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" - https://papers.nips.cc/paper/2021/hash/544defa9fddff50c53b71c43e0da72be-Abstract.html
"Causal Navigation by Continuous-time Neural Networks" - https://papers.nips.cc/paper/2021/hash/67ba02d73c54f0b83c05507b7fb7267f-Abstract.html
Ramin Hasani, Postdoctoral Associate at MIT, will tell us about his research:
"Sparse Flows: Pruning Continuous-depth Models" - https://arxiv.org/abs/2111.04714
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
https://arxiv.org/pdf/2110.06296.pdf
Elias Frantar, IST Austria: "M-FAC: Efficient Matrix-Free Approximations of Second-Order Information" - https://arxiv.org/abs/2107.03356
Alexandra Peste, IST Austria: "AC/DC: Alternating Compressed/Decompressed Training of Deep Neural Networks" - https://arxiv.org/abs/2106.12379
Giorgi Nadiradze, IST Austria: "Fully-Asynchronous Decentralized SGD with Quantized and Local Updates" https://proceedings.neurips.cc/paper/2021/hash/362c99307cdc3f2d8b410652386a9dd1-Abstract.html
Werner Zellinger from SCCH - Software Competence Center Hagenberg will present his paper:
"The balancing principle for parameter choice in distance-regularized domain adaptation" https://papers.nips.cc/paper/2021/hash/ae0909a324fb2530e205e52d40266418-Abstract.html
Viktoriia Korchemna, TU Wien, will present her work: "The Complexity of Bayesian Network Learning: Revisiting the Superstructure" - https://papers.nips.cc/paper/2021/hash/040a99f23e8960763e680041c601acab-Abstract.html
and Kajetan Schweighofer from Kepler Universität Linz will present their workshop paper:
"Understanding the Effects of Dataset Composition on Offline Reinforcement Learning" https://arxiv.org/abs/2111.04714
