A Survey and Formal Analyses on Sequence Learning Methodologies and Deep Neural Networks

Wang, Yingxu; Leung, Henry; Gavrilova, Marina L.; Zatarain, Omar A.; Graves, Daniel; Lu, Jianhua; Howard, Newton; Sheu, Phillip C.‐Y.; Patel, Shushma

doi:10.1109/icci-cc.2018.8482022

Cited by 11 publications

(4 citation statements)

References 37 publications

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“…Learning (WANG et al, 2018). focused on the application in logistics centers, followed by a research of the Paraconsistent Annotated Logic Eτ for application Artificial Intelligence.…”

Section: Methodsmentioning

confidence: 99%

Study of Pann Components in Image Treatment for Medical Diagnostic Decision-Making

Lima

Abe²,

Martı́nez³

et al. 2021

RFSA

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“…Learning (WANG et al, 2018). focused on the application in logistics centers, followed by a research of the Paraconsistent Annotated Logic Eτ for application Artificial Intelligence.…”

Section: Methodsmentioning

confidence: 99%

Study of Pann Components in Image Treatment for Medical Diagnostic Decision-Making

Lima

Abe²,

Martı́nez³

et al. 2021

RFSA

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show abstract

“…According to corollary (1), the philosophy of current machine learning would be questionable, because it is implemented by two phases known as training (domain calibration) and reflexive regression [37]. The former is implemented by large-scale data-driven regressions; while the latter is a reflexive classification.…”

Section: Corollarymentioning

confidence: 99%

“…However, the latter is constrained by the pretrained domain and norms of samples as special solutions within a particular scope of sample data. In generic mathematical theory, there are often infinitive special solutions for the entire domain of a category of targets such as facial images and road conditions [37][38][39][40]. Therefore, there are no generic solutions yet discovered for all-purpose image recognition.…”

Section: Corollarymentioning

confidence: 99%

On the philosophical, cognitive and mathematical foundations of symbiotic autonomous systems

Wang

Karray

Plataniotis

et al. 2021

Phil. Trans. R. Soc. A.

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Symbiotic autonomous systems (SAS) are advanced intelligent and cognitive systems that exhibit autonomous collective intelligence enabled by coherent symbiosis of human–machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered advanced general-AI technologies that either function without human intervention or synergize humans and intelligent machines in coherent cognitive systems. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences. SAS are characterized by the composition of autonomous and symbiotic systems that adopt bio-brain-social-inspired and heterogeneously synergized structures and autonomous behaviours. This paper explores the cognitive and mathematical foundations of SAS. The challenges to seamless human–machine interactions in a hybrid environment are addressed. SAS-based collective intelligence is explored in order to augment human capability by autonomous machine intelligence towards the next generation of general AI, cognitive computers, and trustworthy mission-critical intelligent systems. Emerging paradigms and engineering applications of SAS are elaborated via autonomous knowledge learning systems that symbiotically work between humans and cognitive robots. This article is part of the theme issue ‘Towards symbiotic autonomous systems'.

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“…The latest advances have paved a new way for cognitive computing and autonomous software generation, which put the house before the cart for the software and AI industries [51], [56]. The keynote will demonstrate disruptive technologies encompassing autonomous systems, cognitive robots, real-time autonomous learning, and trustworthy systems for symbiotic humanmachine applications [107], [108], [109], [110], [111], [112], [113], [114], [115], [116], [117], [118], [119], [120], [121], [122], [123], [124], [125], [126]. encompassing artificial intelligence, autonomous systems, theoretical computer science, neurology, engineering mathematics, software engineering, cognitive science, information science, and computational linguistics.…”

mentioning

confidence: 99%

Breakthroughs in Cognitive Robots and AI Programming Underpinned by Discoveries in Intelligence Science and Software Science

Wang

2023

2023 International Conference on Frontiers of Robotics and Software Engineering (FRSE)

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enabled the synergy of classical pre-programmed software engineering and pre-trained AI to an unprecedented platform of training-free machine intelligence generation mimicking human brains and Brain-Inspired Systems (BIS) [67], [68], [69], [70], [71], [72], [73], [74]. It is discovered in IS and CC in general, as well as in CR and SE in particular, that the target entities across these contemporary fields had already out of the denotational power of the classic mathematical domains of real (R) and binary (B) numbers [76]. This leads to the latest discovery that the basic unit of human knowledge as a hyperstructure [50] is a binary relation (bir) [74].

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A Survey and Formal Analyses on Sequence Learning Methodologies and Deep Neural Networks

Cited by 11 publications

References 37 publications

Study of Pann Components in Image Treatment for Medical Diagnostic Decision-Making

Study of Pann Components in Image Treatment for Medical Diagnostic Decision-Making

On the philosophical, cognitive and mathematical foundations of symbiotic autonomous systems

Breakthroughs in Cognitive Robots and AI Programming Underpinned by Discoveries in Intelligence Science and Software Science

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