Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity (2010) — 466 citations
• Estimation of linear non-Gaussian acyclic models for latent factors (2009) — 62 citations
• Cause-Effect Inference by Comparing Regression Errors (2018) — 93 citations
• DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model (2011) — 674 citations
• Use of non-normality in structural equation modeling: Application to direction of causation (2008) — 71 citations
• Estimation of causal effects using linear non-Gaussian causal models with hidden variables (2008) — 243 citations
• A Linear Non-Gaussian Acyclic Model for Causal Discovery (2006) — 2,034 citations
• Causal Inference Using Nonnormality (2004) — 86 citations