Jonathan Ben-tzur

Optimization & Training · 2018

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 16 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Neural Architecture Search & AutoML — 8 citations into it

Whom Jonathan Ben-tzur trusts overall

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

1. Alex Krizhevsky
2. Aaron Klein

Papers from Jonathan Ben-tzur's perspective

Ordered by how strongly the researchers Jonathan Ben-tzur trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• A System for Massively Parallel Hyperparameter Tuning (2018) — 521 citations