Kate Rakelly

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 28 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 30 citations into it; #14,091 of 35,345 by trust here, #11,624 by citations
• Reinforcement Learning — 24 citations into it; #1,115 of 2,242 by trust here, #1,084 by citations
• Representation & Self-Supervised Learning — 8 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it; #13,181 of 21,575 by trust here, #11,721 by citations
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Kate Rakelly trusts overall

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

1. Diederik P. Kingma
2. J. Urgen Schmidhuber
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Chelsea Finn
6. Pieter Abbeel
7. Sergey Levine
8. Alex Krizhevsky
9. Richard S. Sutton
10. Ilya Sutskever

Who trusts Kate Rakelly overall

1. Mennatullah Siam
2. Ron Dorfman
3. Chi Zhang
4. Annie Xie
5. Chelsea Finn
6. Sae-Young Chung
7. Luckeciano Carvalho Melo
8. Jian Peng
9. Sergey Levine
10. Khoi Duc Minh Nguyen

Papers from Kate Rakelly's perspective

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

Their papers

• Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables (2019) — 832 citations
• Conditional Networks for Few-Shot Semantic Segmentation (2018) — 241 citations