David Debot
@daviddebot.bsky.social
PhD student @dtai-kuleuven.bsky.social in neurosymbolic AI and concept-based learning https://daviddebot.github.io/
🚨 Can we trust that the “concepts” in concept-based models actually mean what we think they mean? Our new work, Prototype-Grounded Concept Models (PGCMs), makes concept alignment directly inspectable and correctable. #ICML26 Paper: arxiv.org/abs/2604.16076 🧵 (1/5)
Our accepted ICML paper on prototype-based Concept Bottleneck Models is now on arXiv! We introduce Prototype-Grounded Concept Models (PGCMs), enabling verifiable concept alignment through interpretable visual prototypes 🔍🧠. Check it out at: arxiv.org/abs/2604.16076
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.
AIhub.org@aihub.org · 6mo ago
1/5 Tomorrow I’ll talk about the 𝐩𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐬𝐭𝐢𝐜 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐬𝐞𝐦𝐚𝐧𝐭𝐢𝐜𝐬 𝐨𝐟 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐛𝐥𝐞 𝐩𝐫𝐨𝐯𝐢𝐧𝐠 at #NeurIPS San Diego (poster #614 11am). 📃 openreview.net/pdf?id=rEUbD... 📺 www.youtube.com/watch?v=sOTX...
openreview.net
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!
antonio vergari ⚔️ short-circuiting@nolovedeeplearning.bsky.social · last yr.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/
We developed a library to make logical reasoning embarrasingly 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!
If you're at #AAAI2025, come check out our demo on neurosymbolic reinforcement learning with probabilistic logic shields 🤖 Tomorrow (Sat, March 1) from 12:30–2:30 PM during the poster session 💻
David Debot@daviddebot.bsky.social · last yr.🚀 Do you care about safe AI? Do you want RL agents that are both smart & trustworthy? At #AAAI2025, we present our demo for neurosymbolic RL—combining deep learning with probabilistic logic shields for safer, interpretable AI in complex environments. 🏰🔥 🧵👇 (1/8)
We all know backpropagation can calculate gradients, but it can do much more than that! Come to my #AAAI2025 oral tomorrow (11:45, Room 119B) to learn more.
🔥 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/... 🧵⬇️
🚀 Do you care about safe AI? Do you want RL agents that are both smart & trustworthy? At #AAAI2025, we present our demo for neurosymbolic RL—combining deep learning with probabilistic logic shields for safer, interpretable AI in complex environments. 🏰🔥 🧵👇 (1/8)
🚨 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)
