Building semantic hierarchies of formal concepts by deep cognitive machine learning

Valipour, Mehrdad; Wang, Yingxu

doi:10.1109/icci-cc.2017.8109729

Cited by 6 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the key breakthroughs of κC is autonomous knowledge learning enabled by Level-5 cognitive intelligence according to Figure 5 (Wang, 2022a), (Valipour and Wang, 2017). According to HIM, the abilities for knowledge learning and reasoning are an indispensable foundation for intelligence generation by κC.…”

Section: Paradigms Of κC For Autonomous Knowledge Learning and Machin...mentioning

confidence: 99%

“…In autonomous knowledge learning, κC adopts a relational neural cluster structure for knowledge representation in CKB as illustrated in Figure 4. The set of knowledge acquired by κC for the first 600+ frequently used concepts in English is shown in Figure 11 (Wang and Valipour, 2016;Valipour and Wang, 2017;. It only takes the κC less than a minute to quantitatively acquire such scope of knowledge represented by a network of quantitative semantic relations, which would normally require several months by traditional qualitative ontology-extraction in forms of non-quantitative or subjective knowledge graphs without adopting rigorous IM underpinned by concept algebra (Wang, 2010) and semantic algebra (Wang, 2013).…”

Section: Figurementioning

confidence: 99%

See 1 more Smart Citation

The odyssey to next-generation computers: cognitive computers (κC) inspired by the brain and powered by intelligent mathematics

Wang

Widrow

Hoare

et al. 2023

Front. Comput. Sci.

View full text Add to dashboard Cite

Cognitive computers (κC) are intelligent processors advanced from data and information processing to autonomous knowledge learning and intelligence generation. This work presents a retrospective and prospective review of the odyssey toward κC empowered by transdisciplinary basic research and engineering advances. A wide range of fundamental theories and innovative technologies for κC is explored, and a set of underpinning intelligent mathematics (IM) is created. The architectures of κC for cognitive computing and Autonomous Intelligence Generation (AIG) are designed as a brain-inspired cognitive engine. Applications of κC in autonomous AI (AAI) are demonstrated by pilot projects. This work reveals that AIG will no longer be a privilege restricted only to humans via the odyssey to κC toward training-free and self-inferencing computers.

show abstract

Section: Paradigms Of κC For Autonomous Knowledge Learning and Machin...mentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

The odyssey to next-generation computers: cognitive computers (κC) inspired by the brain and powered by intelligent mathematics

Wang

Widrow

Hoare

et al. 2023

Front. Comput. Sci.

View full text Add to dashboard Cite

show abstract

Section: Machine Learningmentioning

confidence: 99%

The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics

2022

View full text Add to dashboard Cite

Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of data from these recordings has only recently been facilitated by artificial intelligence methods. These have yet to be evaluated with respect to accuracy of different automation strategies and features. Here, we use a recently published machine learning framework to extract syllables from ten bird species ranging in their phylogenetic relatedness from 1 to 85 million years, to compare how phylogenetic relatedness influences accuracy. We also evaluate the utility of applying trained models to novel species. Our results indicate that model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa. However, we also find that the application of models trained on multiple distantly related species can improve the overall accuracy to levels near that of training and analyzing a model on the same species. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy.

show abstract

“…1996, Sprengel et al, 2016Zell, 1994;O'Mahony et al, 2020;LeCun et al, 2015;Alom et al, 2018). These methods have been used on a variety of topics including image processing, video segmentation, and speech recognition (Hemanth & Estrela, 2017;Valipour & Wang, 2017;Kamath et al, 2019). Although challenges remain with respect to scalability, computational efficiency, and how to handle depauperate data (Alom et al, 2018), deep learning is one of the most powerful analytical tools in the modern researcher's toolbox, particularly when human knowledge is lacking, or datasets are too large to be workable by traditional means.…”

Section: Machine Learningmentioning

confidence: 99%

“…Because ANNs can iteratively learn their own features, use large amounts of data, and are less constrained by assumptions about that data, they are extremely flexible and can handle many kinds of tasks [e.g., 36, 37, 38, 39, 40, 41, 42]. These methods have been used on a variety of topics including image processing, video segmentation, and speech recognition [43, 44, 45]. Although challenges remain with respect to scalability, computational efficiency, and how to handle depauperate data [42], deep learning is one of the most powerful analytical tools in the modern researcher’s toolbox, particularly when human knowledge is lacking, or datasets are too large to be workable by traditional means.…”

Section: Introductionmentioning

confidence: 99%

The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics

Provost

Yang

Carstens

2022

Preprint

View full text Add to dashboard Cite

Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness, but automatically extracting vocalization data from existing large databases has only recently gained traction and has yet to be evaluated with respect to accuracy of different approaches. Here, we use a recently-published machine learning framework to extract syllables from six bird species ranging in their phylogenetic relatedness from 1-85 million years, comparing how phylogenetic relatedness impacts accuracy as well as the utility of applying trained models to novel species. Model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa; however, using models trained on multiple distantly related species can recover the lost accuracy. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy.

show abstract

Building semantic hierarchies of formal concepts by deep cognitive machine learning

Cited by 6 publications

References 24 publications

The odyssey to next-generation computers: cognitive computers (κC) inspired by the brain and powered by intelligent mathematics

The odyssey to next-generation computers: cognitive computers (κC) inspired by the brain and powered by intelligent mathematics

The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics

The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics

Contact Info

Product

Resources

About