Our paper Automl for Multi-Class Anomaly Compensation of Sensor Drift has been accepted for publication in Measurement . You can find the corresponding repository on GitHub .
Our paper QPM: Discrete Optimization for Globally Interpretable Image Classification has been accepted at the International Conference on Learning Representations 2025 (ICLR) .
Our work on Quantized Inverse Design for Photonic Integrated Circuits has been accepted for publication in the ACS Omega journal! The source code and accompanying release of our FDTD implementation is available via Github. Additionally, we will present results of comparing our implementation against other FDTD engines at the Photonics West conference in San Francisco.
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.
