{"id":11203,"date":"2019-12-03T10:33:25","date_gmt":"2019-12-03T10:33:25","guid":{"rendered":"https:\/\/cyber-valley.de\/news\/cyber-valley-das-dritte-jahr-in-folge-stark-vertreten-bei-fuehrender-machine-learning-konferenz\/"},"modified":"2026-07-15T14:44:21","modified_gmt":"2026-07-15T14:44:21","slug":"cyber-valley-das-dritte-jahr-in-folge-stark-vertreten-bei-fuehrender-machine-learning-konferenz","status":"publish","type":"news","link":"https:\/\/cyber-valley.de\/de\/news\/cyber-valley-das-dritte-jahr-in-folge-stark-vertreten-bei-fuehrender-machine-learning-konferenz\/","title":{"rendered":"Cyber\u00a0Valley das dritte Jahr in Folge stark vertreten bei f\u00fchrender Machine Learning Konferenz"},"content":{"rendered":"\n

Laut einem Ranking, das auf der Grundlage der Anzahl der Publikationen auf den wichtigsten Konferenzen f\u00fcr maschinelles Lernen NeurIPS und ICML von 2009 bis 2019 erstellt wurde, belegen die akademischen Partner des Cyber\u00a0Valley derzeit den ersten Platz in Europa und Deutschland. Weltweit belegt das Forschungskonsortium den 8. Platz.<\/strong><\/p>\n\n\n\n

Stuttgart\/T\u00fcbingen \u2013 Mit insgesamt 27 akzeptierten Publikationen werden Forscher*innen aus dem Cyber\u00a0Valley \u00d6kosystem auf der kommenden 33. Conference on Neural Information Processing Systems (NeurIPS)<\/a> sehr gut vertreten sein. Die weltweit f\u00fchrende Konferenz f\u00fcr maschinelles Lernen findet vom 8. bis 14. Dezember in Vancouver, Kanada, statt.<\/p>\n\n\n\n

\nDie eingereichten Publikationen bei der NeurIPS haben sich seit 2017 verdoppelt, was auf das rasante Wachstum im Bereich des maschinellen Lernens zur\u00fcckzuf\u00fchren ist. In diesem Jahr wurden auf der Konferenz ein neuer Rekord gebrochen: 6743 Beitr\u00e4ge wurden weltweit eingereicht, von denen 1428 angenommen wurden.<\/p>\n\n\n\n

\n\u201eUnsere solide Pr\u00e4senz auf einer der weltweit kompetitivsten Konferenzen f\u00fcr maschinelles Lernen zeigt deutlich, dass Wissenschaftler*innen aus dem Cyber\u00a0Valley zu den Besten geh\u00f6ren“, sagte Professor Bernhard Sch\u00f6lkopf, Direktor am Max-Planck-Institut f\u00fcr Intelligente Systeme in T\u00fcbingen. Er leitet dort die Abteilung f\u00fcr Empirische Inferenz. \u201eW\u00e4hrend sich das Cyber\u00a0Valley seinem dritten Jahrestag n\u00e4hert, zeigt unser Erfolg bei der NeurIPS eindrucksvoll, welche Dynamik die Region Stuttgart-T\u00fcbingen als globaler KI-Hotspot entwickelt hat.“<\/p>\n\n\n\n

\nDie im Dezember 2016 im S\u00fcdwesten Deutschlands gegr\u00fcndete Initiative Cyber\u00a0Valley ist Europas f\u00fchrendes Forschungskonsortium auf dem Gebiet der k\u00fcnstlichen Intelligenz. Laut einem Ranking, das auf der Anzahl der Ver\u00f6ffentlichungen auf den wichtigsten globalen Konferenzen f\u00fcr maschinelles Lernen NeurIPS und ICML<\/a> von 2009 bis 2019 basiert, belegen die akademischen Partner des Cyber\u00a0Valley derzeit weltweit Platz 8, in Europa und Deutschland sogar Platz 1.<\/p>\n\n\n\n

\"Machine<\/p>\n\n\n\n

F\u00fchrende Institutionen weltweit, basierend auf der Anzahl der Ver\u00f6ffentlichungen auf den wichtigsten Konferenzen f\u00fcr maschinelles Lernen(NeurIPS\/NIPS & ICML)1 (2009-2019)<\/p>\n\n\n\n

