Issei Sato

Artificial Intelligence · 2012–2020

A researcher in the Science corpus: 8 papers; trusts 187 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 129 citations into it; #18,925 of 35,345 by trust here, #14,181 by citations
• Computer Vision and Pattern Recognition — 62 citations into it; #14,091 of 21,575 by trust here, #9,872 by citations
• Optimization & Training — 58 citations into it; #1,279 of 2,601 by trust here, #1,488 by citations
• Safety, Robustness & Interpretability — 39 citations into it; #770 of 2,251 by trust here, #1,186 by citations
• Scaling & Learning Theory — 27 citations into it
• Statistics and Probability — 26 citations into it
• Probabilistic & Bayesian Methods — 21 citations into it
• Computational Mechanics — 13 citations into it

Whom Issei Sato trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Max Welling
6. Yann LeCun
7. Kaiming He
8. Ilya Sutskever
9. Sepp Hochreiter
10. L. Eon Bottou

Who trusts Issei Sato overall

1. Paroma Varma
2. Alexander J. Ratner
3. Balaji Lakshminarayanan
4. Christopher R'e
5. Ferenc Huszár
6. Stephen H. Bach
7. Shakir Mohamed
8. Yingzhen Li
9. Klas Leino
10. Mingzhang Yin

Papers from Issei Sato's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Generative Adversarial Nets from a Density Ratio Estimation Perspective (2016) — 106 citations
• Sequential line search for efficient visual design optimization by crowds (2017) — 92 citations
• A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Escapes From Sharp Minima Exponentially Fast (2020) — 18 citations
• Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting (2020) — 71 citations
• A Diffusion Theory For Minima Selection: Stochastic Gradient Descent Escapes Sharp Minima Exponentially Fast (2020) — 6 citations
• Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks (2018) — 377 citations
• Does Distributionally Robust Supervised Learning Give Robust Classifiers? (2016) — 253 citations
• Reducing Wrong Labels in Distant Supervision for Relation Extraction (2012) — 217 citations