The AICommunityOWL is a private, independent network of AI enthusiasts. It was founded in 2020 by employees of Fraunhofer IOSB-INA, the OWL University of Applied Sciences (TH OWL), the Centrum Industrial IT (CIIT) and Phoenix Contact. Together, we believe in digital progress through the use of machine learning. We want to create sustainable solutions for the challenges of the future: industry, mobility, smart buildings and smart cities – and above all, for people!
The Machine Learning Reading Group (MLRG) of the AICommunityOWL has the goal to get a better understanding of current trends in machine learning on a technical level. The target audience are researchers and practitioners in the field of machine learning. We read and discuss current papers with a high media impact or prominent positioning (at least orals) of the leading conferences, e.g. NeurIPS, ICML, ICLR, AISTATS, UAI, COLT, KDD, AAAI, CVPR, ACL, or IJCAI. Attendees are expected to have read (or skimmed) the papers that are going to be presented so as not to be thrown off by the notation or problem statement and to be able to participate in informed discussions related to the paper. Suggestions for future papers are encouraged, as are volunteer presenters.
We hold our next online meeting on Tuesday, May 24th, at 16:00 under this link.
Don’t miss the date and save the event to your calendar:
Next Session Title:
Diffusion Models Beat GANs on Image Synthesis
https://proceedings.neurips.cc/paper/2021/file/49ad23d1ec9fa4bd8d77d02681df5cfa-Paper.pdf
Abstract:
Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. After an introduction to DDPM, we show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this first by improving model architecture that give a substantial boost to Fréchet inception distance (FID) and then by devising a scheme for trading off diversity for fidelity.
Speakers:
Helmand Shayan (TH OWL)
For questions or suggestions of topics, feel free to contact markus.lange-hegermann@th-owl.de
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