Speaker at the Advanced Analytics & Big Data Infoday 2019 of Daimler

Published by Michael Barz on

Michael Barz and Daniel Sonntag are invited speakers at Daimler’s Advanced Analytics & Big Data Infoday 2019. Title: Interactive and Explainable Machine Learning

Slides on Interactive and Explainable Machine LearningDownload
Categories:
Tags:

Related Posts

Computational Sustainability

Interactive Weak Supervision for Transferring Sound Libraries to Passive Acoustic Monitoring

Passive Acoustic Monitoring (PAM) enables continuous and non-invasive biodiversity monitoring, but analysing large acoustic datasets remains difficult because sound event detectors usually require temporally precise annotations. Creating such instance-level labels is expensive and requires expert Read more

Machine Learning

4D reasoning from demonstration data for VLA

Visual-Language-Action (VLA) models are typically trained through imitation learning, which teaches policies to reproduce demonstrated actions but provides limited supervision about the conditions that define task success. We propose a framework that automatically extracts executable Read more

Computational Sustainability

Machine Learning for Passive Acoustic Wildlife Monitoring: Methods for Semi-Automated Population and Species Assessment

Passive acoustic monitoring (PAM) has become a powerful tool for studying wildlife by continuously recording environmental soundscapes. However, analysing large acoustic datasets remains highly time-consuming, as recordings are often annotated manually by domain experts. In Read more