Colin White
Artificial Intelligence · 2019–2024
A researcher in the Science corpus: 8 papers; trusts 521 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 210 citations into it; #19,865 of 35,345 by trust here, #19,018 by citations
- Computer Vision and Pattern Recognition — 97 citations into it; #12,417 of 21,575 by trust here, #13,269 by citations
- Neural Architecture Search & AutoML — 66 citations into it; #188 of 283 by trust here, #217 by citations
- Evaluation & Benchmarks — 52 citations into it
- Model Architectures — 28 citations into it
- Optimization & Training — 21 citations into it
- Computational Theory and Mathematics — 15 citations into it
- Management Science and Operations Research — 14 citations into it
Whom Colin White trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Quoc V. Le
- Barret Zoph
- Ronald J. Williams
- Geoffrey E. Hinton
- Alex Krizhevsky
- Frank Hutter
- Willie Neiswanger
- Yoshua Bengio
- Karen Simonyan
- Kaiming He
Who trusts Colin White overall
- Willie Neiswanger
- Yash Savani
- Sam Nolen
- Julien N. Siems
- Shen Yan
- Chepuri Shri Krishna
- Vu Nguyen
- Han Shi
- Lucas Zimmer
- Bernd Bischl
Papers from Colin White's perspective
Ordered by how strongly the researchers Colin White trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- A Study on Encodings for Neural Architecture Search (2020) — 85 citations
- Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive (2024) — 257 citations
- LiveBench: A Challenging, Contamination-Free LLM Benchmark (2024) — 317 citations
- To the Cutoff... and Beyond? A Longitudinal Perspective on LLM Data Contamination (2024)
- When Do Neural Nets Outperform Boosted Trees on Tabular Data? (2023) — 356 citations
- Local Search is State of the Art for Neural Architecture Search Benchmarks (2020) — 13 citations
- Local Search is State of the Art for NAS Benchmarks (2020) — 30 citations
- BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search (2019) — 413 citations