George E. Dahl @GeorgeEDahl
Machine learning researcher @Google research. My opinions do not necessarily represent my employer. Prefer email over DMs. https://t.co/FI9gvTzCbO… cs.toronto.edu/~gdahl/ Joined March 2021-
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Want to do some really cool science and apply LLMs to data that's biased and messy, but not in the same way that human language is? We have tons of data, bunches of compute, and an ambitious problem to tackle that just might help save lives 🧬 -> bioptimus.com/careers
Want to do some really cool science and apply LLMs to data that's biased and messy, but not in the same way that human language is? We have tons of data, bunches of compute, and an ambitious problem to tackle that just might help save lives 🧬 -> bioptimus.com/careers
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!
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!
Thrilled to share this work on materials discovery! We found that OOD generalization of GNNs improves predictably, with increasing data from quantum mechanical simulations. These GNNs allowed us to expand the number of known stable materials by an order of magnitude.
Thrilled to share this work on materials discovery! We found that OOD generalization of GNNs improves predictably, with increasing data from quantum mechanical simulations. These GNNs allowed us to expand the number of known stable materials by an order of magnitude.
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."
A big thank you to @GoogleAI for providing the compute resources for the @MLCommons Algorithm benchmark efficiency competition. Learn more about how you can win some of the $50K prize money on our blog. mlcommons.org/2023/11/mlc-al…
A big thank you to @GoogleAI for providing the compute resources for the @MLCommons Algorithm benchmark efficiency competition. Learn more about how you can win some of the $50K prize money on our blog. mlcommons.org/2023/11/mlc-al…
Have you or someone you know worked on second order optimizers? 🤕 Do you need some motivation to pick it back up? 💪 Do you strongly believe diagonal preconditioning is sufficient? 🔔 Then try to beat distributed shampoo in convergence across models? Submit here!
Have you or someone you know worked on second order optimizers? 🤕 Do you need some motivation to pick it back up? 💪 Do you strongly believe diagonal preconditioning is sufficient? 🔔 Then try to beat distributed shampoo in convergence across models? Submit here!
algorithmic efficiency is a crucial bottleneck to overcome the GPU squeeze and reduce the energy footprint of deep learning. I’m excited to see industrial and academic partners join to make progress here.
algorithmic efficiency is a crucial bottleneck to overcome the GPU squeeze and reduce the energy footprint of deep learning. I’m excited to see industrial and academic partners join to make progress here.
Years in the making, the AlgoPerf Competition now opens for submissions! I expect it to set a new industry standard for deep training. Would you like a slice of Adam’s citation count? Your chance! Kudos to @frankstefansch1, @GeorgeEDahl, @zacharynado et al. Great collaboration!
Years in the making, the AlgoPerf Competition now opens for submissions! I expect it to set a new industry standard for deep training. Would you like a slice of Adam’s citation count? Your chance! Kudos to @frankstefansch1, @GeorgeEDahl, @zacharynado et al. Great collaboration!
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…
Introducing the AlgoPerf: Training Algorithms Benchmark! Compete for a share of the $50,000 prize pool by submitting more effective and efficient neural network training algorithms. Learn more mlcommons.org/2023/11/mlc-al… #Algorithms #MachineLearning #Competition
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
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
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
I'm very excited that this paper is out, it has been over 2 years in the making! I started at Google Research speeding up neural net training, but was often frustrated when we didn't know how to declare a win over Adam 🚀
rohan anil @_arohan_
12K Followers 2K Following Principal Engineer, @GoogleDeepMind Gemini. prev PaLM-2. Tinkering with optimization and distributed systems. opinions are my own.
Jim Fan @DrJimFan
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Lucas Beyer (bl16) @giffmana
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MIT CSAIL @MIT_CSAIL
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Delip Rao e/σ @deliprao
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Kyunghyun Cho @kchonyc
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Jeff Dean (@🏡) @JeffDean
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Unbox Research @unboxresearch
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Estadisticas y redes .. @sociadisticas
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43 Followers 504 Following NSU Mom Grad & Registered Mental Health Counselor Intern; Love to Watch and take.
rohan anil @_arohan_
12K Followers 2K Following Principal Engineer, @GoogleDeepMind Gemini. prev PaLM-2. Tinkering with optimization and distributed systems. opinions are my own.
Kyunghyun Cho @kchonyc
60K Followers 2K Following a combination of a mediocre scientist, a mediocre manager, a mediocre advisor & a mediocre PC at @nyuniversity (@CILVRatNYU) & @genentech (@PrescientDesign).
Kevin Patrick Murphy @sirbayes
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Zachary Nado @zacharynado
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Sara Hooker @sarahookr
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Sander Dieleman @sedielem
50K Followers 2K Following Research Scientist at Google DeepMind. I tweet about deep learning (research + software), music, generative models (personal account).
Edward Grefenstette @egrefen
36K Followers 773 Following FR/US/GB AI/ML Person, Director of Research at @GoogleDeepMind, Honorary Professor at @UCL_DARK, @ELLISforEurope Fellow. All posts are personal.
