Search Results for author: Max Mühlhäuser

Found 9 papers, 6 papers with code

Unsupervised Driving Event Discovery Based on Vehicle CAN-data

no code implementations12 Jan 2023 Thomas Kreutz, Ousama Esbel, Max Mühlhäuser, Alejandro Sanchez Guinea

With our approach, we evaluate the applicability of recent time series-related contrastive and generative SSL techniques to learn representations that distinguish driving events.

Contrastive Learning Self-Supervised Learning +2

Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series

1 code implementation30 Dec 2022 Thomas Kreutz, Max Mühlhäuser, Alejandro Sanchez Guinea

In this work, we address the problem of unsupervised moving object segmentation (MOS) in 4D LiDAR data recorded from a stationary sensor, where no ground truth annotations are involved.

Clustering Semantic Segmentation +2

User-Level Label Leakage from Gradients in Federated Learning

2 code implementations19 May 2021 Aidmar Wainakh, Fabrizio Ventola, Till Müßig, Jens Keim, Carlos Garcia Cordero, Ephraim Zimmer, Tim Grube, Kristian Kersting, Max Mühlhäuser

Specifically, we investigate Label Leakage from Gradients (LLG), a novel attack to extract the labels of the users' training data from their shared gradients.

Federated Learning

Enhancing Privacy via Hierarchical Federated Learning

no code implementations23 Apr 2020 Aidmar Wainakh, Alejandro Sanchez Guinea, Tim Grube, Max Mühlhäuser

Federated learning suffers from several privacy-related issues that expose the participants to various threats.

Cryptography and Security

DeepAlign: Alignment-based Process Anomaly Correction using Recurrent Neural Networks

2 code implementations29 Nov 2019 Timo Nolle, Alexander Seeliger, Nils Thoma, Max Mühlhäuser

In this paper, we propose DeepAlign, a novel approach to multi-perspective process anomaly correction, based on recurrent neural networks and bidirectional beam search.

BINet: Multi-perspective Business Process Anomaly Classification

3 code implementations8 Feb 2019 Timo Nolle, Stefan Luettgen, Alexander Seeliger, Max Mühlhäuser

Finally, we demonstrate that a simple set of rules can be used to utilize the output of BINet for anomaly classification.

Anomaly Classification Anomaly Detection +3

Analyzing Business Process Anomalies Using Autoencoders

no code implementations3 Mar 2018 Timo Nolle, Stefan Luettgen, Alexander Seeliger, Max Mühlhäuser

In this paper, we propose a method, using autoencoders, for detecting and analyzing anomalies occurring in the execution of a business process.

Anomaly Detection

Open Source German Distant Speech Recognition: Corpus and Acoustic Model

1 code implementation International Conference on Text, Speech, and Dialogue 2015 Stephan Radeck-Arneth, Benjamin Milde, Arvid Lange, Evandro Gouvea, Stefan Radomski, Max Mühlhäuser, and Chris Biemann

We present a new freely available corpus for German distant speech recognition and report speaker-independent word error rate (WER) results for two open source speech recognizers trained on this corpus.

Distant Speech Recognition speech-recognition

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