1 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
Directional Curvature from Armijo Backtracking: A Low-Cost Sharpness Probe and a Calibration-Free Learning-Rate Safeguard for Adam
Ashmitha R, Jörg Frochte
Local sharpness, defined by the largest Hessian eigenvalue , sets the maximum stable gradient update size, but its computation would usually require running Lanczos or Hessian…
Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling
Sarah Grewe, Jörg Frochte
In physically dominated machining processes, experimental datasets are small, expensive, and material-specific; in this regime, data curation, evaluation design, and the form of ph…
Multiple Additive Neural Networks for Structured and Unstructured Data
Janis Mohr, Jörg Frochte
This paper extends and explains the Multiple Additive Neural Networks (MANN) methodology, an enhancement to the traditional Gradient Boosting framework, utilizing nearly shallow ne…
Exploring Student Expectations and Confidence in Learning Analytics
Hayk Asatryan, Basile Tousside, Janis Mohr +5
Learning Analytics (LA) is nowadays ubiquitous in many educational systems, providing the ability to collect and analyze student data in order to understand and optimize learning a…
Group and Exclusive Sparse Regularization-based Continual Learning of CNNs
Basile Tousside, Janis Mohr, Jörg Frochte
We present a regularization-based approach for continual learning (CL) of fixed capacity convolutional neural networks (CNN) that does not suffer from the problem of catastrophic f…