John F. Bronskill

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 6 papers; trusts 459 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 169 citations into it; #12,780 of 35,345 by trust here, #13,760 by citations
• Computer Vision and Pattern Recognition — 162 citations into it
• Representation & Self-Supervised Learning — 75 citations into it
• Evaluation & Benchmarks — 36 citations into it
• Meta & Continual Learning — 33 citations into it; #254 of 1,041 by trust here, #286 by citations
• Optimization & Training — 32 citations into it
• Model Architectures — 22 citations into it
• Probabilistic & Bayesian Methods — 20 citations into it; #973 of 1,525 by trust here, #739 by citations

Whom John F. Bronskill trusts overall

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

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

Who trusts John F. Bronskill overall

1. Luisa M. Zintgraf
2. Quang Pham
3. Massimiliano Patacchiola
4. Aliaksandra Shysheya
5. Daniela Massiceti
6. Eleni Triantafillou
7. Wei-Hong Li
8. Aristeidis Panos
9. Richard E. Turner
10. Antreas Antoniou

Papers from John F. Bronskill's perspective

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

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
• ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition (2021) — 55 citations
• FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification (2022) — 37 citations
• Memory Efficient Meta-Learning with Large Images (2021) — 21 citations
• Meta-Learning Probabilistic Inference for Prediction (2018) — 289 citations