James Requeima

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 68 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #13,069 of 35,345 by trust here, #11,327 by citations
• Computer Vision and Pattern Recognition — 40 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Optimization & Training — 21 citations into it
• Probabilistic & Bayesian Methods — 15 citations into it
• Computational Theory and Mathematics — 13 citations into it
• Meta & Continual Learning — 12 citations into it; #617 of 1,041 by trust here, #649 by citations
• Reinforcement Learning — 11 citations into it

Whom James Requeima trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. J. Urgen Schmidhuber
4. Yoshua Bengio
5. Diederik P. Kingma
6. Sergey Ioffe
7. Yann LeCun
8. Ruslan Salakhutdinov
9. Oriol Vinyals
10. Kaiming He

Who trusts James Requeima overall

1. John F. Bronskill
2. Wei-Hong Li
3. Quang Pham
4. Daniela Massiceti
5. Eleni Triantafillou
6. Xialei Liu
7. Noel Loo
8. Timothy M. Hospedales
9. Lida Theodorou
10. Cuong V Nguyen

Papers from James Requeima's perspective

Ordered by how strongly the researchers James Requeima 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes (2019) — 265 citations
• TaskNorm: Rethinking Batch Normalization for Meta-Learning (2020) — 93 citations
• Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space (2017) — 206 citations