Ben D. Fulcher
Cognitive Neuroscience · 2013–2019
A researcher in the Science corpus: 5 papers; trusts 54 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 52 citations into it; #5,067 of 10,852 by trust here, #7,130 by citations
- Radiology, Nuclear Medicine and Imaging — 33 citations into it
- Signal Processing — 32 citations into it; #1,972 of 7,572 by trust here, #3,309 by citations
- Molecular Biology — 22 citations into it
- Artificial Intelligence — 19 citations into it; #18,383 of 35,345 by trust here, #23,077 by citations
- Economics and Econometrics — 18 citations into it; #11,221 of 23,481 by trust here, #14,505 by citations
- Computer Vision and Pattern Recognition — 8 citations into it
- Statistics and Probability — 7 citations into it
Whom Ben D. Fulcher trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Eamonn J. Keogh
- Isabelle M Guyon
- Robert Tibshirani
- Fabian Mörchen
- Aixia Guo
- David Ruppert
- Trevor J. Hastie
- Li Wei
- Tim Bollerslev
- Julien Clinton Sprott
Who trusts Ben D. Fulcher overall
- Rohit J. Kate
- Carl Henning Lubba
- Kasun Bandara
- Aurina Arnatkeviciute
- Bernardo Pérez Orozco
- Nick S. Jones
- Sarab S. Sethi
- Christoph Bergmeir
- Philip Knaute
- Rob J Hyndman
Papers from Ben D. Fulcher's perspective
Ordered by how strongly the researchers Ben D. Fulcher trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Regression Shrinkage and Selection via the Lasso
- Et al
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Long Short-Term Memory
- Going deeper with convolutions
Their papers
- Highly Comparative Feature-Based Time-Series Classification (2014) — 358 citations
- catch22: CAnonical Time-series CHaracteristics (2019) — 374 citations
- A practical guide to linking brain-wide gene expression and neuroimaging data (2018) — 745 citations
- An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI (2017) — 764 citations
- Highly comparative time-series analysis: the empirical structure of time series and their methods (2013) — 403 citations