Yashesh D. Dhebar

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 177 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 100 citations into it; #26,046 of 35,345 by trust here, #17,412 by citations
• Computer Vision and Pattern Recognition — 53 citations into it; #15,991 of 21,575 by trust here, #12,172 by citations
• Model Architectures — 26 citations into it
• Neural Architecture Search & AutoML — 25 citations into it; #242 of 283 by trust here, #217 by citations
• Reinforcement Learning — 22 citations into it
• Computational Theory and Mathematics — 21 citations into it
• Optimization & Training — 20 citations into it
• Evolutionary Computation — 9 citations into it

Whom Yashesh D. Dhebar trusts overall

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

1. Kalyanmoy Deb
2. Marek Kretowski
3. Richard S. Sutton
4. Vladimir Naumovich Vapni
5. Leon Bobrowski
6. Alex Krizhevsky
7. Ankur Sinha
8. Geoffrey E. Hinton
9. Kaiming He
10. Yoshua Bengio

Who trusts Yashesh D. Dhebar overall

1. Xiangxiang Chu
2. Kalyanmoy Deb

Papers from Yashesh D. Dhebar's perspective

Ordered by how strongly the researchers Yashesh D. Dhebar 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• NSGA-Net: neural architecture search using multi-objective genetic algorithm (2018) — 544 citations
• Toward Interpretable-AI Policies Using Evolutionary Nonlinear Decision Trees for Discrete-Action Systems (2020) — 20 citations
• Interpretable Rule Discovery Through Bilevel Optimization of Split-Rules of Nonlinear Decision Trees for Classification Problems (2020) — 32 citations
• NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search (2018) — 88 citations