Abstract Machine Translation (MT) is a current topic in the Computational Linguistics (CL) community. Training an MT model on a domain and using it on another domain does not yield the expected performance due to the syntactic and...

Colloquium by Stefano Panzeri (ZMNH, Department of Excellence for Neural Information Processing, UKE)

Thursday, 25 November 2021 16:00, Zoom Meeting

Abstract: Aggregate measures of neural activity, such as the Electroencephalograms...

Abstract Telepresence refers to a set of technologies, which enables humans to visit a remote environment (RE) and interact with people there without physical travel. Ideally, users would feel a sense of presence in the RE during the...

Colloquium by Prof. Dr. Björn Schuller

Abstract: Computer Audition (CA) is all about making machines listen – and understanding what they “hear”. The last decade or less has seen the field mature with “boost on”. This comes, as like...

Ring Lecture: Introduction to Cognitive Neuroscience

Abstract: The lecture, organized by Prof. Brigitte Röder, gives an overview of essential topics in the Cognitive Neurosciences. All CML students in Psychology, Medicine, and...

Colloquium by Dr. M. Jorge Cardoso, King's College Londong

Abstract: The London AI Centre partner hospitals provide care to 1/4th of the UK population. King’s College London, jointly with Imperial College and Queen Mary University of...

Prof. Dr. Stefano Panzeri has joined Universität Hamburg as a nucleus professor. With its calls for applications for these professorships, the University aims to attract leading researchers and to advance the development of emerging fields...

Colloquium by Prof. Dr. Matthias Schulte and Prof. Dr.-Ing. Timo Gerkmann

Friday, 16 July 10:00, Zoom Meeting

Inaugural Lecture Colloquium

CML principal investigator Timo Gerkmann will give a talk on occasion of the inaugural...

Colloquium by Prof. Ullrich Köthe, Visual Learning Lab, University of Heidelberg

Thursday, 8 July 2021 14:00, Zoom Meeting

Abstract: Invertible neural networks (INNs) are designed to run efficiently in both the forward (from inputs...