Worum es bei uns geht

A meetup for academics, professionals and hobbyists interested in applications and latest developments in Machine Learning, and AI more broadly. We talk about:

• Computer vision, speech recognition, text mining, generative design

• New papers that we're excited about, and software that we use

• Cool applications of AI & machine learning, and how we made them

We strive to focus on the science & technology side, as opposed to the commercial side.

We typically meet the first Monday of every month.

We're always looking for interesting presentations. If you have a topic you want to talk about, anything from 10 to 45 minutes long, then please email gtrent@gmail.com. For talks we are explicitly *not* commercial. We organize this meetup because we are passionate about AI & ML, not to promote some product or service.

If an organization would like to host us, or sponsor food & drink, let us know.

Our official Twitter hashtag is #MLBerlin (https://twitter.com/search?q=%23MLBerlin).

VISIT US AT: http://machinelearning.berlin/

Bevorstehende Events (2)

ML * Privacy * 2

Benötigt einen Veranstaltungsort

The event will be online, as usual these days. We'll share a link as the date approaches. ---- Talk I: Does machine learning threaten privacy? Speaker I: Verena Battis, Fraunhofer Institute for Secure Information Technology Abstract: Methods of machine learning have become an integral part of our everyday life. In many cases, private and/or sensitive information is used to train these models. Until recently, it was assumed that it was not possible to infer from the final model the data used for training. However, recent research has shown that this assumption is a fallacy. This talk will address the privacy threats posed by machine learning techniques and the questions that arise from them – e.g. can an attacker extract private training data from a trained model? Is it possible to steal a model by simple query access? Bio: After completing her master's degree in statistics at the University of Trier, Verena has been working as a research associate at the Fraunhofer Institute for Secure Information Technology (SIT) since March 2019. Her focus is on research into the risks to the privacy of individuals through the use of modern machine learning methods - e.g. neural networks -, the fundamentals that allow for those privacy threats in the first place, and ways to mitigate them. ---- Talk II: Privacy-preserving Machine Learning Speaker II: Franziska Boenisch, Fraunhofer Institute for Applied and Integrated Security Abstract: With the growing amount of data being collected about individuals, ever more complex machine learning models can be trained based on those individuals’ characteristics and behaviors. Methods for extracting private information from the trained models become more and more sophisticated, such that individual privacy is threatened. In this talk, I will introduce some powerful methods for training neural networks with privacy guarantees. I will also show how to apply those methods effectively in order to achieve a good trade-off between utility and privacy. Bio: Franziska has completed a Master’s degree in Computer Science at Freie University Berlin and Technical University Eindhoven. For the past 1,5 years, she has been working at Fraunhofer AISEC as a Research Associate in topics related to Privacy Preserving Machine Learning, Data Protection, and Intellectual Property Protection for Neural Networks. Additionally, she is currently doing her PhD in Berlin.

ML Group Berlin - topics TBD

Benötigt einen Veranstaltungsort

Talks will be online. The link will be shared as the event nears. -- Talk 1: Training RNN agents in a MOBA setting using GA Speaker: Fredrik Norén Abstract: In this talk we will investigate how we can train RNN agents to fight each other in a multiplayer battleground setting. Traditionally this is accomplished using backpropagation, but the Genetic Algorithm has been getting attention as a viable option in the last few years (eg. Uber's "Welcoming the Era of Deep Neuroevolution"). We'll implement an example of this and look at the pros and cons of the method. Bio: Fredrik Norén is the creator of Derk's Gym; the GPU accelerated MOBA RL environment used in this talk. He's also developing an educational ML game based on the same engine. Previously he worked as an engineer and PM at Spotify, NYC. -- Talk 2: TBD Speaker: Abstract: Bio:

Vergangene Events (72)

Compression & efficient processing of Deep Neural Networks

Fotos (35)