activity
20212025
most citedDeep Learning Reproducibility and Explainable AI (XAI)

7 citations · 12 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025

Modeling Biological Multifunctionality with Echo State Networks

Anastasia-Maria Leventi-Peetz, Jörg-Volker Peetz, Kai Weber +1

In this work, a three-dimensional multicomponent reaction-diffusion model has been developed, combining excitable-system dynamics with diffusion processes and sharing conceptual fe…

cs.LG2022★ 7 cited

Deep Learning Reproducibility and Explainable AI (XAI)

A. -M. Leventi-Peetz, T. Östreich

The nondeterminism of Deep Learning (DL) training algorithms and its influence on the explainability of neural network (NN) models are investigated in this work with the help of im…

cs.LG2021★ 1 cited

Scope and Sense of Explainability for AI-Systems

A. -M. Leventi-Peetz, T. Östreich, W. Lennartz +1

Certain aspects of the explainability of AI systems will be critically discussed. This especially with focus on the feasibility of the task of making every AI system explainable. E…

cs.CR2021★ 3 cited

CryptoMiniSat Switches-Optimization for Solving Cryptographic Instances

A. -M. Leventi-Peetz, O. Zendel, W. Lennartz +1

Performing hundreds of test runs and a source-code analysis, we empirically identified improved parameter configurations for the CryptoMiniSat (CMS) 5 for solving cryptographic CNF…

cs.AI2021★ 1 cited

ML Supported Predictions for SAT Solvers Performance

A. -M. Leventi-Peetz, Jörg-Volker Peetz, Martina Rohde

In order to classify the indeterministic termination behavior of the open source SAT solver CryptoMiniSat in multi-threading mode while processing hard to solve boolean satisfiabil…