<\/p>\n\n\n\n

Beitr\u00e4ge von deutschen Forschungsstandorten zu den wichtigsten Konferenzen f\u00fcr machinelles Lernen (NeurIPS\/NIPS & ICML)2, in Prozent\u00a0(2009-2019)<\/p>\n\n\n\n

1. Quelle: dlpb, 2. Quelle: interne Analyse<\/p>\n\n\n\n

\n\u201eUnsere Forschungsexzellenz ist der Hauptgrund, warum das Cyber\u00a0Valley seit seiner Gr\u00fcndung vor drei Jahren stark gewachsen ist. Im Herzen Baden-W\u00fcrttembergs ist ein KI-\u00d6kosystem entstanden, das in Europa seinesgleichen sucht“, sagt Sch\u00f6lkopf. Allein im vergangenen Monat wurden mehrere wichtige Meilensteine erreicht, darunter der Beitritt der Fraunhofer Gesellschaft, der Start des neuen Cyber-Valley-Start-Up-Netzwerks, zus\u00e4tzliche Investitionen der Industriepartner Robert Bosch GmbH und Amazon in T\u00fcbingen sowie der erfolgreiche Abschluss einer dritten Bewerbungsrunde an der International Max Planck Research School for Intelligent Systems<\/a>, dem im Rahmen der Cyber-Valley-Initiative gegr\u00fcndeten Doktoranden-Programm.<\/p>\n\n\n\n

NeurIPS 2019: Accepted Submissions from the Cyber\u00a0Valley Ecosystem (alphabetical)<\/span><\/span><\/b><\/span><\/span><\/p>\n\n\n\n

