NeurIPS 2021 - The Austrian contributions

NeurIPS 2021 - The Austrian contributions

We celebrate the Austrian researchers machine learning contribution to NeurIPS 2021!

Watch 10 paper presentations of Austrian machine learning and artificial researchers (See the paper list below):

Mathias Lechner, Machine Learning Researcher and a PhD candidate @ IST Austria will explain about two of his papers which were accepted:
"Infinite Time Horizon Safety of Bayesian Neural Networks" -

"Causal Navigation by Continuous-time Neural Networks" -

Ramin Hasani, Postdoctoral Associate at MIT, will tell us about his research:
"Sparse Flows: Pruning Continuous-depth Models" -

Rahim Entezari, Ph.D. Candidate at TU Graz/Complexity Science Hub, will talk about his research: The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks

Elias Frantar, IST Austria: "M-FAC: Efficient Matrix-Free Approximations of Second-Order Information" -

Alexandra Peste, IST Austria: "AC/DC: Alternating Compressed/Decompressed Training of Deep Neural Networks" -

Giorgi Nadiradze, IST Austria: "Fully-Asynchronous Decentralized SGD with Quantized and Local Updates"

Werner Zellinger from SCCH - Software Competence Center Hagenberg will present his paper:
"The balancing principle for parameter choice in distance-regularized domain adaptation"

Viktoriia Korchemna, TU Wien, will present her work: "The Complexity of Bayesian Network Learning: Revisiting the Superstructure" -

and Kajetan Schweighofer from Kepler Universit├Ąt Linz will present their workshop paper:
"Understanding the Effects of Dataset Composition on Offline Reinforcement Learning"

Liad Magen
Vienna, Austria
Besides conducting machine learning workshops and teaching technical courses, I help the professional developers' community to keep current by writing technical blog posts.