Bisakha Ray

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 3 papers; trusts 4 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #24,523 of 35,345 by trust here, #15,236 by citations
• Optimization & Training — 27 citations into it
• Computational Theory and Mathematics — 17 citations into it
• Neural Architecture Search & AutoML — 13 citations into it
• Statistics and Probability — 12 citations into it
• Statistical Learning Theory — 11 citations into it

Whom Bisakha Ray trusts overall

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

1. Frank Hutter
2. Jerome H. Friedman
3. James Bergstra

Who trusts Bisakha Ray overall

1. Marc-André Zöller

Papers from Bisakha Ray's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Scikit-learn: Machine Learning in Python
3. Random Forests
4. Random Search for Hyper-Parameter Optimization
5. Algorithms for Hyper-Parameter Optimization
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Gradient-based learning applied to document recognition
8. Neural Architecture Search with Reinforcement Learning
9. Representation Learning: A Review and New Perspectives
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Analysis of the AutoML Challenge Series 2015-2018 (2019) — 189 citations
• A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention (2016) — 84 citations
• Design of the 2015 ChaLearn AutoML challenge (2015) — 135 citations