62 citations · 85 across the 7 of their papers we have counts for
8 papers
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural n…
Multi-Objective Constraint Inference using Inverse reinforcement learning
Syed Ihtesham Hussain Shah, Floris den Hengst, Aneta Lisowska +1
Constraint inference is widely considered essential to align reinforcement learning agents with safety boundaries and operational guidelines by observing expert demonstrations. How…
Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes
Majid Mohammadi, Krikamol Muandet, Ilaria Tiddi +2
Shapley values are widely recognized as a principled method for attributing importance to input features in machine learning. However, the exact computation of Shapley values scale…
A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
Emile van Krieken, Thiviyan Thanapalasingam, Jakub M. Tomczak +2
We study the problem of combining neural networks with symbolic reasoning. Recently introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as DeepProbLog, perfo…
Local Explanations for Clinical Search Engine results
Edeline Contempré, Zoltán Szlávik, Majid Mohammadi +3
Health care professionals rely on treatment search engines to efficiently find adequate clinical trials and early access programs for their patients. However, doctors lose trust in…
Storchastic: A Framework for General Stochastic Automatic Differentiation
Emile van Krieken, Jakub M. Tomczak, Annette ten Teije
Modelers use automatic differentiation (AD) of computation graphs to implement complex Deep Learning models without defining gradient computations. Stochastic AD extends AD to stoc…
Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases
Michael van Bekkum, Maaike de Boer, Frank van Harmelen +2
The unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognised as one of the key challenges of modern AI. Recent years have seen large nu…
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
Frank van Harmelen, Annette ten Teije
We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from kno…