2 citations · 4 across the 7 of their papers we have counts for
7 papers
Towards Zero-Shot Task Transfer with Neurosymbolic World Models
Isidoro Tamassia, Lennert De Smet, Giuseppe Marra
State-of-the-art model-based reinforcement learning methods learn neural world models that allow policy improvement by planning in a latent space, without assumptions on the struct…
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…
EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logic
Victor Verreet, Lennert De Smet, Luc De Raedt +1
Neural probabilistic logic systems follow the neuro-symbolic (NeSy) paradigm by combining the perceptive and learning capabilities of neural networks with the robustness of probabi…
Differentiable Sampling of Categorical Distributions Using the CatLog-Derivative Trick
Lennert De Smet, Emanuele Sansone, Pedro Zuidberg Dos Martires
Categorical random variables can faithfully represent the discrete and uncertain aspects of data as part of a discrete latent variable model. Learning in such models necessitates t…