Our work HiCMC: High-Efficiency Contact Matrix Compressor has been published in the Journal BMC Bioinformatics. The source code of HiCMC is available at github.
Our work "Self-supervised Domain Adaptation for Machinery Remaining Useful Life Prediction" has been published in the Journal Reliability Engineering & System Safety as part of the Special Issue RUL Prediction and System Reliability of Complex Systems. For more details, please visit DOI.
Our work "Indoor Scene Change Understanding (SCU): Segment, Describe, and Revert Any Change" in collaboration with EDU/Perth has been accepted at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024). Details will follow.
Our work "Mastering Zero-Shot Interactions in Cooperative and Competitive Simultaneous Games" was accepted at the International Conference on Machine Learning (ICML) 2024 and will be presented in Vienna in July.
The “Institut für Informationsverarbeitung” (Institute for Information Processing) is the home of machine learning at the Leibniz University Hannover. We focus on three main research directions, namely (I) computer vision & representation learning, (II) signal processing & -coding and (III) automated machine learning. Our methods range from deep learning, automated machine learning, reinforcement learning, image analysis, remote sensing and compression of audio, image, video as well as DNA to biomedical data. Our efforts are directed towards making efficient use of multi-modal and high dimensional data for reliable predictions, ultimately supporting end-users, developers and decision makers in a vast range of applications.
Since the foundation in 1973, the institute holds a strong tradition of cultivating connections to industry partners and jointly developing solutions to automatically process and harness information. Some of the developed methodology were successfully spun-off commercially as for instance with driver assistance modules, cochlear implants or component testing. The institute is also well known for being actively involved in the standardization of MP3, MPEG-2, AVC (H.264), HEVC (H.265) as well as MPEG-G.
Do you want to join us? We have open positions.
"In der Promotion am TNT treffen sich Forschung und Lehre auf höchstem Niveau. Durch das tolle Team und die engagierte Betreuung ergänzt sich dazu noch eine Menge Spaß und Freude an der Arbeit!"
