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. 🚀