In particular, in this work we identified how in an infinitely wide limit, the autoencoding landscape starts to encode the true "data dictionary" - a fully non-realizable data setup. We were kind-of close to discovering the NTK idea - but we missed it 🫥
In particular, in this work we identified how in an infinitely wide limit, the autoencoding landscape starts to encode the true "data dictionary" - a fully non-realizable data setup. We were kind-of close to discovering the NTK idea - but we missed it 🫥
With all the discussion about "Sparse AutoEncoders" as a way of doing #MechanisticInterpretability of LLMs, I am resharing a part of my PhD where we proved years ago about how sparsity automatically emerges in autoencoding. @NeelNanda5arxiv.org/abs/1708.03735
With all the discussion about #DeepMind 's use of PINNs to "find" unstable singularities in 3D Euler PDE, I feel like advertising that more than a year ago, we had shown that generalization bounds exist for these setups.
iopscience.iop.org/article/10.108…
At #DRSciML, my student Dibyakanti Kumar presenting our results on sample complexity of solving Navier-Stokes PDE via neural nets. Possibly the first such generalization error bounds for a non-linear PDE. #AI4Science#ScientificML
At #DRSciML, my student Dibyakanti Kumar presenting our results on sample complexity of solving Navier-Stokes PDE via neural nets. Possibly the first such generalization error bounds for a non-linear PDE. #AI4Science#ScientificML https://t.co/zmOr4808MA
Registrations close for #DRSciML by Noon (Manchester time). Do register soon to ensure you get the Zoom links to attend this exciting event on the foundations of #ScientificML 💥
Recently I gave an online talk @iiscbangalore 's "Bangalore Theory Seminars" where I explained our results on size lowerbounds for neural models of solving PDEs via neural nets. #SciML I cover work by my 1st year student, Sebastien.
youtu.be/CWvnhv1nMRY?fe…
If you ever needed a paper to start reading LLMs from, then it's hard to find anything better than what's published at venues like these. Do read these by one of my former advisers ! 🔥
If you ever needed a paper to start reading LLMs from, then it's hard to find anything better than what's published at venues like these. Do read these by one of my former advisers ! 🔥
Today is 70th anniversary of the summer meeting at Dartmouth which officially marked the beginning of AI research 💥 Interestingly "Objective 3" in 1955 was already about having theory of neural nets. 🙂
stanford.io/2WJJJGN
I got selected for the "Early Career Highlights" of ACM IKDD International Conference on Data Science (#CODS) 2025. Looking forward to giving the related presentation in December at IISER, Pune.
acm.org/articles/acm-i…
To the best of my surveying so far, the finite-sample guarantee for #KalmanFilter is fairly recent - surprisingly so! It's this Ghattas-Alonso paper doi.org/10.1093/imaiai… at #Information&Inference - one of my favorite journals for obvious reasons 😉
To the best of my surveying so far, the finite-sample guarantee for #KalmanFilter is fairly recent - surprisingly so! It's this Ghattas-Alonso paper doi.org/10.1093/imaiai… at #Information&Inference - one of my favorite journals for obvious reasons 😉
#KalmanFilters are probably among the most important/useful topics that I missed studying during my PhD, despite all the months of lecture on approximation theory that I sat in and did homeworks for. Trying to catch up now! #AI4Science
#KalmanFilters are probably among the most important/useful topics that I missed studying during my PhD, despite all the months of lecture on approximation theory that I sat in and did homeworks for. Trying to catch up now! #AI4Science
Some luck to be hosted by a Godel Prize winner, Prof. Sebastien Pokutta, and to present our work in their group 💥 Sebastien heads this "Zuse Institute Berlin (#ZIB) " which is an amazing oasis of applied mathematics bringing together experts from different institutes in Berlin.
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