Anurag Ajay

Artificial Intelligence · 2016–2024

A researcher in the Science corpus: 5 papers; trusts 158 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 127 citations into it; #20,316 of 35,345 by trust here, #11,624 by citations
• Reinforcement Learning — 88 citations into it
• Computer Vision and Pattern Recognition — 67 citations into it; #18,833 of 21,575 by trust here, #10,154 by citations
• Control and Systems Engineering — 26 citations into it; #7,983 of 8,257 by trust here, #5,784 by citations
• Generative Models — 16 citations into it
• Management Science and Operations Research — 15 citations into it
• Computational Theory and Mathematics — 14 citations into it
• Evaluation & Benchmarks — 13 citations into it

Whom Anurag Ajay trusts overall

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

1. Sergey Levine
2. Diederik P. Kingma
3. Richard S. Sutton
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Alex Krizhevsky
7. Pieter Abbeel
8. Ofir Nachum
9. Jimmy Ba
10. Yann LeCun

Who trusts Anurag Ajay overall

1. Yilun Du
2. Moritz Reuss
3. Kevin Black
4. Mengjiao Yang
5. Cheng Chi
6. Chelsea Finn
7. Ofir Nachum
8. Bencheng Liao
9. Rico Jonschkowski
10. Martin Brossard

Papers from Anurag Ajay's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Backprop KF: Learning Discriminative Deterministic State Estimators (2016) — 228 citations
• OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning (2020) — 195 citations
• Reset-free guided policy search: Efficient deep reinforcement learning with stochastic initial states (2016) — 40 citations
• Is Conditional Generative Modeling all you need for Decision-Making? (2022) — 657 citations
• OpenEQA: Embodied Question Answering in the Era of Foundation Models (2024) — 348 citations