We are excited to announce that our work BACON: Bacterial Clone Recognition from Metagenomic Sequencing Data won the Best Presentation Award at AICPM 2023!
We are pleased to announce that we will be offering the new course Applied Machine Learning in Genomic Data Science in the upcoming winter semester. In recent years, the combined field of machine learning, genomics, and data science has witnessed a remarkable rise, transforming the landscape of biomedical research and healthcare, and revolutionizing our understanding of disease mechanisms and drug development, paving the way for precision medicine. In this new course, students will deepen their understanding of how machine learning techniques can be applied to analyze and interpret biological data, specifically in the context of genomics. The course will be available in the university-wide course directory and the faculty-wide “module catalogs” (FEI, Maschinenbau, WiWi) beginning in September.
We are happy to announce that 2 papers have been accepted to 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)! Our work HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization will be presented at the Workshop on Resource Efficient Deep Learning for Computer Vision, and our paper Personalized 3D Human Pose and Shape Refinement, a collaboration with Epic Games, will be presented at the Computer Vision for Metaverse Workshop.
Our paper Learned Fourier Bases for Deep Set Feature Extractors in Automotive Reinforcement Learning has been accepted to ITSC 2023! You can find the accepted version here. We have published our implementation on Github.
In a joint project with the LUHAI, our Reinforcement Learning Benchmark CARL was accepted to the Transactions on Machine Learning Research (TMLR). Try your RL algorithms on our benchmark with the published code on Github!
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.
