DeepProbLog
deepproblog-d0fcce59·2 events·first seen 2d agoAliases: DeepProbLog
Co-occurring entities
More like this (12)
Recent events (2)
DeepSWIP: Counterfactual reasoning for neural probabilistic logic programs via quotient-WMC
DeepSWIP introduces a single-world counterfactual semantics for DeepProbLog, enabling causal inference over neurosymbolic programs that combine neural perception with probabilistic logic. The approach uses neural materialization to reduce neural predicates to standard ProbLog choices, then applies Single World Intervention Programs (SWIPs) and weighted model counting to compute exact counterfactuals from a single transformed program. Experiments on MPI3D validate the method against a DeepTwin construction across 12,000 queries and show a 2.14× inference speedup, while a SUMO HOV experiment demonstrates that neural calibration degradation biases plug-in causal estimates and that a correctly scoped AIPW estimator removes most first-order bias.
NeSyCat Torch: Differentiable tensor framework unifying neurosymbolic semantics via monadic abstraction
NeSyCat Torch extends the NeSyCat/ULLER neurosymbolic framework with neural network support for predicates and functions, implemented via probabilistic programming and tensor backends (HaskTorch, JAX, PyTorch). The key technical contribution is a lazy log-tensor monad over the log-semiring enabling numerically stable, differentiable training, alongside a batch monad for efficient batched inference. On MNIST addition benchmarks, the implementations outperform LTN and DeepProbLog in speed and accuracy while remaining within a uniform categorical framework that generalizes across first-order neurosymbolic approaches. The work positions itself as a unifying foundation for classical, fuzzy, probabilistic, and neural truth semantics.