114 citations · 181 across the 6 of their papers we have counts for
6 papers
A Privacy Preserving System for Movie Recommendations Using Federated Learning
David Neumann, Andreas Lutz, Karsten Müller +1
Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are utilized by many online businesses to drive enga…
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy
Christopher J. Anders, David Neumann, Wojciech Samek +2
Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction strategies can rarely be understood. With recent advances in Explainable Artificial Intelligence…
Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements
Felix Sattler, Jackie Ma, Patrick Wagner +6
Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS…
Finding and Removing Clever Hans: Using Explanation Methods to Debug and Improve Deep Models
Christopher J. Anders, Leander Weber, David Neumann +3
Contemporary learning models for computer vision are typically trained on very large (benchmark) datasets with millions of samples. These may, however, contain biases, artifacts, o…
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
Simon Wiedemann, Heiner Kirchoffer, Stefan Matlage +10
The field of video compression has developed some of the most sophisticated and efficient compression algorithms known in the literature, enabling very high compressibility for lit…
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression
Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage +9
We present DeepCABAC, a novel context-adaptive binary arithmetic coder for compressing deep neural networks. It quantizes each weight parameter by minimizing a weighted rate-distor…