Jonathan Gordon

Artificial Intelligence · with Bronskill · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 336 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #12,563 of 35,345 by trust here, #11,921 by citations
• Computer Vision and Pattern Recognition — 89 citations into it; #16,972 of 21,575 by trust here, #12,689 by citations
• Representation & Self-Supervised Learning — 48 citations into it
• Model Architectures — 38 citations into it
• Optimization & Training — 22 citations into it
• Probabilistic & Bayesian Methods — 21 citations into it; #862 of 1,525 by trust here, #568 by citations
• Meta & Continual Learning — 19 citations into it; #255 of 1,041 by trust here, #317 by citations
• Reinforcement Learning — 14 citations into it

Whom Jonathan Gordon trusts overall

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

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

Who trusts Jonathan Gordon overall

1. John F. Bronskill
2. Luisa M. Zintgraf
3. Massimiliano Patacchiola
4. Daniela Massiceti
5. Antreas Antoniou
6. Timothy M. Hospedales
7. Jake Snell
8. Najoung Kim
9. Wei-Hong Li
10. James Requeima

Papers from Jonathan Gordon's perspective

Ordered by how strongly the researchers Jonathan Gordon 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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

• Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes (2019) — 265 citations
• TaskNorm: Rethinking Batch Normalization for Meta-Learning (2020) — 93 citations
• Probabilistic Neural Architecture Search (2019) — 79 citations
• Permutation Equivariant Models for Compositional Generalization in Language (2020) — 112 citations
• Meta-Learning Probabilistic Inference for Prediction (2018) — 289 citations