Erin Grant

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 7 papers; trusts 150 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 99 citations into it; #10,430 of 35,345 by trust here, #6,720 by citations
• Representation & Self-Supervised Learning — 57 citations into it
• Computer Vision and Pattern Recognition — 47 citations into it
• Probabilistic & Bayesian Methods — 31 citations into it
• Optimization & Training — 23 citations into it; #1,491 of 2,601 by trust here, #730 by citations
• Meta & Continual Learning — 22 citations into it; #466 of 1,041 by trust here, #521 by citations
• Statistics and Probability — 17 citations into it
• Reinforcement Learning — 15 citations into it

Whom Erin Grant trusts overall

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

1. Radford M. Neal
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. J. Urgen Schmidhuber
5. Diederik P. Kingma
6. Ruslan Salakhutdinov
7. James Martens
8. Alex Krizhevsky
9. Yann LeCun
10. Oriol Vinyals

Who trusts Erin Grant overall

1. Chelsea Finn
2. Sergey Levine
3. Tianhe Yu
4. Qianru Sun
5. Yaoyao Liu
6. Oleksiy Ostapenko
7. Yinbo Chen
8. Christopher Fifty
9. Massimiliano Patacchiola
10. Alireza Fallah

Papers from Erin Grant's perspective

Ordered by how strongly the researchers Erin Grant 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

• Recasting Gradient-Based Meta-Learning as Hierarchical Bayes (2018) — 553 citations
• Doing more with less: meta-reasoning and meta-learning in humans and machines (2019) — 134 citations
• Evaluating Theory of Mind in Question Answering (2018) — 110 citations
• Online gradient-based mixtures for transfer modulation in meta-learning (2018) — 12 citations
• Modulating transfer between tasks in gradient-based meta-learning (2018) — 7 citations
• Reconciling meta-learning and continual learning with online mixtures of tasks (2018) — 113 citations
• How Can Memory-Augmented Neural Networks Pass a False-Belief Task? (2017) — 35 citations