Natasha Jaques

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 12 papers; trusts 202 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 307 citations into it; #11,695 of 35,345 by trust here, #6,266 by citations
• Reinforcement Learning — 190 citations into it; #774 of 2,242 by trust here, #488 by citations
• Natural Language Processing — 121 citations into it; #1,932 of 5,688 by trust here, #1,188 by citations
• Computer Vision and Pattern Recognition — 73 citations into it; #7,302 of 21,575 by trust here, #4,001 by citations
• Electrical and Electronic Engineering — 53 citations into it
• Management Science and Operations Research — 50 citations into it
• Computational Theory and Mathematics — 37 citations into it
• Control and Systems Engineering — 37 citations into it

Whom Natasha Jaques trusts overall

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

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

Who trusts Natasha Jaques overall

1. Tomasz Korbak
2. Aviral Kumar
3. Sergey Levine
4. Scott Fujimoto
5. Rafael Rafailov
6. Ethan Perez
7. Sergey I. Nikolenko
8. Ilya Kostrikov
9. Tianhe Yu
10. Georgios Papoudakis

Papers from Natasha Jaques's perspective

Ordered by how strongly the researchers Natasha Jaques 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. 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

• Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog (2019) — 397 citations
• Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control (2016) — 222 citations
• Human-centric Dialog Training via Offline Reinforcement Learning (2020) — 120 citations
• Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems (2019) — 100 citations
• Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design (2020) — 327 citations
• Hierarchical Reinforcement Learning for Open-Domain Dialog (2019) — 61 citations
• Adversarial Environment Generation for Learning to Navigate the Web (2021) — 19 citations
• Emergent Social Learning via Multi-agent Reinforcement Learning (2020) — 69 citations
• Tackling Climate Change with Machine Learning (2019) — 1,184 citations
• Learning via social awareness: improving sketch representations with facial feedback (2018) — 13 citations
• Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning (2018) — 553 citations
• Tuning Recurrent Neural Networks with Reinforcement Learning (2016) — 113 citations