🚨 Interpretable AI often means sacrificing accuracy—but what if we could have both? Most interpretable AI models, like Concept Bottleneck Models, force us to trade accuracy for interpretability. But not anymore, due to Concept-Based Memory Reasoner (CMR)! #NeurIPS2024 (1/7)

Are you interested in more scalable reasoning under uncertainty and attending NeurIPS? Then pass by our poster #3708 later today at 4.30pm! TL;DR Probabilistic inference of integer arithmetic for probabilistic reasoning can be massively accelerated with tensors and the FFT. 🚀

Unsure where to submit your next research paper to now that aideadlin.es is not updated anymore? Is the location for you as important as the conference? Then check out 🗺️ deadlines.pieter.ai

🔥 Can AI reason over time while following logical rules in relational domains? We will present Relational Neurosymbolic Markov Models (NeSy-MMs) next week at #AAAI2025! 🎉 📜 Paper: arxiv.org/pdf/2412.13023 💻 Code: github.com/ML-KULeuven/ne… 🧵⬇️

We are going to present our poster "Sum of Squares Circuits" at AAAI in Philadelphia today Hall E 12:30pm-14:00pm poster #840 We trace expressiveness connections of different types of additive and subtractive deep mixture models and tensor networks 📜 arxiv.org/abs/2408.11778

Our paper Massively Parallel Expectation Maximization For Approximate Posteriors is now on arXiv! In this work we introduce the QEM method for fast approximate posterior estimation in Hierarchical Bayesian models. 🧵👇

the #TPM ⚡Tractable Probabilistic Modeling ⚡Workshop is back at UAI 2025 #UAI2025! Submit your works on: - fast and #reliable inference - #circuits and #tensor #networks - normalizing #flows - scaling #NeSy #AI 🕓 deadline: 23/05/25 👉 …able-probabilistic-modeling.github.io/tpm2025/

We developed a library to make logical reasoning embarrassingly parallel on the GPU. For those at ICLR 🇸🇬: you can get the juicy details tomorrow (poster #414 at 15:00). Hope to see you there!

🚨 New paper: “Towards Adaptive Self-Normalized IS” TLDR; To estimate µ = E_p[f(θ)] when p(θ) has intractable partition, instead of doing MCMC on p(θ) or learning a parametric q(θ), we try MCMC directly on p(θ)| f(θ)-µ | - variance-minimizing proposal. arxiv.org/abs/2505.00372

Just under 10 days left to submit your latest endeavours in ⚡#tractable⚡ probabilistic models❗ Join us at TPM ยัยตัวร้าย auai.org #UAI2025 and show how to build #neurosymbolic / #probabilistic AI that is both fast and trustworthy!

Obsession with statistics or not .. better make sure evaluations make sense and we're not tuning the random seed. Don't need to know all the fanciest statistical tests, but doesn't seem like that's the issue typically (to me) 🤷‍♂️ Heard of some statistical precipices ..