David A. Clifton

Artificial Intelligence · 2014–2022

A researcher in the Science corpus: 13 papers; trusts 674 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 257 citations into it; #17,206 of 35,345 by trust here, #7,413 by citations
• Computer Vision and Pattern Recognition — 200 citations into it
• Multimodal Learning — 113 citations into it
• Representation & Self-Supervised Learning — 47 citations into it
• Signal Processing — 39 citations into it
• Model Architectures — 35 citations into it
• Statistics and Probability — 31 citations into it
• Cardiology and Cardiovascular Medicine — 28 citations into it; #497 of 906 by trust here, #665 by citations

Whom David A. Clifton trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Kaiming He
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Yann LeCun
7. Ilya Sutskever
8. Ross B. Girshick
9. Jimmy Ba
10. Karen Simonyan

Who trusts David A. Clifton overall

1. Eric T. Nalisnick
2. Anastasios Nikolas Angelopoulos
3. Yinhe Zheng
4. Alistair E. W. Johnson
5. Akihiro Matsukawa
6. Rémi Domingues
7. Joseph D. Futoma
8. Li-Fang Cheng

Papers from David A. Clifton's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A review of novelty detection (2014) — 1,664 citations
• Multimodal Learning With Transformers: A Survey (2022) — 1,083 citations
• CLOCS: Contrastive Learning of Cardiac Signals (2020) — 317 citations
• Non-contact video-based vital sign monitoring using ambient light and auto-regressive models (2014) — 428 citations
• Multitask Gaussian Processes for Multivariate Physiological Time-Series Analysis (2015) — 167 citations
• A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data (2015) — 238 citations
• Signal-Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring (2015) — 355 citations
• Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review (2017) — 338 citations
• Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI (2022) — 554 citations
• Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI (2022) — 405 citations
• Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test (2020) — 108 citations
• Predicting in-hospital mortality and unanticipated admissions to the intensive care unit using routinely collected blood tests and vital signs: Development and validation of a multivariable model (2018) — 42 citations
• Machine Learning and Decision Support in Critical Care (2016) — 408 citations