1,905 research outputs found
Exploring Causal Learning through Graph Neural Networks: An In-depth Review
In machine learning, exploring data correlations to predict outcomes is a
fundamental task. Recognizing causal relationships embedded within data is
pivotal for a comprehensive understanding of system dynamics, the significance
of which is paramount in data-driven decision-making processes. Beyond
traditional methods, there has been a surge in the use of graph neural networks
(GNNs) for causal learning, given their capabilities as universal data
approximators. Thus, a thorough review of the advancements in causal learning
using GNNs is both relevant and timely. To structure this review, we introduce
a novel taxonomy that encompasses various state-of-the-art GNN methods employed
in studying causality. GNNs are further categorized based on their applications
in the causality domain. We further provide an exhaustive compilation of
datasets integral to causal learning with GNNs to serve as a resource for
practical study. This review also touches upon the application of causal
learning across diverse sectors. We conclude the review with insights into
potential challenges and promising avenues for future exploration in this
rapidly evolving field of machine learning
Neural Crystals
We face up to the challenge of explainability in Multimodal Artificial
Intelligence (MMAI). At the nexus of neuroscience-inspired and quantum
computing, interpretable and transparent spin-geometrical neural architectures
for early fusion of large-scale, heterogeneous, graph-structured data are
envisioned, harnessing recent evidence for relativistic quantum neural coding
of (co-)behavioral states in the self-organizing brain, under competitive,
multidimensional dynamics. The designs draw on a self-dual classical
description - via special Clifford-Lipschitz operations - of spinorial quantum
states within registers of at most 16 qubits for efficient encoding of
exponentially large neural structures. Formally 'trained', Lorentz neural
architectures with precisely one lateral layer of exclusively inhibitory
interneurons accounting for anti-modalities, as well as their co-architectures
with intra-layer connections are highlighted. The approach accommodates the
fusion of up to 16 time-invariant interconnected (anti-)modalities and the
crystallization of latent multidimensional patterns. Comprehensive insights are
expected to be gained through applications to Multimodal Big Data, under
diverse real-world scenarios.Comment: preprint revised; to appear In Proceedings of the IEEE International
Conference on Big Data 2023/ 3rd Workshop on Multimodal AI (MMAI 2023
A Survey on Explainable AI for 6G O-RAN: Architecture, Use Cases, Challenges and Research Directions
The recent O-RAN specifications promote the evolution of RAN architecture by
function disaggregation, adoption of open interfaces, and instantiation of a
hierarchical closed-loop control architecture managed by RAN Intelligent
Controllers (RICs) entities. This paves the road to novel data-driven network
management approaches based on programmable logic. Aided by Artificial
Intelligence (AI) and Machine Learning (ML), novel solutions targeting
traditionally unsolved RAN management issues can be devised. Nevertheless, the
adoption of such smart and autonomous systems is limited by the current
inability of human operators to understand the decision process of such AI/ML
solutions, affecting their trust in such novel tools. eXplainable AI (XAI) aims
at solving this issue, enabling human users to better understand and
effectively manage the emerging generation of artificially intelligent schemes,
reducing the human-to-machine barrier. In this survey, we provide a summary of
the XAI methods and metrics before studying their deployment over the O-RAN
Alliance RAN architecture along with its main building blocks. We then present
various use-cases and discuss the automation of XAI pipelines for O-RAN as well
as the underlying security aspects. We also review some projects/standards that
tackle this area. Finally, we identify different challenges and research
directions that may arise from the heavy adoption of AI/ML decision entities in
this context, focusing on how XAI can help to interpret, understand, and
improve trust in O-RAN operational networks.Comment: 33 pages, 13 figure
Analysis of modern methods of intelligent data processing in network systems
With the growth of internet of things and cloud computing, the volume of data generated by network systems is massive and growing exponentially. Effective analysis of this data is crucial for various applications including anomaly detection, traffic engineering and predictive maintenance. This paper analyses modern methods used for intelligent processing of networked system data. State-of-the-art techniques such as deep learning, ensemble modeling, feature engineering and distributed computing are surveyed. Both supervised and unsupervised techniques are evaluated on real network datasets. The objective is to identify approaches that can process data from network systems in a scalable, online and intelligent manner
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