Justin Gilmer @jmgilmer
Research Scientist at Google Brain. Joined June 2014-
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Happy to share that our #MLOps paper "Improving Training Stability for Multitask Ranking Models in Recommender Systems" got accepted to KDD 2023 🎉 arxiv.org/abs/2302.09178 Joint work between Google DeepMind (@edchi, @jmgilmer and others) and YouTube. Link to code below. 🧵
1/ There is a huge headroom for improving capabilities of our vision models and given the lessons we've learned from LLMs, scaling is a promising bet. We are introducing ViT-22B, the largest vision backbone reported to date: arxiv.org/abs/2302.05442
(6/6) ...sin embargo no se ha probado que converja a algún número. Lo que sí se sabe (lo probó Justin Gilmer @jmgilmer en 2013), es que la densidad de números felices está acotada entre 0.1138 y 0.18577. degruyter.com/document/doi/1…
I often get asked for advice on breaking into a machine learning role. Here's a short post, in case it helps you. varungodbole.github.io/2022/09/18/bre…
I finally found time to put my PhD thesis on arxiv. Check it out! Thanking everyone who made this thesis possible 🖤🎊 arxiv.org/abs/2104.12053
This 2019 paper on Fourier analysis of adversarial robustness, by Dong Yin et al., is really worth a look. It gives a simple, intuitive way of understanding a wide variety of adversarial and robustness phenomena. papers.nips.cc/paper/9483-a-f…
Check out our new paper on adversarial robustness as a prior for better transfer learning! A great colab w/ @andrew_ilyas @logan_engstrom @akapoor_av8r @aleks_madry We open-source ~70 robust ImageNet models which you might find helpful! github.com/microsoft/robu…
Check out our new paper on adversarial robustness as a prior for better transfer learning! A great colab w/ @andrew_ilyas @logan_engstrom @akapoor_av8r @aleks_madry We open-source ~70 robust ImageNet models which you might find helpful! github.com/microsoft/robu…
Every day a new Y-axis extension for the country without a plan New cases beget new cases, hospitalizations, chronic illness, and deaths. That demands serious, aggressive action to intervene.
Very thought-provoking talk by Justin Gilmer at the #ICML2020 UDL workshop. Adversarial examples are just a case of out-of-distribution error. There is no particular reason to defend against the nearest OOD error (i.e., L-infty adversarial example) 1/
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Many of these trends don't hold. Last week we celebrated @geoffreyhinton's retirement, and a few weeks earlier saw @kkariko receive the Nobel Prize. Their research took decades to come together, and had enormous impact at a world scale. We'd be much worse off if they'd pivoted!
Enjoyed visiting UC Berkeley’s Machine Learning Club yesterday, where I gave a talk on doing AI research. Slides: docs.google.com/presentation/d… In the past few years I’ve worked with and observed some extremely talented researchers, and these are the trends I’ve noticed: 1. When…
not only is @jmgilmer a beast at debugging large scale transformers, he can also do a graduate thesis's worth of mathematics in an hour long walk A Constant Lower Bound for Frankl's Union-Closed Sets Conjecture youtu.be/veMx-r1iQpY via @YouTube
Startling new from a seminar in Cambridge about a problem of Erdős. Which problem??? gilkalai.wordpress.com/2023/03/16/som…
The confusion of people assigning human attributes stems from the fact that humans use language to communicate their internal state quite often. LLMs never communicate their internal state. They just aim to model the distribution of past human outputs in certain contexts.
Simplest example of warm-up being useful. Minimize (x*y)^2. Gradient descent with exponentially increasing learning rate recovers least-norm solution, constant learning rate does not. Petal-shape is due to some initial points requiring more steps to converge.
@poolio Not sure what you’re after and why. There is a concept of measure and density on a manifold. In your case, the manifold is R^D and the density is the Gaussian. Apparently, there is also the concept of “fuzzy manifold” but that seems like a whole other can of worms 😅
(6/6) ...sin embargo no se ha probado que converja a algún número. Lo que sí se sabe (lo probó Justin Gilmer @jmgilmer en 2013), es que la densidad de números felices está acotada entre 0.1138 y 0.18577. degruyter.com/document/doi/1…
I love my partner to death, but dear god, I made the mistake of peeking at some simulation source code he was writing. Never look at source code written by Physicists.
You Wouldn't Last 48 Hours In The Real Wild West...
MIT CSAIL is excited to announce the Mentored Opportunity in Research Postdoctoral Fellowship (Meteor), to broaden participation in computing and artificial intelligence: csail.mit.edu/meteor
I finally found time to put my PhD thesis on arxiv. Check it out! Thanking everyone who made this thesis possible 🖤🎊 arxiv.org/abs/2104.12053
I'm struck by how the divide in the US is not between red states and blue states but between urban and rural regions. This is certainly true in Oregon. 1/
We may be opponents — but we are not enemies. We are Americans.
This 2019 paper on Fourier analysis of adversarial robustness, by Dong Yin et al., is really worth a look. It gives a simple, intuitive way of understanding a wide variety of adversarial and robustness phenomena. papers.nips.cc/paper/9483-a-f…
Check out our new paper on adversarial robustness as a prior for better transfer learning! A great colab w/ @andrew_ilyas @logan_engstrom @akapoor_av8r @aleks_madry We open-source ~70 robust ImageNet models which you might find helpful! github.com/microsoft/robu…
Does adv. robustness help transfer learning? W/ @hadisalmanX @andrew_ilyas @logan_engstrom @akapoor_av8r @MSFTResearch we show l2-robust ImageNet models transfer better, despite lower accuracy: arxiv.org/abs/2007.08489. Blog: gradientscience.org/transfer-learn… Code: github.com/microsoft/robu…
Apropos of recent discussions on whether "Lp robustness is useful", @RICEric22 and I have a new paper on learning adversarial perturbation models from data. This learns to map an L2 latent space (over which we perform adversarial training) to "realistic" perturbations of inputs.
1/ New paper on learning perturbation sets for robust machine learning! We study how to characterize real world perturbations in a well-defined set. Paper: arxiv.org/abs/2007.08450 Blog post: locuslab.github.io/2020-07-20-per… Code: github.com/locuslab/pertu… Joint work with @zicokolter
1/ New paper on learning perturbation sets for robust machine learning! We study how to characterize real world perturbations in a well-defined set. Paper: arxiv.org/abs/2007.08450 Blog post: locuslab.github.io/2020-07-20-per… Code: github.com/locuslab/pertu… Joint work with @zicokolter
@jmgilmer @tdietterich Totally agreed. Perhaps some nuance is lost in orig tweet and I should watch your talk :) I think most of community now views Lp as test for methods, rather than directly useful task (similar to e.g. CIFAR-10). IMO, Lp work still important but needs clarity on true motivation.
@quantrad I think Justin would say rather that data shift is a huge problem and that if we solve it, we will also have solved the adversarial example “problem”
@SebastienBubeck Interesting. I view adversarial examples as a symptom of a weakness (cannot write disease because this is twitter and I'd be eaten alive even though I have no bad intentions). Then the question is whether you want to treat the symptom, or discover and cure the weakness.
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