Tim Conrad

Orcid: 0000-0002-5590-5726

Affiliations:
  • Zuse Institute, Berlin, Germany
  • Free University of Berlin, Germany


According to our database1, Tim Conrad authored at least 29 papers between 2005 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Federated Learning With Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification.
IEEE J. Biomed. Health Informatics, November, 2024

Combining LLMs and Knowledge Graphs to Reduce Hallucinations in Question Answering.
CoRR, 2024

Vahana.jl - A framework (not only) for large-scale agent-based models.
CoRR, 2024

2023
Enhancing Electrocardiogram (ECG) Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification.
Mach. Learn. Knowl. Extr., December, 2023

Parallel Exchange of Randomized SubGraphs for Optimization of Network Alignment: PERSONA.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023

An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023

Neural parameter calibration and uncertainty quantification for epidemic forecasting.
CoRR, 2023

Making Mathematical Research Data FAIR: A Technology Overview.
CoRR, 2023

Bravo MaRDI: A Wikibase Powered Knowledge Graph on Mathematics.
CoRR, 2023

Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification.
CoRR, 2023

Bravo MaRDI: A Wikibase Knowledge Graph on Mathematics.
Proceedings of the Wikidata Workshop 2023 co-located with 22nd International Semantic Web Conference (ISWC 2023), 2023

Predicting Coma Recovery After Cardiac Arrest With Residual Neural Networks.
Proceedings of the Computing in Cardiology, 2023

2022
Forget Embedding Layers: Representation Learning for Cold-start in Recommender Systems.
CoRR, 2022

Understanding microbiome dynamics via interpretable graph representation learning.
CoRR, 2022

2021
A Convergent Discretization Method for Transition Path Theory for Diffusion Processes.
Multiscale Model. Simul., 2021

2020
Dictionary learning for transcriptomics data reveals type-specific gene modules in a multi-class setting.
it Inf. Technol., 2020

GraphKKE: graph Kernel Koopman embedding for human microbiome analysis.
Appl. Netw. Sci., 2020

2019
Learning Chemical Reaction Networks from Trajectory Data.
SIAM J. Appl. Dyn. Syst., 2019

Convergent discretisation schemes for transition path theory for diffusion processes.
CoRR, 2019

Deep Learning for Proteomics Data for Feature Selection and Classification.
Proceedings of the Machine Learning and Knowledge Extraction, 2019

2017
Practically efficient methods for performing bit-reversed permutation in C++11 on the x86-64 architecture.
CoRR, 2017

Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data.
BMC Bioinform., 2017

2016
Epithelial-Mesenchymal Transition Regulatory Network-Based Feature Selection in Lung Cancer Prognosis Prediction.
Proceedings of the Bioinformatics and Biomedical Engineering, 2016

2015
Minimum-overlap Clusterings and the Sparsity of Overcomplete Decompositions of Binary Matrices.
Proceedings of the International Conference on Computational Science, 2015

Better Interpretable Models for Proteomics Data Analysis Using Rule-Based Mining.
Proceedings of the Towards Integrative Machine Learning and Knowledge Extraction, 2015

2013
Finding Modules in Networks with Non-modular Regions.
Proceedings of the Experimental Algorithms, 12th International Symposium, 2013

2008
New statistical algorithms for the analysis of mass spectrometry time-of- flight mass data with applications in clinical diagnostics (Neue statistische Algorithmen zur Analyse von Massenspektrometrie Time-Of- Flight Massendaten mit Anwendungen in der klinischen Diagnostik)
PhD thesis, 2008

2006
Beating the Noise: New Statistical Methods for Detecting Signals in MALDI-TOF Spectra Below Noise Level.
Proceedings of the Computational Life Sciences II, 2006

2005
New statistical algorithms for clinical proteomics.
Proceedings of the Computational Proteomics, 20.-25. November 2005, 2005


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