M.Sc. David Neumann
PhD Candidate and Research Associate in Machine Learning
Working at Fraunhofer HHI, Berlin, Germany
in the Artificial Intelligence Department
Publications
A Privacy Preserving System for Movie Recommendations Using Federated Learning
November 24, 2023
David Neumann, Andreas Lutz, Karsten Müller, Wojciech Samek
Just Accepted for Publication in the ACM Transactions on Recommender Systems (TORS) Special Issue on Trustworthy Recommender Systems.
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy
February 28, 2023
Christopher J. Anders, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin
Finding and Removing Clever Hans: Using Explanation Methods to Debug and Improve Deep Models
January 2022
Christopher J. Anders, Leander Weber, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin
Published in Elsevier Information Fusion Volume 77.
Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements
October 6, 2020
Felix Sattler, Jackie Ma, Patrick Wagner, David Neumann, Markus Wenzel, Ralf Schäfer, Wojciech Samek, Klaus-Robert Müller, Thomas Wiegand
Published in Nature npj Digital Medicine, Volume 3, Number 1.
DeepCABAC: Plug&Play Compression of Neural Network Weights and Weight Updates
September 30, 2020
David Neumann, Felix Sattler, Heiner Kirchhoffer, Simon Wiedemann, Karsten Müller, Heiko Schwarz, Thomas Wiegand, Detlev Marpe, Wojciech Samek
Published in the Proceedings of th 2020 IEEE International Conference on Image Processing (ICIP).
XAI for Analyzing and Unlearning Spurious Correlations in ImageNet
July 17, 2020
Christopher J. Anders, David Neumann, Talmaj Marinč, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin
Presented at the Extending Explainable AI Beyond Deep Models and Classifiers Workshop (XXAI) at the 2020 International Conference on Machine Learning (ICML).
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
May 2020
Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage, Paul Haase, Arturo Marbán González, Talmaj Marinč, David Neumann, Tung Nguyen, Heiko Schwarz, Thomas Wiegand, Detlev Marpe, Wojciech Samek
Published in IEEE Journal of Selected Topics in Signal Processing Volume 14, Number 4.
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression
15 May 2019
Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage, Paul Haase, Arturo Marbán González, Talmaj Marinč, David Neumann, Ahmed Osman, Detlev Marpe, Heiko Schwarz, Thomas Wiegand, Wojciech Samek
Presented at the Joint Workshop on On-Device Machine Learning and Compact Deep Neural Network Representations (ODML-CDNNR) at the 2019 International Conference on Machine Learning (ICML), Received Best Paper Award.
Talks
DeepCABAC: Plug&Play Compression of Neural Network Weights and Weight Updates
Monday, October 26, 2020, 12:00 - 12:25 (Abu Dhabi, Gulf Standard Time)
2020 International Conference on Image Processing (ICIP)