Michael Biehl
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View article: Explainable machine learning for movement disorders - Classification of tremor and myoclonus
Explainable machine learning for movement disorders - Classification of tremor and myoclonus Open
The excellent classification results provide a proof of concept for the discrimination of ET and CM by applying a GMLVQ machine learning analysis to power spectra derived from accelerometry recordings. Such a system could support clinician…
View article: IRMA: Machine learning-based harmonization of $$^{18}$$F-FDG PET brain scans in multi-center studies
IRMA: Machine learning-based harmonization of $$^{18}$$F-FDG PET brain scans in multi-center studies Open
Purpose Center-specific effects in PET brain scans arise due to differences in technical and procedural aspects. This restricts the merging of data between centers and introduces source-specific bias. Methods We demonstrate the use of the …
View article: IRMA: Machine learning-based harmonization of 18F-FDG PET brain scans in multi-center studies
IRMA: Machine learning-based harmonization of 18F-FDG PET brain scans in multi-center studies Open
Purpose: Center-specific effects in PET brain scans arise due to differences in technical and procedural aspects. This restricts the merging of data between centers and introduces source-specific bias. Methods: We demonstrate the use of th…
View article: Phase transition analysis for shallow neural networks with arbitrary activation functions
Phase transition analysis for shallow neural networks with arbitrary activation functions Open
In this work we extend the statistical physics based analysis of so-called soft committee machines (SCM), two-layered feedforward neural networks with differentiable hidden unit activation, in student–teacher model scenarios. In particular…
View article: The Role of the Learning Rate in Layered Neural Networks with ReLU Activation Function
The Role of the Learning Rate in Layered Neural Networks with ReLU Activation Function Open
Using the statistical physics framework, we study the online learning dynamics in a particular case of shallow feed-forward neural networks with ReLU activation. By expanding the activation function in terms of Hermite polynomials we deriv…
View article: Interpretable machine learning for the diagnosis of hyperkinetic movement disorders
Interpretable machine learning for the diagnosis of hyperkinetic movement disorders Open
We present a machine learning approach to the challenging differentiation of hyperkinetic movement disorders, based on accelerometric sensor data. We address the diagnosis of essential tremor and cortical myoclonus as a specific example. G…
View article: Mitigating the Bias in Data for Fairness Using an Advanced Generalized Learning Vector Quantization Approach -- FA(IR)$^2$MA-GLVQ
Mitigating the Bias in Data for Fairness Using an Advanced Generalized Learning Vector Quantization Approach -- FA(IR)$^2$MA-GLVQ Open
We propose a bias detection and mitigating scheme for data in the context of classification tasks based on learning vector quantizers (LVQ) as classifier. For this purpose generalized LVQ endowed with an advanced matrix adaptation scheme i…
View article: Aligning Generalisation Between Humans and Machines
Aligning Generalisation Between Humans and Machines Open
Recent advances in AI -- including generative approaches -- have resulted in technology that can support humans in scientific discovery and forming decisions, but may also disrupt democracies and target individuals. The responsible use of …
View article: Forecasting relative returns for S&P 500 stocks using machine learning
Forecasting relative returns for S&P 500 stocks using machine learning Open
Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fluctuate. The random walk hypothesis and efficient market hypothesis essentially state that it is not possible to systematicall…
View article: Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces
Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces Open
We introduce and investigate the iterated application of Generalized Matrix Learning Vector Quantization for the analysis of feature relevances in classification problems, as well as for the construction of class-discriminative subspaces. …
View article: Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces
Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces Open
We introduce and investigate the iterated application of Generalized Matrix Learning Vector Quantizaton for the analysis of feature relevances in classification problems, as well as for the construction of class-discriminative subspaces. T…
View article: Interpreting Hybrid AI through Autodecoded Latent Space Entities
Interpreting Hybrid AI through Autodecoded Latent Space Entities Open
Explainable AI models and methods have seen a rise in interest in recent years as a reaction to the widespread use of neural networks and similar black-box models in machine learning. In this project, we combine explainable, prototype-base…
View article: On-line Learning Dynamics in Layered Neural Networks with Arbitrary Activation Functions
On-line Learning Dynamics in Layered Neural Networks with Arbitrary Activation Functions Open
We revisit and extend the statistical physics based analysis oflayered neural networks trained by online gradient descent. We focus onthe influence of the hidden unit activation functions on the typical learningbehavior in model scenarios.…
View article: Translating the potential of the urine steroid metabolome to stage NAFLD (TrUSt-NAFLD): study protocol for a multicentre, prospective validation study
Translating the potential of the urine steroid metabolome to stage NAFLD (TrUSt-NAFLD): study protocol for a multicentre, prospective validation study Open
Introduction Non-alcoholic fatty liver disease (NAFLD) affects approximately one in four individuals and its prevalence continues to rise. The advanced stages of NAFLD with significant liver fibrosis are associated with adverse morbidity a…
View article: OR20-01 Machine Learning-based Steroid Metabolome Analysis In Women With Polycystic Ovary Syndrome Reveals Three Distinct Androgen Excess Subtypes With Different Metabolic Risk Profiles.
OR20-01 Machine Learning-based Steroid Metabolome Analysis In Women With Polycystic Ovary Syndrome Reveals Three Distinct Androgen Excess Subtypes With Different Metabolic Risk Profiles. Open
Disclosure: T.P. Rocha: None. E. Melson: None. R.J. Veen: None. L. Abdi: None. T. McDonnell: None. V. Tandl: None. J. Hawley: None. L. Wittemans: None. A. Anthony: None. L. Gilligan: None. F. Shaheen: None. P. Kempegowda: None. C.D. Gillet…
View article: Survey of feature selection and extraction techniques for stock market prediction
Survey of feature selection and extraction techniques for stock market prediction Open
In stock market forecasting, the identification of critical features that affect the performance of machine learning (ML) models is crucial to achieve accurate stock price predictions. Several review papers in the literature have focused o…
View article: Improved Interpretation of Feature Relevances: Iterated Relevance Matrix Analysis (IRMA)
Improved Interpretation of Feature Relevances: Iterated Relevance Matrix Analysis (IRMA) Open
We introduce and investigate the iterated application of Generalized Matrix Relevance Learning for the analysis of feature relevances in classification problems.The suggested Iterated Relevance Matrix Analysis (IRMA), identifies a linear s…
View article: Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis
Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis Open
Understanding the influence of activation functions on the learning behaviour of neural networks is of great practical interest.The GELU, being similar to swish and ReLU, is analysed for soft committee machines in the statistical physics f…
View article: The Shallow and the Deep: A biased introduction to neural networks and old school machine learning
The Shallow and the Deep: A biased introduction to neural networks and old school machine learning Open
The Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general. However, it was clear from the beginning that these notes would not be able to cover this …