Vashisht Madhavan

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 6 papers; trusts 294 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 127 citations into it; #13,571 of 35,345 by trust here, #4,466 by citations
• Computer Vision and Pattern Recognition — 97 citations into it; #8,147 of 21,575 by trust here, #3,445 by citations
• Reinforcement Learning — 68 citations into it; #1,953 of 2,242 by trust here, #1,174 by citations
• Optimization & Training — 36 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Model Architectures — 15 citations into it
• Management Science and Operations Research — 12 citations into it
• Automotive Engineering — 11 citations into it

Whom Vashisht Madhavan trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Richard S. Sutton
5. J. Urgen Schmidhuber
6. Yoshua Bengio
7. Kaiming He
8. Volodymyr Mnih
9. Ronald J. Williams
10. Ilya Sutskever

Who trusts Vashisht Madhavan overall

1. Jeff Clune
2. Shauharda Khadka
3. Steven Dalton
4. Jiale Zhi
5. Felipe Petroski Such
6. Kenneth O. Stanley
7. Adrien Ecoffet
8. Rui Wang
9. Eden Belouadah
10. Joel Lehman

Papers from Vashisht Madhavan's perspective

Ordered by how strongly the researchers Vashisht Madhavan 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling (2018) — 960 citations
• BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning (2018) — 3,030 citations
• Best Practices for Fine-Tuning Visual Classifiers to New Domains (2016) — 161 citations
• Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning (2017) — 759 citations
• Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents (2017) — 382 citations
• An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents (2018) — 55 citations