Philipp Hennig @PhilippHennig5
Professor for the Methods of Machine Learning at the University of Tübingen. mml.inf.uni-tuebingen.de Tübingen, Germany Joined January 2020-
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more power to advanced numerical linear algebra in deep learning.
more power to advanced numerical linear algebra in deep learning.
Fantastic news for our cluster member @PhilippHennig5 ! Find out more about his awarded project in the linked article below. #ERC
Fantastic news for our cluster member @PhilippHennig5 ! Find out more about his awarded project in the linked article below. #ERC
Really enjoying @maosbot's textbook on probabilist numerics, a refreshing take on the field! The musings on the difference between "probabilistic" and "stochastic" are also very provoking (though I might not agree with everything).
I am organising a PhD Summer School on Artificial Intelligence and Law next May! This has been designed specifically for doctoral researchers working in this area. Travel and accommodation costs will be covered for all participants. More here: ailawinstitute.de
Still some places available for probnumschool.org 2024. Join us in Southampton in April to discuss how the algorithmic bedrock of machine learning can be adapted to quantify computational uncertainty from the ground up. Beautiful methods, amazing people. A place for you?
Still some places available for probnumschool.org 2024. Join us in Southampton in April to discuss how the algorithmic bedrock of machine learning can be adapted to quantify computational uncertainty from the ground up. Beautiful methods, amazing people. A place for you?
Textbooks I want to read in the near future: "Understanding Deep Learning", @SimonPrinceAI "Deep Learning", Bishop & Bishop "Probabilistic Numerics", @PhilippHennig5 (1/3)
After 2 years of hard work by the team, we are thrilled to release scholar-inbox.com today! Scholar Inbox is a personal paper recommender which enables you to stay up-to-date with the most relevant progress by delivering personal suggestions directly to your inbox.🧵
Apply by January 15 to join the Tübingen AI faculty as a tenure-track or full professor! Want to learn more? Check out tuebingen.ai/careers or connect with me at #NeurIPS2023 ☕
We’ve built something special in this beautiful corner of Germany. Come and join the best AI/ML/vision/robotics research environment in Europe.
We’ve built something special in this beautiful corner of Germany. Come and join the best AI/ML/vision/robotics research environment in Europe.
Apply by January 15 to join the Tübingen AI Center as a Full Professor or Tenure-Track Professor in the field of #ML & Intelligent Systems! tuebingen.ai/careers #jobs #professorship #ai #machinelearning @uni_tue @MPI_IS @Cyber_Valley @ELLISforEurope
Exciting! "The AlgoPerf: Training algorithms benchmark is a competitive, time-to-result benchmark that runs on a fixed system and compares training algorithms on multiple deep learning workloads. ... Submitters must develop and compete on the basis of more efficient algorithms."
Exciting! "The AlgoPerf: Training algorithms benchmark is a competitive, time-to-result benchmark that runs on a fixed system and compares training algorithms on multiple deep learning workloads. ... Submitters must develop and compete on the basis of more efficient algorithms."
The #ML community is currently unable to reliably identify training algorithm improvements, or even determine the state-of-the-art training algorithm. This has to change if we want to make progress speeding up neural network training mlcommons.org/2023/11/mlc-al…
The #ML community is currently unable to reliably identify training algorithm improvements, or even determine the state-of-the-art training algorithm. This has to change if we want to make progress speeding up neural network training mlcommons.org/2023/11/mlc-al…
After 3 years of hard work, our unprecedented neural network training algorithm competition is finally open! The exciting part starts now, seeing what the community can create. 🏆Submit, become the next Adam, and bag $50,000 in prizes! mlcommons.org/2023/11/mlc-al…
Michael Black @Michael_J_Black
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NeurIPS Conference @NeurIPSConf
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Zoubin Ghahramani @ZoubinGhahrama1
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ECMWF @ECMWF
50K Followers 172 Following European Centre for Medium-Range Weather Forecasts For press enquiries or for general queries to the Service Desk, please visit https://t.co/s2OcUa2Ean
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680 Followers 427 Following Assistant professor for data science in diabetes care at the University of Bern
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398 Followers 114 Following Early Career Group Leader at @ml4science | @uni_tue. Working on #AutoML for Science. All opinions are my own.
