Predicting the Level of Co-Activation of One Muscle Head from the Other Muscle Head of the Biceps Brachii Muscle by Linear Regression and Shallow Feedforward Neural Networks

Grimmelsmann N, Mechtenberg M, Vieth M, Schulz A, Hammer B, Schneider A (2024)
In: Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications: 611-621.

Semantic Properties of Cosine Based Bias Scores for Word Embeddings

Schroeder S, Schulz A, Hinder F, Hammer B (2024)
In: Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. Vol. 1. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications: 160-168.

Abundance, classification and genetic potential of Thaumarchaeota in metagenomes of European agricultural soils: a meta-analysis

Nelkner J, Huang L, Lin TW, Schulz A, Osterholz B, Henke C, Blom J, Pühler A, Sczyrba A, Schlüter A (2023)
Environmental Microbiome 18(1): 26.

Generating Cardiovascular Data to Improve Training of Assistive Heart Devices

Kummert J, Schulz A, Feldhans R, Habigt M, Stemmler M, Kohler C, Abel D, Rossaint R, Hammer B (2023)
In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE: 1292-1297.

Data Augmentation for Cardiovascular Time Series Data Using WaveNet

Feldhans R, Schulz A, Kummert J, Habigt M, Stemmler M, Kohler C, Abel D, Rossaint R, Hammer B (2023)
In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE: 836-841.

Measuring Fairness with Biased Data: A Case Study on the Effects of Unsupervised Data in Fairness Evaluation

Schroeder S, Schulz A, Tarakanov I, Feldhans R, Hammer B (2023)
In: Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I. Rojas I, Joya G, Catala A (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 134-145.

One-Class Intrusion Detection with Dynamic Graphs

Liuliakov A, Schulz A, Hermes L, Hammer B (2023)
In: Artificial Neural Networks and Machine Learning – ICANN 2023. 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part IV. Iliadis L, Papaleonidas A, Angelov P, Jayne C (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 537-549.

Extending Drift Detection Methods to Identify When Exactly the Change Happened

Vieth M, Schulz A, Hammer B (2023)
In: Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I. Rojas I, Joya G, Catala A (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 92-104.

Metric Learning with Self-Adjusting Memory for Explaining Feature Drift

Kummert J, Schulz A, Hammer B (2023)
SN Computer Science 4(4): 376.

"Why Here and not There?": Diverse Contrasting Explanations of Dimensionality Reduction

Artelt A, Schulz A, Hammer B (2023)
In: Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications: 27-38.

So Can We Use Intrinsic Bias Measures or Not?

Schroeder S, Schulz A, Kenneweg P, Hammer B (2023)
In: Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications: 403-410.

Debiasing Sentence Embedders Through Contrastive Word Pairs

Kenneweg P, Schroeder S, Schulz A, Hammer B (2023)
In: Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications: 205-212.

Reservoir Memory Machines as Neural Computers

Paaßen B, Schulz A, C. Stewart T, Hammer B (2022)
IEEE Transactions on Neural Networks and Learning Systems 33(6): 2575–2585.

Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers

Kenneweg P, Schulz A, Schroeder S, Hammer B (2022)
In: Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Yin H, Camacho D, Tino P (Eds); Lecture Notes in Computer Science, 13756. Cham: Springer International Publishing: 252-261.

BERT WEAVER: Using WEight AVERaging to Enable Lifelong Learning for Transformer-based Models

Langnickel L, Schulz A, Hammer B, Fluck J (2022)
arXiv.

Reservoir Stack Machines

Paaßen B, Schulz A, Hammer B (2021)
Neurocomputing 470: 352-364.

Reservoir Memory Machines as Neural Computers

Paassen B, Schulz A, Stewart TC, Hammer B (2021)
IEEE Transactions on Neural Networks and Learning Systems: 1-11.

Efficient Reject Options for Particle Filter Object Tracking in Medical Applications

Kummert J, Schulz A, Redick T, Ayoub N, Modabber A, Abel D, Hammer B (2021)
Sensors 21(6): 2114.

DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction

Schulz A, Hinder F, Hammer B (2020)
In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}.

Reservoir memory machines

Paaßen B, Schulz A (2020)
In: Proceedings of the 28th European Symposium on Artificial Neural Networks (ESANN 2020). Verleysen M (Ed); Bruges: i6doc: 567-572.

The composition of the human ribosome varies significantly in different normal and malignant tissues

Panda A, Yadav A, Yeerna H, Singh A, Biehl M, Lux M, Schulz A, Klecha T, Doniach S, Khiabanian H, Ganesan S, et al. (2020)
In: Proceedings: AACR Annual Meeting 2020. Cancer Research, 80(16_ Supplement). Philadelphia: Amer Assoc Cancer Research.

Tissue- and development-stage-specific mRNA and heterogeneous CNV signatures of human ribosomal proteins in normal and cancer samples.

Panda A, Yadav A, Yeerna H, Singh A, Biehl M, Lux M, Schulz A, Klecha T, Doniach S, Khiabanian H, Ganesan S, et al. (2020)
Nucleic acids research.

Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning

Prahm C, Schulz A, Paaßen B, Schoisswohl J, Kaniusas E, Dorffner G, Hammer B, Aszmann O (2019)
IEEE Transactions on Neural Systems and Rehabilitation Engineering 27(5): 956-962.

Transfer Learning of Complex Motor Skills on the Humanoid Robot Affetto

Schulz A, Queißer J, Ishihara H, Asada M (2018)
Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo (In Press).

Expectation maximization transfer learning and its application for bionic hand prostheses

Paaßen B, Schulz A, Hahne J, Hammer B (2018)
Neurocomputing 298: 122-133.