  1. Provably robust boosted decision stumps and trees against adversarial attacks<\/span><\/span><\/a>\nMaksym Andriushchenko (EPFL) Matthias Hein (University of T\u00fcbingen)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  2. Control What You Can: Intrinsically Motivated Task-Planning Agent<\/span><\/span><\/a>\nSebastian Blaes (Max Planck Institute for Intelligent Systems) Marin Vlastelica Pogan (Max-Planck Institute for Intelligent Systems, T\u00fcbingen) Jia-Jie Zhu (Max Planck Institute for Intelligent Systems) Georg Martius (MPI for Intelligent Systems)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  3. Accurate, reliable and fast robustness evaluation<\/span><\/span><\/a>\nWieland Brendel (AG Bethge, University of T\u00fcbingen) Jonas Rauber (University of T\u00fcbingen) Matthias K\u00e4mmerer (University of T\u00fcbingen) Ivan Ustyuzhaninov (University of T\u00fcbingen) Matthias Bethge (University of T\u00fcbingen)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  4. Disentangled behavioural representations<\/span><\/span><\/a>\nAmir Dezfouli (Data61, CSIRO) Hassan Ashtiani (McMaster University) Omar Ghattas (CSIRO) Richard Nock (Data61, the Australian National University and the University of Sydney) Peter Dayan (Max Planck Institute for Biological Cybernetics) Cheng Soon Ong (Data61 and ANU)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  5. Foundations of Comparison-Based Hierarchical Clustering<\/span><\/span><\/a>\nDebarghya Ghoshdastidar (University of T\u00fcbingen) Micha\u00cel Perrot (Max Planck Institute for Intelligent Systems) Ulrike von Luxburg (University of T\u00fcbingen)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  6. On the Role of Inductive Bias From Simulation and the Transfer to the Real World: a new Disentanglement Dataset<\/span><\/span><\/a>\nMuhammad Waleed Gondal (Max Planck Institute for Intelligent Systems) Manuel Wuthrich (Max Planck Institute for Intelligent Systems) Djordje Miladinovic (ETH Zurich) Francesco Locatello (ETH Z\u00fcrich – MPI T\u00fcbingen) Martin Breidt (MPI for Biological Cybernetics)\u00a0 Valentin Volchkov (Max Planck Institute for Intelligent Systems) Joel Akpo (Max Planck Institute for Intelligent Systems) Olivier Bachem (Google Brain) Bernhard Sch\u00f6lkopf (MPI for Intelligent Systems) Stefan Bauer (MPI for Intelligent Systems)<\/span><\/span><\/span><\/span><\/li>\n
  7. Grid Saliency for Context Explanations of Semantic Segmentation<\/span><\/span><\/a>\nLukas Hoyer (Bosch Center for Artificial Intelligence) \u00b7 Mauricio Munoz (Bosch Center for Artificial Intelligence) \u00b7 Prateek Katiyar (Bosch Center for Artificial Intelligence) \u00b7 Anna Khoreva (Bosch Center for Artificial Intelligence) \u00b7 Volker Fischer (Robert Bosch GmbH, Bosch Center for Artificial Intelligence)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  8. Causal regularization<\/span><\/span><\/a>\nDominik Janzing (Amazon)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  9. Convergence Guarantees for Adaptive Bayesian Quadrature Methods<\/span><\/span><\/a>\nMotonobu Kanagawa (EURECOM) Philipp Hennig (University of T\u00fcbingen and MPI for Intelligent Systems T\u00fcbingen)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  10. On Tractable Computation of Expected Predictions<\/span><\/span><\/a>\nPasha Khosravi (UCLA) YooJung Choi (UCLA) Yitao Liang (UCLA) Antonio Vergari (Max Planck Institute for Intelligent Systems) Guy Van den Broeck (UCLA)<\/span><\/span><\/span><\/span>\n\n\t\u00a0<\/li>\n
  11. Limitations of the empirical Fisher approximation<\/span><\/span><\/a>\nFrederik Kunstner (EPFL) Philipp Hennig (University of T\u00fcbingen and MPI for Intelligent Systems T\u00fcbingen) Lukas Balles (University of Tuebingen)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  12. Learning from brains how to regularize machines<\/span><\/span><\/a>\nZhe Li (Baylor College of Medicine) Wieland Brendel (AG Bethge, University of T\u00fcbingen) Edgar Walker (Baylor College of Medicine) Erick Cobos (Baylor College of Medicine) Taliah Muhammad (Baylor College of Medicine) Jacob Reimer (Baylor College of Medicine) Matthias Bethge (University of T\u00fcbingen) Fabian Sinz (University T\u00fcbingen) Zachary Pitkow (BCM\/Rice) Andreas Tolias (Baylor College of Medicine)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  13. On the Fairness of Disentangled Representations<\/span><\/span><\/a>\nFrancesco Locatello (ETH Z\u00fcrich – MPI T\u00b8bingen) Gabriele Abbati (University of Oxford) Tom Rainforth (University of Oxford) Stefan Bauer (MPI for Intelligent Systems) Bernhard Sch\u00f6lkopf (MPI for Intelligent Systems) Olivier Bachem (Google Brain)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  14. Stochastic Frank-Wolfe for Composite Convex Minimization<\/span><\/span><\/a>\nFrancesco Locatello (ETH Z\u00fcrich – MPI T\u00fcbingen) Alp Yurtsever (EPFL) Olivier Fercoq (Telecom ParisTech) Volkan Cevher (EPFL)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  15. The Functional Neural Process<\/span><\/span><\/a>\nChristos Louizos (University of Amsterdam) Xiahan Shi (Bosch Center for Artificial Intelligence) Klamer Schutte (TNO) Max Welling (University of Amsterdam \/ Qualcomm AI Research)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  16. Fisher Efficient Inference of Intractable Models<\/span><\/span><\/a>\nSong Liu (University of Bristol) Takafumi Kanamori (Tokyo Institute of Technology\/RIKEN) Wittawat Jitkrittum (Max Planck Institute for Intelligent Systems) Yu Chen (University of Bristol)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  17. Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs<\/span><\/span><\/a>\nPedro Mercado (University of T\u00fcbingen) Francesco Tudisco (University of Strathclyde) Matthias Hein (University of T\u00fcbingen)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  18. Selecting causal brain features with a single conditional independence test per feature<\/span><\/span><\/a>\nAtalanti Mastakouri (Max Planck Institute for Intelligent Systems) Bernhard Sch\u00f6lkopf (MPI for Intelligent Systems) Dominik Janzing (Amazon)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  19. Beating SGD Saturation with Tail-Averaging and Minibatching<\/span><\/span><\/a>\nNicole Muecke (University of Stuttgart) Gergely Neu (Universitat Pompeu Fabra) Lorenzo Rosasco (University of Genova \u2013 MIT \u2013 IIT)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  20. Perceiving the arrow of time in autoregressive motion<\/span><\/span><\/a>\nKristof Meding (Max Planck Institute for Intelligent Systems) Dominik Janzing (Amazon) Bernhard Sch\u00f6lkopf (MPI for Intelligent Systems) Felix A. Wichmann (University of T\u00fcbingen)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  21. DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision<\/span><\/span><\/a>\nTam Nguyen (Freiburg Computer Vision Lab) Maximilian Dax (Bosch GmbH) Chaithanya Kumar Mummadi (Bosch Center for Artificial Intelligence) Nhung Ngo (Bosch Center for Artificial Intelligence) \u00b7 Thi Hoai Phuong Nguyen (Karlsruhe Institute of Technology (KIT))\u00a0 Zhongyu Lou (Robert Bosch Gmbh) Thomas Brox (University of Freiburg)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  22. Practical and Consistent Estimation of f-Divergences<\/span><\/span><\/a>\nPaul Rubenstein (Max Planck Institute for Intelligent Systems) Olivier Bousquet (Google Brain (Zurich)) Josip Djolonga (Google Research, Brain Team) Carlos Riquelme (Google Brain) Ilya Tolstikhin (MPI for Intelligent Systems)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  23. Kernel Stein Tests for Multiple Model Comparison<\/span><\/span><\/a>\nJen Ning Lim (Max Planck Institute for Intelligent Systems) Makoto Yamada (Kyoto University \/ RIKEN AIP) Bernhard Sch\u00f6lkopf (MPI for Intelligent Systems) Wittawat Jitkrittum (Max Planck Institute for Intelligent Systems)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  24. Invert to Learn to Invert<\/span><\/span><\/a>\nPatrick Putzky (University of Amsterdam, Max Planck Institute for Intelligent Systems), Max Welling (University of Amsterdam)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  25. Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse<\/span><\/span><\/a>\nCornelius Schr\u00f6der (University of T\u00fcbingen) Ben James (University of Sussex) Leon Lagnado (University of Sussex) Philipp Berens (University of T\u00fcbingen)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  26. Are Disentangled Representations Helpful for Abstract Visual Reasoning?<\/span><\/span><\/a>\nSjoerd van Steenkiste (The Swiss AI Lab – IDSIA) Francesco Locatello (ETH Z\u00fcrich – MPI T\u00fcbingen) J\u00fcrgen Schmidhuber (Swiss AI Lab, IDSIA (USI & SUPSI) – NNAISENSE) Olivier Bachem (Google Brain)<\/span><\/span><\/span><\/span>\n\t\u00a0<\/li>\n
  27. Progressive Augmentation of GANs<\/span><\/span><\/a>\nDan Zhang (Bosch Center for Artificial Intelligence) \u00b7 Anna Khoreva (Bosch Center for Artificial Intelligence)<\/span><\/span><\/span><\/span><\/li><\/ol>\n","protected":false},"featured_media":11532,"parent":0,"template":"","meta":{"_acf_changed":false},"cv-tag":[],"location":[],"network-role":[],"news-category":[362],"user-group":[249],"class_list":["post-11203","news","type-news","status-publish","has-post-thumbnail","hentry","news-category-press-release","user-group-journalist"],"acf":[],"yoast_head":"\nCyber Valley das dritte Jahr in Folge stark vertreten bei f\u00fchrender Machine Learning Konferenz - Cyber Valley<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cyber-valley.de\/de\/news\/cyber-valley-das-dritte-jahr-in-folge-stark-vertreten-bei-fuehrender-machine-learning-konferenz\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cyber Valley das dritte Jahr in Folge stark vertreten bei f\u00fchrender Machine Learning Konferenz - Cyber Valley\" \/>\n<meta property=\"og:description\" content=\"Laut einem Ranking, das auf der Grundlage der Anzahl der Publikationen auf den wichtigsten Konferenzen f\u00fcr maschinelles Lernen NeurIPS und ICML von 2009 bis 2019 erstellt wurde, belegen die akademischen Partner des Cyber\u00a0Valley derzeit den ersten Platz in Europa und Deutschland. 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