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David W Hogg @davidwhogg
11K Followers 802 Following peace, cosmology, stars, exoplanets, engineering, data analysis, emcee, wobble, The Cannon, https://t.co/GDgZayQiDJ, The Joker, #openscience, #otherpeoplesdata
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636 Followers 426 Following Geophysicist at University of Tübingen. Sometimes I talk too much.
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64 Followers 64 Following Machine Learning + Glaciology. PhD student @mackelab, @uni_tue.
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68 Followers 103 Following PhD student in Machine Learning with @c_f_baumgartner | Uncertainty quantification for medical prediction systems
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168 Followers 203 Following Neuroscientist and Clinician Scientist in Neurology. Interested in Epilepsy, Oscillations, Working Memory, Medical AI.
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3K Followers 647 Following Redakteurin @derspiegel Biochemikerin. Krimi- und Sachbuchautorin. Sehe mir X nur noch eine Weile an, teste jetzt @ninaweber.bsky.social
MUT - Museum der Univ.. @museum_uni_tue
2K Followers 275 Following Das MUT beherbergt in mehr als 70 Sammlungen zahlreiche Superlative und spektakuläre Objekte. Größtes Haus: Museum Alte Kulturen auf Schloss Hohentübingen.
Karsten Roth @confusezius
799 Followers 374 Following Generalization, Continual Learning, Multimodality @ELLISforEurope 🇪🇺 Prev. @MetaAI 🇲🇫 @awscloud 🇩🇪 @VectorInst 🇨🇦 @Mila_Quebec 🇨🇦A point I didn't dare make more explicitly in my thesis is that a lot of deep learning active learning can still be summarized by MacKay's 1992 papers (e.g. one below). Whether it's my own works, or e.g. BADGE, BAIT, etc by @jordan_t_ash et al, or others. direct.mit.edu/neco/article-a…
Happy to announce we started an open collaboration with @laion_ai to advance the state of open-source conversational AI! The voice assistant runs on-device, and we strive to reach very low latencies and maximally natural conversations. Joint work with @bschoelkopf…
We present BUD-E: A naturally sounding open-source voice assistant that runs on a standard gaming laptop with low latency, without requiring an internet connection. Join our Discord & help us build your future BUD-E! :) laion.ai/blog/bud-e
If you are looking for a PhD position in the intersection between Deep Learning and Optimization, it's not too late to apply to my group at @MPI_IS and @ELLISforEurope Institute Tübingen! Send a DM if you are interested :) institute-tue.ellis.eu/research-group…
@uni_tue @ml4science Amazing! Congratulations @PhilippHennig5
Fantastic news for our cluster member @PhilippHennig5 ! Find out more about his awarded project in the linked article below. #ERC
👏Comprehensive resource management and data economy for artificial intelligence. #AI researcher Philipp Hennig receives #ERC Consolidator Grant: uni-tuebingen.de/en/news-defaul… @ml4science #AI 📷Philipp Hennig
Really enjoying @maosbot's textbook on probabilist numerics, a refreshing take on the field! The musings on the difference between "probabilistic" and "stochastic" are also very provoking (though I might not agree with everything).
