Steven Reece

Artificial Intelligence · 2010–2015

A researcher in the Science corpus: 6 papers; trusts 34 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #19,509 of 35,345 by trust here, #13,353 by citations
• Statistics and Probability — 17 citations into it
• Computational Theory and Mathematics — 13 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it
• Optimization & Training — 11 citations into it
• Economics and Econometrics — 7 citations into it
• Management Science and Operations Research — 6 citations into it
• Electrical and Electronic Engineering — 5 citations into it

Whom Steven Reece trusts overall

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

1. Volker Tresp
2. Radford M. Neal
3. Michael A. Osborne
4. Max Welling
5. Roman Garnett
6. Donald R. Jones
7. Nikolaus Hansen
8. James Bergstra
9. Stephen J. Roberts
10. David John Cameron MacKay

Who trusts Steven Reece overall

1. Stephen J. Roberts
2. Michael A. Osborne
3. Aaron Klein

Papers from Steven Reece's perspective

Ordered by how strongly the researchers Steven Reece 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. Learning Multiple Layers of Features from Tiny Images
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Sequential Bayesian Prediction in the Presence of Changepoints and Faults (2010) — 79 citations
• Automated Machine Learning on Big Data using Stochastic Algorithm Tuning (2014) — 25 citations
• Anomaly Detection and Removal Using Non-Stationary Gaussian Processes (2015) — 11 citations
• A Gaussian process framework for modelling stellar activity signals in radial velocity data (2015) — 219 citations
• Automated Machine Learning using Stochastic Algorithm Tuning (2014)
• Gaussian processes for time-series modelling (2013) — 623 citations