👩💻 Paige Bai.. @DynamicWebPaige
59K Followers 2K Following ✨Keep it simple, make it scale. AI should be about empowering people, building understanding, & making dreams realities. 👩💻GenAI @GoogleDeepMind ex-@GitHub
Mark Saroufim @marksaroufim
9K Followers 652 Following @pytorch dev broadly interested in performance https://t.co/6KJ328JUwv
Gael Varoquaux @GaelV.. @GaelVaroquaux
22K Followers 318 Following Research & code: Research director @inria ►Data, Health, & Computer science ►Python coder, (co)founder of @scikit_learn & joblib ►Art on @artgael ►Physics PhD
Ekin Dogus Cubuk @ekindogus
3K Followers 381 Following Research scientist at Google DeepMind, working on machine learning and materials science.
Daphne Ippolito @daphneipp
1K Followers 72 Following I am a senior research scientist at Google. I research topics in natural language generation.
Sharad Vikram @sharadvikram
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Black in AI @black_in_ai
45K Followers 323 Following A place for sharing ideas, fostering collaborations and discussing initiatives to increase the presence of Black people in the field of Artificial Intelligence.
WiML @WiMLworkshop
18K Followers 1K Following Women in Machine Learning organization. Maintains a list of women in ML. Profiles the research of women in ML. Annual workshop and other events.
Sherry Yang @mengjiao_yang
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David Bieber @Bieber
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@[email protected].. @cdleary
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Laurent Dinh @laurent_dinh
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Erin Grant @ermgrant
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Ari Morcos @arimorcos
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Maithra Raghu @maithra_raghu
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Anna Huang @huangcza
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Adam Roberts @ada_rob
7K Followers 645 Following ai researcher @ Google DeepMind :: ♫ (MusicVAE, NSynth, MusicLM, SingSong) & 📝 (T5, PaLM) & :: t5x & seqio // recovering comp biologist
Jasper @latentjasper
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Yee Whye Teh @yeewhye
24K Followers 1K Following Find me @[email protected] Professor at @OxCSML, @oxfordstats and Research Director at @GoogleDeepMind. All opinions are my own.Want to do some really cool science and apply LLMs to data that's biased and messy, but not in the same way that human language is? We have tons of data, bunches of compute, and an ambitious problem to tackle that just might help save lives 🧬 -> bioptimus.com/careers
In case you missed it @bioptimus_ai: We are looking for the best talents (ML/biology/large-scale infrastructure) to join our fantastic technical team @ZeldaMariet @FelipeLlinares @jeanphi_vert 👉 Bioptimus.com/careers
Ok I’m at NeurIPs to talk about our in person competition workshop on LLM efficiency on Friday Dec 15 between 1:30 - 4:30 pm CST Competitors had to fine tune 1 LLM in 1 day on 1 GPU and the reception was incredible. This was one of the most popular ML competitions of the year.…
@zacharynado It's actually 2 times $25,000 that you can win if someone even needs more motivation to submit🙂
@pr0timr until March 28, 2024 to develop submissions (but read the full rules for the setup github.com/mlcommons/algo…)
@raxtechbits I would love to see how good AI generated optimizers are compared to the baselines
@GeorgeEDahl You're arguing that the bottleneck is what biostatisticiens call "external validation"? In other words, what we actually want to do is generalize from a benchmark to a domain
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
Thrilled to share this work on materials discovery! We found that OOD generalization of GNNs improves predictably, with increasing data from quantum mechanical simulations. These GNNs allowed us to expand the number of known stable materials by an order of magnitude.
Introducing GNoME: an AI tool that helped discover 2.2 million new crystals. 💎 Crystals are found in everything from the chips powering our phones to solar cells creating clean energy. The model also better predicts the stability of new materials. 🧵 dpmd.ai/GNoME-AI
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
A big thank you to @GoogleAI for providing the compute resources for the @MLCommons Algorithm benchmark efficiency competition. Learn more about how you can win some of the $50K prize money on our blog. mlcommons.org/2023/11/mlc-al…
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
Have you or someone you know worked on second order optimizers? 🤕 Do you need some motivation to pick it back up? 💪 Do you strongly believe diagonal preconditioning is sufficient? 🔔 Then try to beat distributed shampoo in convergence across models? Submit here!
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
More information here: mlcommons.org/2023/11/mlc-al…
algorithmic efficiency is a crucial bottleneck to overcome the GPU squeeze and reduce the energy footprint of deep learning. I’m excited to see industrial and academic partners join to make progress here.
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
Years in the making, the AlgoPerf Competition now opens for submissions! I expect it to set a new industry standard for deep training. Would you like a slice of Adam’s citation count? Your chance! Kudos to @frankstefansch1, @GeorgeEDahl, @zacharynado et al. Great collaboration!
Introducing the AlgoPerf: Training Algorithms Benchmark! Compete for a share of the $50,000 prize pool by submitting more effective and efficient neural network training algorithms. Learn more mlcommons.org/2023/11/mlc-al… #Algorithms #MachineLearning #Competition
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…
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
New exciting competition! Beat Adam, win money:)
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…
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