Magnus Rattray

Probabilistic & Bayesian Methods · 1998–2012

A researcher in the Science corpus: 7 papers; trusts 11 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Probabilistic & Bayesian Methods — 15 citations into it
• Statistics and Probability — 12 citations into it; #11,025 of 12,932 by trust here, #10,855 by citations
• Artificial Intelligence — 10 citations into it; #8,045 of 35,345 by trust here, #13,760 by citations

Whom Magnus Rattray trusts overall

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

1. Radford M. Neal
2. Masa-aki Sato
3. Neil D. Lawrence
4. Carl E. Rasmussen
5. Zoubin Ghahramani
6. Tom Minka
7. Shun-ichi Amari
8. Manfred Opper
9. Christopher K. I. Williams
10. John K. Kruschke

Who trusts Magnus Rattray overall

1. Tom M. Heskes
2. Mauricio A Álvarez
3. Neil D. Lawrence
4. Michalis K. Titsias
5. Sebastian Goldt
6. Nolan Wagener
7. Razvan Pascanu
8. Nilesh Tripuraneni
9. James Hensman
10. Shun-ichi Amari

Papers from Magnus Rattray's perspective

Ordered by how strongly the researchers Magnus Rattray trusts endorse each paper. Same corpus, a different order for every researcher.

1. Pattern Recognition and Machine Learning
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Learning Multiple Layers of Features from Tiny Images
6. Gradient-based learning applied to document recognition
7. Expectation Propagation for approximate Bayesian inference
8. Latent Dirichlet Allocation
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Long Short-Term Memory

Their papers

• Fast Variational Inference in the Conjugate Exponential Family (2012) — 138 citations
• Modelling transcriptional regulation using Gaussian Processes (2006) — 127 citations
• Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities (2008) — 127 citations
• Statistical mechanics of learning multiple orthogonal signals: asymptotic theory and fluctuation effects. (2007) — 31 citations
• Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities (2006) — 102 citations
• Efficient Sampling for Gaussian Process Inference using Control Variables (2008) — 51 citations
• Natural gradient descent for on-line learning (1998)