Sebastian Farquhar

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 8 papers; trusts 338 researchers and is trusted by 169 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 170 citations into it; #1,949 of 35,345 by trust here, #4,170 by citations
• Computer Vision and Pattern Recognition — 81 citations into it; #2,794 of 21,575 by trust here, #4,807 by citations
• Safety, Robustness & Interpretability — 48 citations into it
• Probabilistic & Bayesian Methods — 46 citations into it; #509 of 1,525 by trust here, #878 by citations
• Natural Language Processing — 44 citations into it
• Evaluation & Benchmarks — 28 citations into it; #242 of 3,147 by trust here, #970 by citations
• Representation & Self-Supervised Learning — 21 citations into it
• Optimization & Training — 20 citations into it

Whom Sebastian Farquhar trusts overall

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

1. Alex Graves
2. Radford M. Neal
3. Yarin Gal
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Geoffrey E. Hinton
7. David John Cameron MacKay
8. Yoshua Bengio
9. Yann LeCun
10. Max Welling

Who trusts Sebastian Farquhar overall

1. Rahaf Aljundi
2. Gido M. van de Ven
3. Martin Mundt
4. Rahul Ramesh
5. Lucas Caccia
6. Matteo Boschini
7. Massimo Caccia
8. Timothée Lesort
9. Gobinda Saha
10. Christian Henning

Papers from Sebastian Farquhar's perspective

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

Their papers

• Towards Robust Evaluations of Continual Learning (2018) — 329 citations
• The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation (2018) — 923 citations
• Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation (2023) — 830 citations
• Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning (2019) — 72 citations
• Detecting hallucinations in large language models using semantic entropy (2024) — 1,470 citations
• A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks (2019) — 125 citations
• A Unifying Bayesian View of Continual Learning (2019) — 80 citations
• Pricing Externalities to Balance Public Risks and Benefits of Research (2017) — 10 citations