Thomas Briegel

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4ranked-venue papers

2as first author

0since 2021 · last 1999

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 4 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 3 papers		Probabilistic and Bayesian machine learning · 62% Trustworthy machine learning · 18% Deep learning architectures and training · 14%
Interdisciplinary, comprehensive, and emerging computing 1 paper		Medical and health informatics · 100%

Topics — the 7 heaviest of 8, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Machine learning › Trustworthy machine learning robustness	1	1999	Robust Neural Network Regression for Offline and Online Learning · NIPS 1999
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference approximate inference	1	1998	Fisher Scoring and a Mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space Models · NIPS 1998
Machine learning › Probabilistic and Bayesian machine learning › dynamical system nonlinear state-space model	1	1998	Fisher Scoring and a Mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space Models · NIPS 1998
Machine learning › Probabilistic and Bayesian machine learning missing data	1	1997	A Solution for Missing Data in Recurrent Neural Networks with an Application to Blood Glucose Prediction · NIPS 1997
Machine learning › Deep learning architectures and training recurrent neural network	1	1997	A Solution for Missing Data in Recurrent Neural Networks with an Application to Blood Glucose Prediction · NIPS 1997
Machine learning › Learning theory online learning	1	1999	Robust Neural Network Regression for Offline and Online Learning · NIPS 1999
Medical and health informatics › clinical time series analysis blood glucose prediction	1	1997	A Solution for Missing Data in Recurrent Neural Networks with an Application to Blood Glucose Prediction · NIPS 1997

Methods — techniques the papers use, named apart from their topics

recurrent neural network · 0.0robust regression · 0.0mixture of modes · 0.0fisher scoring · 0.0

Year	Publication	Venue	Position
1999	Robust Neural Network Regression for Offline and Online Learning Thomas Briegel, Volker Tresp	NIPS	1
1999	Neural-network models for the blood glucose metabolism of a diabetic abstract We study the application of neural networks to modeling the blood glucose metabolism of a diabetic. In particular we consider recurrent neural networks and time series convolution neural networks which we compare to linear models and to nonlinear compartment models. We include a linear error model to take into account the uncertainty in the system and for handling missing blood glucose observations. Our results indicate that best performance can be achieved by the combination of the recurrent neural network and the linear error model. Volker Tresp, Thomas Briegel, John Moody	IEEE Trans. Neural Networks	2
1998	Fisher Scoring and a Mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space Models Thomas Briegel, Volker Tresp	NIPS	1
1997	A Solution for Missing Data in Recurrent Neural Networks with an Application to Blood Glucose Prediction Volker Tresp, Thomas Briegel	NIPS	2