The AI community is increasingly putting its attention towards combining symbolic and
neural approaches, as it is often argued that the strengths and weaknesses of these …

Diffusion models have achieved remarkable success in generating high-quality diverse and
creative images. However in text-based image generation they often struggle to accurately …

Combining symbolic and neural approaches has gained considerable attention in the AI
community, as it is often argued that the strengths and weaknesses of these approaches are …

Anomaly detection (AD) is an important task in a broad range of domains. A popular choice
for AD are Deep Support Vector Data Description models. When learning such models …

Extensive research on formal verification of machine learning systems indicates that
learning from data alone often fails to capture underlying background knowledge such as …

This article presents a comprehensive approach to designing and optimizing a hierarchical
rule-base reduction-based adaptive-network-based fuzzy inference system (ANFIS) for …

Neurosymbolic AI aims to integrate deep learning with symbolic AI. This integration has
many promises, such as decreasing the amount of data required to train a neural network …

Recent direct preference alignment algorithms (DPA), such as DPO, have shown great
promise in aligning large language models to human preferences. While this has motivated …

Deep learning is typically performed by learning a neural network solely from data in the
form of input–output pairs ignoring available domain knowledge. In this work, the Constraint …

Deep learning models have repeatedly shown their strengths in various application
domains. However, their predictions often struggle to meet background knowledge …