This inventor holds 3 USPTO granted patents and 15 published patent applications. Top assignee: Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.v.. Active years: 2026.
Company Filing History:
Years Active: 2026
Title: Simon Wiedemann: Innovator in Neural Network Compression
Introduction
Simon Wiedemann is a prominent inventor based in Berlin, Germany. He has made significant contributions to the field of neural networks, particularly in the area of parameter compression. His innovative work has the potential to enhance the efficiency of neural network models.
Latest Patents
Wiedemann holds a patent for "Methods and apparatuses for compressing parameters of neural networks." This patent describes an encoder designed to encode weight parameters of a neural network using context-dependent arithmetic coding. The encoder selects a context for encoding based on previously encoded weight parameters and syntax elements, thereby optimizing the encoding process. This invention also includes corresponding decoders, quantizers, methods, and computer programs.
Career Highlights
Simon Wiedemann is associated with the Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V., a leading research organization in Germany. His work at this institution has allowed him to explore advanced techniques in neural network technology and contribute to cutting-edge research.
Collaborations
Wiedemann has collaborated with notable colleagues, including Paul Haase and Arturo Marban Gonzalez. These partnerships have fostered a collaborative environment that encourages innovation and the sharing of ideas.
Conclusion
Simon Wiedemann's contributions to neural network parameter compression exemplify the impact of innovative thinking in technology. His work continues to influence advancements in the field, paving the way for more efficient neural network applications.