Recent publications

June 2022

A Benchmark Dataset for the Ground Truth Evaluation of Neural Network Explanations

Leila Arras, Wojciech Samek, Ahmed Osman

Recently, the field of explainable AI (XAI) has developed methods that provide such explanations for already trained neural networks. So far XAI methods along with their heatmaps were mainly validated qualitatively via human-based assessment, or...

May 2022

Improve the Deep Learning Models in Forestry Based on Explanations and Expertise

Ximeng Cheng, Ali Doosthosseini, Julian Kunkel

This research improves deep learning models based on explanations and expertise. The way is to set the annotation matrix for each training sample. Three image classification tasks in forestry verify the method.

May 2022

Coherent Wireless Link at 300 GHz with 160 Gbit/s Enabled by a Photonic Transmitter

Simon Nellen, Sebastian Lauck, Emilien Peytavit, Pascal Szriftgiser, Martin Schell, Guillaume Ducourn, Björn Globisch

We demonstrate a wireless link at 300 GHz using a fiber-coupled PIN photodiode as the transmitter. We achieved a maximum line rate of 160 Gbit/s with 32QAM modulation. The highest spectral efficiency was achieved with 64QAM at 8 GBaud, i.e. 48...

May 2022

Low-Loss Bragg-ReflectionWaveguides for On-Chip Time-Bin Entanglement

Hannah Thiel, Moritz Kleinert, Hauke Conradi, Lennart Jehle, Robert Chapman, Stefan Frick, Gregor Weihs, Marita Wagner, Bianca Nardi, Alexander Schlager, Holger Suchomel, Martin Kamp, Sven Hofling, Christian Schneider

We fabricate low-loss AlGaAs Bragg-reflection waveguides for the creation of C-band photon pairs via parametric down-conversion. These photon pairs are used in a hybrid on-chip time-bin entanglement scheme.

May 2022

Towards Auditable AI Systems: From Principles to Practice

Christian Berghoff, Thomas Wiegand, Wojciech Samek, Markus Wenzel, Jona Böddinghaus, Vasilios Danos, Gabrielle Davelaar, Thomas Doms, Heiko Ehrich, Alexandru Forrai, Radu Grosu, Ronan Hamon, Henrik Junklewitz, Matthias Neu, Simon Romanski, Dirk Schlesinger, Jan-Eve Stavesand, Sebastian Steinbach, Arndt von Twickel, Robert Walter, Johannes Weissenböck

Auditing AI systems is a complex endeavour since multiple aspects have to be considered along the AI lifecycle that require multi-disciplinary approaches. AI audit methods and tools are in many cases subject of research and not practically...

May 2022

Overview of the Neural Network Compression and Representation (NNR) Standard

Heiner Kirchhoffer, Karsten Müller, Werner Bailer, Fabien Racape, Wojciech Samek, Shan Liu, Miska M. Hannuksela, Paul Haase, Hamed Rezazadegan-Tavakoli, Francesco Cricri, Emre Aksu, Wei Jiang, Wei Wang, Swayambhoo Jain, Shahab Hamidi-Rad

Neural Network Coding and Representation (NNR) is the first international standard for efficient compression of neural networks. The NNR standard contains quantization and an arithmetic coding scheme as core encoding and decoding technologies, as...

April 2022

ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs

Daniel Becking, Karsten Müller, Wojciech Samek, Sebastian Lapuschkin, Maximilian Dreyer

In this chapter, we develop and describe a novel quantization paradigm for DNNs: Our method leverages concepts of explainable AI (XAI) and concepts of information theory: Instead of assigning weight values based on their distances to the...

April 2022

xxAI - Beyond Explainable AI

Andreas Holzinger, Klaus-Robert Müller, Wojciech Samek, Randy Goebel, Ruth Fong, Taesup Moon

This book takes next steps towards a broader vision for explainable AI in moving beyond explaining classifiers, to include explaining other kinds of models (e.g., unsupervised and reinforcement learning models) via a diverse array of XAI...

April 2022

Explaining the Predictions of Unsupervised Learning Models

Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek, Jacob R. Kauffmann

In this chapter, we review our recently proposed "neuralization-propagation" (NEON) approach for bringing XAI to workhorses of unsupervised learning. NEON first converts the unsupervised model into a functionally equivalent neural network so...

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