If you care about enforcing constraints over time without breaking your computational resources, then read our new blog post over at @aihub.org!
It focuses on showing how our neurosymbolic Markov models beat the SOTA in out-of-distribution generalisation and so much more.
Just under 10 days left to submit your latest endeavours in #tractable probabilistic models!
Join us at TPM @auai.org #UAI2025 and show how to build #neurosymbolic / #probabilistic AI that is both fast and trustworthy!
🚨 New paper: “Towards Adaptive Self-Normalized IS”, @ IEEE Statistical Signal Processing Workshop.
TLDR;
To estimate µ = E_p[f(θ)] with SNIS, 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
Thanks for the opportunity! Having a great time as usual @uoe-sps.bsky.social thanks to @unaeuropa.bsky.social!
Today we have @lennertds.bsky.social from KU Leuven teaching us how to adapt NeSy methods to deal with sequential problems 🚀
Super interesting topic combining DL + NeSy + HMMs! Keep an eye on Lennert's future works!
the #TPM ⚡Tractable Probabilistic Modeling ⚡Workshop is back at @auai.org #UAI2025!
Submit your works on:
- fast and #reliable inference
- #circuits and #tensor #networks
- normalizing #flows
- scaling #NeSy #AI
...& more!
🕓 deadline: 23/05/25
👉 tractable-probabilistic-modeling.github.io/tpm2025/
🔥 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/...
🧵⬇️
Are you interested in more scalable reasoning under uncertainty and attending NeurIPS? Then pass by our poster #3708 later today at 4.30pm! 🕟
We use recursive integer arithmetic to express combinatorial problems and add uncertainty. Inference can be massively accelerated with tensors and the FFT. 🚀
