Stephen S. Intille
Computer Vision and Pattern Recognition · 2002–2013
A researcher in the Science corpus: 9 papers; trusts 16 researchers and is trusted by 134 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 25 citations into it; #2,915 of 21,575 by trust here, #2,902 by citations
- Artificial Intelligence — 9 citations into it
- Biomedical Engineering — 9 citations into it; #147 of 10,734 by trust here, #1,830 by citations
- Electrical and Electronic Engineering — 8 citations into it; #2,914 of 16,539 by trust here, #6,767 by citations
- Human-Computer Interaction — 6 citations into it; #1,243 of 2,253 by trust here, #1,813 by citations
- Information Systems and Management — 6 citations into it; #2,383 of 3,842 by trust here, #3,415 by citations
- Physiology — 5 citations into it; #12 of 2,276 by trust here, #479 by citations
- Signal Processing — 5 citations into it; #2,188 of 7,572 by trust here, #2,052 by citations
Whom Stephen S. Intille trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ling Bao
- Eric Horvitz
- Arthur A. Stone
- Nicky Kern
- Chih-Jen Lin
- Jani Mäntyjärvi
- J. Ross Quinlan
- Aiko M. Hormann
- Peter Clark
- Claudio S. Pinhanez
Who trusts Stephen S. Intille overall
- Diane Joyce Cook
- Narayanan Chatapuram Krishnan
- Kerem Altun
- Andrea Mannini
- Attila Reiss
- Parisa Rashidi
- Stephen J. Preece
- Yolande A. A. Strengers
- Giovanni Acampora
- Liming Luke Chen
Papers from Stephen S. Intille's perspective
Ordered by how strongly the researchers Stephen S. Intille trusts endorse each paper. Same corpus, a different order for every researcher.
- The Nature of Statistical Learning Theory
- LIBSVM: A library for support vector machines
- Activity Recognition from User-Annotated Acceleration Data
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Multi-sensor Activity Context Detection for Wearable Computing (EUSAI)
- C4.5: Programs for Machine Learning
- Notification, Disruption, and Memory: Effects of Messaging Interruptions on Memory and Performance (IFIP TC13 International Conference on Human-Computer Interaction)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
Their papers
- Activity Recognition from User-Annotated Acceleration Data (2004) — 3,515 citations
- Activity Recognition in the Home Using Simple and Ubiquitous Sensors (2004) — 1,467 citations
- Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor (2007) — 560 citations
- Activity recognition using a single accelerometer placed at the wrist or ankle (2013) — 391 citations
- Health and the mobile phone. (2008) — 382 citations
- A Long-Term Evaluation of Sensing Modalities for Activity Recognition (2007) — 441 citations
- Using a Live-In Laboratory for Ubiquitous Computing Research (2006) — 391 citations
- Using context-aware computing to reduce the perceived burden of interruptions from mobile devices (2005) — 421 citations
- Designing a Home of the Future (2002) — 459 citations