5 papers
How to Approximate Inference with Subtractive Mixture Models
Lena Zellinger, Nicola Branchini, Lennert De Smet +3
Classical mixture models (MMs) are widely used tractable proposals for approximate inference settings such as variational inference (VI) and importance sampling (IS). Recently, mix…
The DeepLog Neurosymbolic Machine
Vincent Derkinderen, Robin Manhaeve, Rik Adriaensen +4
We contribute a theoretical and operational framework for neurosymbolic AI called DeepLog. DeepLog introduces building blocks and primitives for neurosymbolic AI that make abstract…
Defining neurosymbolic AI
Lennert De Smet, Luc De Raedt
Neurosymbolic AI focuses on integrating learning and reasoning, in particular, on unifying logical and neural representations. Despite the existence of an alphabet soup of neurosym…
Relational Neurosymbolic Markov Models
Lennert De Smet, Gabriele Venturato, Luc De Raedt +1
Sequential problems are ubiquitous in AI, such as in reinforcement learning or natural language processing. State-of-the-art deep sequential models, like transformers, excel in the…
A Fast Convoluted Story: Scaling Probabilistic Inference for Integer Arithmetic
Lennert De Smet, Pedro Zuidberg Dos Martires
As illustrated by the success of integer linear programming, linear integer arithmetic is a powerful tool for modelling combinatorial problems. Furthermore, the probabilistic exten…