Takeru Ohta

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 13 citations into it; #28,667 of 35,345 by trust here, #13,760 by citations
• Optimization & Training — 7 citations into it

Whom Takeru Ohta trusts overall

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

1. James Bergstra
2. Alex Krizhevsky
3. Frank Hutter
4. Wes McKinney

Who trusts Takeru Ohta overall

1. Ivan Rubachev
2. Artem Babenko

Papers from Takeru Ohta's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Reading Digits in Natural Images with Unsupervised Feature Learning
3. Practical Bayesian Optimization of Machine Learning Algorithms
4. Learning Multiple Layers of Features from Tiny Images
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Gradient-based learning applied to document recognition
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Algorithms for Hyper-Parameter Optimization
9. Automatic differentiation in PyTorch
10. TensorFlow: A system for large-scale machine learning

Their papers

• Optuna: A Next-generation Hyperparameter Optimization Framework (2019) — 10,668 citations