Index

Retrieval Augmented Generation for Medical Question Answering

Project Seminar: Reproduce Research Results

In this seminar, students will engage in reproducing state-of-the-art scientific results with two main objectives. Firstly, students will work on projects that are close to current state-of-the-art research, and secondly, they will develop essential competencies in reproducing and critically analyzing scientific results. The projects will be tailored to match each student’s interests in terms of methodology and application, while the task requirements and grading criteria will be standardized across the board. The outcome of this project will contribute to the scientific community by providing a report on the state of reproducibility within the field.

The seminar will begin with a series of lectures. Students will initially evaluate publications from leading conferences in the field, focusing on their reproducibility, to gather comprehensive insights and understand the challenges involved. Typically, the evaluation will concentrate on publications from top-tier international conferences, such as CVPR and MICCAI. The specific conferences of focus may change each semester and will be announced at the start of the semester.

Students will have the option to choose from varying degrees of reproduction effort, ranging from attempting to reproduce a single result from a paper to fully implementing an entire paper. Depending on the complexity of the chosen task, students may analyze one or multiple publications.

Peer feedback and exchanges within small groups will form part of the seminar, although all reproduction efforts and deliverables will be individual work.

If you are interested, please join the first lecture on 16.10.2024 at 8.15 am in lecture hall H4 (Martensstraße 1, 91058 Erlangen).

Course registration opens on October 16, 2024, and will close on October 20, 2024. The StudOn link and password will be shared during the first lecture. Registration will follow a first-come, first-served basis.

Automatic speaker anonymization using diffusion models

Speech Emotion Recognition Demo

If you are interested, please send an email with your transcripts to paula.andrea.perez@fau.de with the subject SERDemo LME. The students should have knowledge of Python coding and GUI toolkits such as PyQt.

SwinU: A Swin Transformer-Based Model for CT Image Restoration

Survey on Image Segmentation and Concise Introductions to DeepMedic

TSI Challenge Summer 2024: Heat & Water Demand Forecasting

The Time Series Intelligence group from the Pattern Recognition Lab offers a 5/10 ECTs project in a challenge format. This is a “contest” where the students are expected to use different machine learning and deep learning methods for time series forecasting. The course is limited to 20 students per semester and they can decide whether to work alone or form a group with another student.