Stephen J. Preece

Biomedical Engineering · 2009–2017

A researcher in the Science corpus: 3 papers; trusts 13 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Biomedical Engineering — 27 citations into it; #5,188 of 10,734 by trust here, #5,019 by citations
• Computer Vision and Pattern Recognition — 26 citations into it; #14,174 of 21,575 by trust here, #9,435 by citations
• Physical Therapy, Sports Therapy and Rehabilitation — 16 citations into it; #284 of 390 by trust here, #147 by citations
• Physiology — 15 citations into it
• Artificial Intelligence — 10 citations into it
• Electrical and Electronic Engineering — 6 citations into it
• Signal Processing — 6 citations into it

Whom Stephen J. Preece trusts overall

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

1. Tom Chau
2. Bijan Najafi
3. Miikka Ermes
4. Ling Bao
5. Tâm Huynh
6. Juha Pärkkä
7. Kamiar Aminian
8. Stéphane Mallat
9. Jani Mäntyjärvi
10. Bernt Schiele

Who trusts Stephen J. Preece overall

1. Kerem Altun
2. Billur Barshan
3. Weijun Tao
4. D. V. D. Valk
5. Tao Liu
6. Wanmin Wu
7. Muhammad Shoaib
8. Ricardo Chavarriaga
9. Stephan Bosch
10. Rencheng Zheng

Papers from Stephen J. Preece's perspective

Ordered by how strongly the researchers Stephen J. Preece 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. Gradient-based learning applied to document recognition
4. Activity Recognition from User-Annotated Acceleration Data
5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Activity and Location Recognition Using Wearable Sensors (IEEE pervasive computing)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Activity identification using body-mounted sensors—a review of classification techniques (2009) — 617 citations
• A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data (2009) — 570 citations
• Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study (2017) — 1,139 citations