🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

Excited to present Generative Marginalization Models (MAMs) at #ICML2024! MAM trains a neural network for fast estimation of arbitrary marginals in discrete data, bypassing the need to eval. a sequence of conditional probs in any-order-ARMs. w/ Peter Ramadge, Ryan Adams 1/7

We are about one election cycle away from prediction markets being manipulated by state actors in the way social media was in 2016.

Excited to share CryoBench🧊🪑 our dataset and benchmarking effort for heterogeneous cryo-EM reconstruction! Led by Minkyu Jeon, who is an absolute machine, and super fun collab with Pilar Cossio Sonya groups Flatiron Institute A few thoughts on our benchmark design 👇

Our recent work on developing a "physics aware" (or convex hull aware) active search method to more efficiently discover stable materials is now published in Materials Horizons! Link to PDF: pubs.rsc.org/en/content/art…

Our new paper in Nature Machine Intelligence tells a story about how, and why, ML methods for solving PDEs do not work as well as advertised. We find that two reproducibility issues are widespread. As a result, we conclude that ML-for-PDE solving has reached overly optimistic conclusions.

BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”

We are looking for AI Postdoctoral Fellows! Be a part of AI for Accelerating Invention, a new research initiative out of the Princeton AI Lab. You can learn about AI for Accelerating Invention here: invent.ai.princeton.edu and apply for the postdoc here: puwebp.princeton.edu/AcadHire/apply….

#PrincetonU has launched AI for Accelerating Invention. Led by professors Mengdi Wang and Ryan Adams, the AI^2 initiative aims to achieve faster breakthroughs across engineering disciplines: bit.ly/3TM5Gzn

Since adaptive tokenisation is trendy these days, this paper from a decade ago (an absolute eternity in DL ⌛️) is worth revisiting! By Oren Rippel, Michael Gelbart and Ryan Adams arxiv.org/abs/1402.0915

#CVPR2025 Area Chairs (ACs) identified a number of highly irresponsible reviewers, those who either abandoned the review process entirely or submitted egregiously low-quality reviews, including some generated by large language models (LLMs). 1/2

You need to understand both General Relativity and Hubble expansion to correctly engineer Global Positioning System, which is a central part of modern civilian and military infrastructure. Here's why. 1/

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Not sure why the gutting of American science funding isn’t a bigger story. No one voted for it, it reduces American innovation and economic competitiveness in the near-term and long-term, and it isn’t even being done efficiently, if that were in fact the goal.

Congrats to Kai Li on being named a member of the American Academy of Arts & Sciences! 🎉 Li joined Princeton University in 1986 and has made important contributions to several research areas in computer science. bit.ly/3RPLxas

Missing ICML due to visa :'(, but looking forward to share our ICML paper (arxiv.org/abs/2502.05318) as a poster at #BayesComp, Singapore! Work on symmetrising neural nets for schrodinger equation in crystals, with the amazing Zhan Ni, Elif Ertekin, Peter Orbanz and Ryan Adams