Causal reasoning: Charting a revolutionary course for next-generation ai-native wireless networks
Despite the basic premise that next-generation wireless networks (eg, 6G) will be artificial
intelligence (AI)-native, to date, most existing efforts remain either qualitative or incremental …
intelligence (AI)-native, to date, most existing efforts remain either qualitative or incremental …
Explainable artificial intelligence for 6G: Improving trust between human and machine
W Guo - IEEE Communications Magazine, 2020 - ieeexplore.ieee.org
As 5G mobile networks are bringing about global societal benefits, the design phase for 6G
has started. Evolved 5G and 6G will need sophisticated AI to automate information delivery …
has started. Evolved 5G and 6G will need sophisticated AI to automate information delivery …
Artificial general intelligence (AGI)-native wireless systems: A journey beyond 6G
Building future wireless systems that support services like digital twins (DTs) is challenging
to achieve through advances to conventional technologies like meta-surfaces. While artificial …
to achieve through advances to conventional technologies like meta-surfaces. While artificial …
Causal deep learning
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
gcastle: A python toolbox for causal discovery
K Zhang, S Zhu, M Kalander, I Ng, J Ye, Z Chen… - arXiv preprint arXiv …, 2021 - arxiv.org
$\texttt {gCastle} $ is an end-to-end Python toolbox for causal structure learning. It provides
functionalities of generating data from either simulator or real-world dataset, learning causal …
functionalities of generating data from either simulator or real-world dataset, learning causal …
Evaluation methods and measures for causal learning algorithms
The convenient access to copious multifaceted data has encouraged machine learning
researchers to reconsider correlation-based learning and embrace the opportunity of …
researchers to reconsider correlation-based learning and embrace the opportunity of …
Deep causal learning: representation, discovery and inference
Causal learning has attracted much attention in recent years because causality reveals the
essential relationship between things and indicates how the world progresses. However …
essential relationship between things and indicates how the world progresses. However …
Theoretical impediments to machine learning with seven sparks from the causal revolution
J Pearl - arXiv preprint arXiv:1801.04016, 2018 - arxiv.org
Current machine learning systems operate, almost exclusively, in a statistical, or model-free
mode, which entails severe theoretical limits on their power and performance. Such systems …
mode, which entails severe theoretical limits on their power and performance. Such systems …
Causal discovery and injection for feed-forward neural networks
Neural networks have proven to be effective at solving a wide range of problems but it is
often unclear whether they learn any meaningful causal relationship: this poses a problem …
often unclear whether they learn any meaningful causal relationship: this poses a problem …
Explainable and robust artificial intelligence for trustworthy resource management in 6G networks
Artificial intelligence (AI) is expected to be an integral part of radio resource management
(RRM) in sixth-generation (6G) networks. However, the opaque nature of complex deep …
(RRM) in sixth-generation (6G) networks. However, the opaque nature of complex deep …