@tychovdo @sid09_singh @f_dangel and I just included a PyTorch K-FAC implementation as a linear operator in the latest release of curvlinops (github.com/f-dangel/curvl…). It supports many variations of K-FAC for nn.Linear and nn.Conv2d modules. Implementation: github.com/f-dangel/curvl…
I am organising a PhD Summer School on Artificial Intelligence and Law next May! This has been designed specifically for doctoral researchers working in this area. Travel and accommodation costs will be covered for all participants. More here: ailawinstitute.de
Textbooks I want to read in the near future: "Understanding Deep Learning", @SimonPrinceAI "Deep Learning", Bishop & Bishop "Probabilistic Numerics", @PhilippHennig5 (1/3)
Apply by January 15 to join the Tübingen AI faculty as a tenure-track or full professor! Want to learn more? Check out tuebingen.ai/careers or connect with me at #NeurIPS2023 ☕
🚨The training and test set of the Street View House Numbers (SVHN) dataset are NOT from the same distribution!🚨 Join us at the #NeurIPS2023 workshop on DistShift this Friday (10:30 am, room R06-R09) to find out more! arxiv.org/abs/2312.02168 w/ @johanneszenn @robamler
How can Kronecker-Factored Approximate Curvature (K-FAC) be generalised to modern deep learning architectures like transformers, graph, and convolutional neural networks? Find out in our #NeurIPS2023 spotlight paper! (1/10) arxiv.org/abs/2311.00636
For the first time, we (with @f_dangel, @runame_, @k_neklyudov @akristiadi7, Richard E. Turner, @AliMakhzani) propose a sparse 2nd-order method for large NN training with BFloat16 and show its advantages over AdamW. also @NeurIPS workshop on Opt for ML arxiv.org/abs/2312.05705 /1
Large NNs like transformers (i) need fp16 to train => matrix inversion in 2nd order methods is unstable, (ii) expensive to store the preconditioner 😩 Our work solves both by exploiting the Riemannian geometry of preconditioning matrices---it's as efficient as AdamW! 🌐
For the first time, we (with @f_dangel, @runame_, @k_neklyudov @akristiadi7, Richard E. Turner, @AliMakhzani) propose a sparse 2nd-order method for large NN training with BFloat16 and show its advantages over AdamW. also @NeurIPS workshop on Opt for ML arxiv.org/abs/2312.05705 /1
To understand machine learning, first understand optimisation. To understand optimisation, first understand machine learning. To understand both—read our book!
It’s out! Oh boy! Probabilistic Numerics: big ideas for the internals of learning machines — and now also a BOOK, w/ @maosbot and @HansKersting! What is #ProbabilisticNumerics, and why does it matter for ML? Thread below, with a link to a free pdf of the book at the end :)
Improved training algorithms can save time, computational resources, and lead to better, more accurate, models. Thank you @AIatMeta for participating in the @MLCommons effort to drive innovation in training algorithms. Join the challenge!
Today the @MLCommons AlgoPerf working group, including researchers from Meta, are introducing a standardized & competitive benchmark designed to provide objective comparisons & quantify progress in the development of new training algorithms. Details ➡️ bit.ly/49Y6E24
Adam must go, we can't let @dpkingma citation counts go to infinity ;)
tl;dr submit a training algorithm* that is faster** than Adam*** and win $10,000 💸🚀 *a set of hparams, self-tuning algorithm, and/or update rule **see rules for how we measure speed ***beat all submissions, currently the best is NAdamW in wallclock and DistShampoo in steps
Apply by January 15 to join the Tübingen AI Center as a Full Professor or Tenure-Track Professor in the field of #ML & Intelligent Systems! tuebingen.ai/careers #jobs #professorship #ai #machinelearning @uni_tue @MPI_IS @Cyber_Valley @ELLISforEurope
Exciting! "The AlgoPerf: Training algorithms benchmark is a competitive, time-to-result benchmark that runs on a fixed system and compares training algorithms on multiple deep learning workloads. ... Submitters must develop and compete on the basis of more efficient algorithms."
To highlight the importance of #ML training & algorithmic efficiency, we’re excited to provide compute resources to help evaluate the best submissions to the @MLCommons AlgoPerf training algorithms competition, w/ a chance to win a prize from MLCommons! goo.gle/3N3sHdD
Show everyone that your optimizer is truly better than AdamW and claim the price money 👇🏻💰
After 3 years of hard work, our unprecedented neural network training algorithm competition is finally open! The exciting part starts now, seeing what the community can create. 🏆Submit, become the next Adam, and bag $50,000 in prizes! mlcommons.org/2023/11/mlc-al…
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