Going beyond xai: A systematic survey for explanation-guided learning
As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing
DNNs become more complex and diverse, ranging from improving a conventional model …
DNNs become more complex and diverse, ranging from improving a conventional model …
Neuro-symbolic artificial intelligence: The state of the art
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 …
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …
Neurosymbolic AI: the 3rd wave
Abstract Current advances in Artificial Intelligence (AI) and Machine Learning have achieved
unprecedented impact across research communities and industry. Nevertheless, concerns …
unprecedented impact across research communities and industry. Nevertheless, concerns …
Neuro-symbolic speech understanding in aircraft maintenance metaverse
A Siyaev, GS Jo - Ieee Access, 2021 - ieeexplore.ieee.org
In the emerging world of metaverses, it is essential for speech communication systems to be
aware of context to interact with virtual assets in the 3D world. This paper proposes the …
aware of context to interact with virtual assets in the 3D world. This paper proposes the …
Neuro-symbolic approaches in artificial intelligence
Neuro-symbolic artificial intelligence refers to a field of research and applications that
combines machine learning methods based on artificial neural networks, such as deep …
combines machine learning methods based on artificial neural networks, such as deep …
A review of some techniques for inclusion of domain-knowledge into deep neural networks
We present a survey of ways in which existing scientific knowledge are included when
constructing models with neural networks. The inclusion of domain-knowledge is of special …
constructing models with neural networks. The inclusion of domain-knowledge is of special …
Modular design patterns for hybrid learning and reasoning systems: a taxonomy, patterns and use cases
The unification of statistical (data-driven) and symbolic (knowledge-driven) methods is
widely recognized as one of the key challenges of modern AI. Recent years have seen a …
widely recognized as one of the key challenges of modern AI. Recent years have seen a …
Inductive logic programming at 30: a new introduction
A Cropper, S Dumančić - Journal of Artificial Intelligence Research, 2022 - jair.org
Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce
a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we …
a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we …
Deepstochlog: Neural stochastic logic programming
Recent advances in neural-symbolic learning, such as DeepProbLog, extend probabilistic
logic programs with neural predicates. Like graphical models, these probabilistic logic …
logic programs with neural predicates. Like graphical models, these probabilistic logic …
Neural-logic human-object interaction detection
The interaction decoder utilized in prevalent Transformer-based HOI detectors typically
accepts pre-composed human-object pairs as inputs. Though achieving remarkable …
accepts pre-composed human-object pairs as inputs. Though achieving remarkable …