Linear Supervised Transfer Learning for the Large Margin Nearest Neighbor Classifier

Berger K, Schulz A, Paaßen B, Hammer B (2018)
Presented at the SSCI CIDM 2017.

Linear Supervised Transfer Learning Toolbox

Paaßen B, Schulz A (2017)
Bielefeld University.

An EM transfer learning algorithm with applications in bionic hand prostheses

Paaßen B, Schulz A, Hahne J, Hammer B (2017)
In: Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Verleysen M (Ed); Bruges: i6doc.com: 129-134.

Discriminative dimensionality reduction: variations, applications, interpretations

Schulz A (2017)
Bielefeld: Universität Bielefeld.

Efficient Kernelization of Discriminative Dimensionality Reduction

Schulz A, Brinkrolf J, Hammer B (2017)
Neurocomputing 268(SI): 34-41.

Echo State Networks as Novel Approach for Low-Cost Myoelectric Control

Prahm C, Schulz A, Paaßen B, Aszmann O, Hammer B, Dorffner G (2017)
In: Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017). ten Telje A, Popow C, Holmes JH, Sacchi L (Eds); Lecture Notes in Computer Science, 10259. Springer: 338--342.

Discriminative Dimensionality Reduction in Kernel Space

Schulz A, Hammer B (2016)
In: ESANN2016 Proceedings. 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium,27-29 April 2016. i6doc.com.

Linear Supervised Transfer Learning for Generalized Matrix LVQ

Paaßen B, Schulz A, Hammer B (2016)
In: Proceedings of the Workshop New Challenges in Neural Computation 2016. Hammer B, Martinetz T, Villmann T (Eds); Machine Learning Reports(4). 11-18.

Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift

Prahm C, Paaßen B, Schulz A, Hammer B, Aszmann O (2016)
In: Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016). Ibáñez J, Gonzáles-Vargas J, Azorín JM, Akay M, Pons JL (Eds); Springer: 153--157.

Parametric nonlinear dimensionality reduction using kernel t-SNE

Gisbrecht A, Schulz A, Hammer B (2015)
Neurocomputing 147: 71-82.

Inferring Feature Relevances From Metric Learning

Schulz A, Mokbel B, Biehl M, Hammer B (2015)
In: 2015 IEEE Symposium Series on Computational Intelligence. Piscataway, NJ: IEEE.

Using Discriminative Dimensionality Reduction to Visualize Classifiers

Schulz A, Gisbrecht A, Hammer B (2015)
Neural Processing Letters 42(1): 27-54.

Visualization of Regression Models Using Discriminative Dimensionality Reduction

Schulz A, Hammer B (2015)
In: Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, 9257. Cham: Springer Science + Business Media: 437-449.

Unsupervised Dimensionality Reduction for Transfer Learning

Blöbaum P, Schulz A, Hammer B (2015)
In: Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco: 507-512.

Discriminative dimensionality reduction for regression problems using the Fisher metric

Schulz A, Hammer B (2015)
In: 2015 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical & Electronics Engineers (IEEE): 1-8.

Towards Dimensionality Reduction for Smart Home Sensor Data

Mokbel B, Schulz A (2015)
In: Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015). Hammer B, Martinetz T, Villmann T (Eds); Machine Learning Reports(3). 41-48.

Metric Learning in Dimensionality Reduction

Schulz A, Hammer B (2015)
In: Proceedings of the International Conference on Pattern Recognition Applications and Methods. Scitepress: 232-239.

Valid interpretation of feature relevance for linear data mappings

Frenay B, Hofmann D, Schulz A, Biehl M, Hammer B (2014)
In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). Piscataway, NJ: Institute of Electrical & Electronics Engineers (IEEE): 149-156.

Relevance learning for dimensionality reduction

Schulz A, Gisbrecht A, Hammer B (2014)
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Bruges, Belgium: i6doc.com: 165-170.

Transfer Learning without given Correspondences

Bloebaum P, Schulz A (2014)
In: Proceedings of the Workshop New Challenges in Neural Computation (NC² 2014). Hammer B, Martinetz T, Villmann T (Eds); Machine Learning Reports. 42-51.

Discriminative Dimensionality Reduction for the Visualization of Classifiers

Gisbrecht A, Schulz A, Hammer B (2014)
In: Pattern Recognition Applications and Methods. Advances in Intelligent Systems and Computing, 318. Cham: Springer Science + Business Media: 39-56.

Applications of discriminative dimensionality reduction

Hammer B, Gisbrecht A, Schulz A (2013)
In: Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods. SCITEPRESS: 33-41.

Using Nonlinear Dimensionality Reduction to Visualize Classifiers

Schulz A, Gisbrecht A, Hammer B (2013)
In: Advances in computational intelligence. Proceedings. Vol 1. Rojas I, Joya G, Gabestany J (Eds); Lecture Notes in Computer Science, 7902. Springer: 59-68.

Learning the Appropriate Model Population Structures for Locally Weighted Regression

Vukanovicz S, Schulz A, Haschke R, Ritter H (2013)
In: Workshop New Challenges in Neural Computation 2013. Machine Learning Reports, 2013(02). Bielefeld: Universität Bielefeld: 87.

Classifier inspection based on different discriminative dimensionality reductions

Schulz A, Gisbrecht A, Hammer B (2013)
In: Workshop NC^2 2013. TR Machine Learning Reports: 77-86.

How to visualize a classifier?

Schulz A, Gisbrecht A, Bunte K, Hammer B (2012)
In: Proceedings of the Workshop - New Challenges in Neural Computation 2012. Machine Learning Reports: 73-83.

How to Visualize Large Data Sets?

Hammer B, Gisbrecht A, Schulz A (2012)
Presented at the Workshop Advances in Self-Organizing Maps (WSOM), Santiago, Chile.