Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …

Nowadays, robots are dominating the manufacturing, entertainment, and healthcare
industries. Robot vision aims to equip robots with the capabilities to discover information …

We introduce a novel model-agnostic system that explains the behavior of complex models
with high-precision rules called anchors, representing local," sufficient" conditions for …

Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …

Artificial Neural Networks are powerful function approximators capable of modelling
solutions to a wide variety of problems, both supervised and unsupervised. As their size and …

Abstract Knowledge graph reasoning, which aims at predicting missing facts through
reasoning with observed facts, is critical for many applications. Such a problem has been …

Statistical pattern recognition is a very active area of study andresearch, which has seen
many advances in recent years. New andemerging applications-such as data mining, web …

This new edition continues to serve as a comprehensive guide to modern and classical
methods of statistical computing. The book is comprised of four main parts spanning the …

Probabilistic logic programs are logic programs in which some of the facts are annotated
with probabilities. This paper investigates how classical inference and learning tasks known …

A multitude of different probabilistic programming languages exists today, all extending a
traditional programming language with primitives to support modeling of complex, structured …