Anusha Nagabandi

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 7 papers; trusts 288 researchers and is trusted by 209 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 175 citations into it; #1,452 of 35,345 by trust here, #3,362 by citations
• Reinforcement Learning — 148 citations into it; #283 of 2,242 by trust here, #365 by citations
• Control and Systems Engineering — 50 citations into it; #603 of 8,257 by trust here, #377 by citations
• Computer Vision and Pattern Recognition — 48 citations into it; #10,614 of 21,575 by trust here, #10,677 by citations
• Representation & Self-Supervised Learning — 33 citations into it
• Computational Theory and Mathematics — 24 citations into it
• Robotics & Control — 21 citations into it; #250 of 609 by trust here, #359 by citations
• Biomedical Engineering — 19 citations into it; #676 of 10,734 by trust here, #1,666 by citations

Whom Anusha Nagabandi trusts overall

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

1. Sergey Levine
2. L. Eon Bottou
3. E. Todorov
4. Chelsea Finn
5. J. Urgen Schmidhuber
6. Pieter Abbeel
7. Geoffrey E. Hinton
8. Richard S. Sutton
9. Diederik P. Kingma
10. Yoshua Bengio

Who trusts Anusha Nagabandi overall

1. Sergey Levine
2. Michael Janner
3. Pieter Abbeel
4. Chelsea Finn
5. Gabriel Dulac-Arnold
6. Kevin Lu
7. Ignasi Clavera
8. Abhishek Gupta
9. Igor Mordatch
10. David R Ha

Papers from Anusha Nagabandi's perspective

Ordered by how strongly the researchers Anusha Nagabandi 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning (2017) — 1,091 citations
• Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model (2019) — 428 citations
• Deep Dynamics Models for Learning Dexterous Manipulation (2019) — 474 citations
• Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning (2018) — 659 citations
• Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL (2018) — 215 citations
• Deep Online Learning via Meta-Learning : Continual Adaptation for Model-Based RL (2019)
• Learning to Adapt: Meta-Learning for Model-Based Control (2018) — 94 citations