Lena Zellinger

and to @leanderk.bsky.social @paolomorettin.bsky.social Roberto Sebastiani, @andreapasserini.bsky.social @nolovedeeplearning.bsky.social
for the ✨Best Student Paper Runner Up Award✨ for

"A Probabilistic Neurosymbolic Layer for Algebraic Constraint Satisfaction"

👉 openreview.net/forum?id=9Uk...

24 hours more to submit your latest papers on #TPMs!

We propose Neurosymbolic Diffusion Models! We find diffusion is especially compelling for neurosymbolic approaches, combining powerful multimodal understanding with symbolic reasoning 🚀

Read more 👇

Towards Adaptive Self-Normalized Importance Samplers The self-normalized importance sampling (SNIS) estimator is a Monte Carlo estimator widely used to approximate expectations in statistical signal processing and machine learning. The efficiency of S...

🚨 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

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!

It’s great to have @wouterboomsma.bsky.social talking at UoE today! Happening at 2pm at EFI 2.35.

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/

I am at @realaaai.bsky.social #AAAI25 in sunny #Philadelphia 🌞

reach out if you want to grab coffee and chat about #probabilistic #ML #AI #nesy #neurosymbolic #tensor #lowrank models!

check out our tutorial
👉 april-tools.github.io/aaai25-tf-pc...

and workshop
👉 april-tools.github.io/colorai/

GitHub - adrianjav/causal-flows: CausalFlows: A library for Causal Normalizing Flows in Pytorch CausalFlows: A library for Causal Normalizing Flows in Pytorch - adrianjav/causal-flows

Have you ever been curious to try Causal Normalizing Flows for your project but found them intimidating? Say no more 😜

I just released a small library to easily implement and use causal-flows:

github.com/adrianjav/ca...

Interested in estimating posterior predictives in Bayesian inference? Really want to know if your approximate inference "is working"?
Come to our poster at the NeurIPS BDU workshop on Saturday - see TL;DR below.

many of the recent successes in #AI #ML are due to #structured low-rank representations!

but...What's the connection between #lowrank adapters, #tensor networks, #polynomials and #circuits?

join our #AAAI25 workshop to know the answer!

and 2 more days to submit!
👇👇👇
april-tools.github.io/colorai/