Conceptual Representations for Computational Concept Creation

Xiao, Ping; Toivonen, Hannu; Gross, Oskar; Cardoso, Amı́lcar; Correia, João; Machado, Penousal; Martins, Pedro; Oliveira, Hugo Gonçalo; Sharma, Rahul; Pinto, Alexandre Miguel; Díaz, Alberto; Francisco, Virginia; Gervás, Pablo; Hervás, Raquel; León, Carlos; Forth, Jamie; Purver, Matthew; Wiggins, Geraínt A.; Miljković, Dragana; Podpečan, Vid; Pollak, Senja; Kralj, Jan; Žnidaršič, Martin; Bohanec, Marko; Lavrač, Nada; Urbančič, Tanja; Velde, Frank van der; Battersby, Stuart

doi:10.1145/3186729

Cited by 14 publications

(8 citation statements)

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“…Research in NLP addresses computational learning, comprehension and processing of human-understandable language, and its components. An interesting article published a work about the representation of abstract, and concrete concepts in daily written language using a text-based multimodal architecture of NLP [3]. Other than NLP, semantic networks are also used to study semantic similarity among abstract, and concrete nouns (of Greek, and English) [25] with the aid of network-based Distributed Semantic Model [26].…”

Section: Related Workmentioning

confidence: 99%

“…Each theory has a way to represent concrete concepts through perception (or recognition), action, emotion, and introspection, but the notion of abstract concepts is debatable [1]. Abstract concepts are largely studied in psychology, and there are attempts to study them by the computational linguistics research community for Natural Language Processing (NLP) [3]. However, the representation aspect of abstract concepts is still a challenge.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts

et al. 2020

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The term concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. Concepts are also studied in the computational domain through their symbolic, distributed and hybrid representations. The majority of these approaches focused on addressing concrete concepts notion, but the view of the abstract concept is rarely explored. Moreover, most computational approaches have a predefined structure or configurations. The proposed method, Regulated Activation Network (RAN), has an evolving topology and learns representations of abstract concepts by exploiting the geometrical view of concepts, without supervision. In the article, first, a Toy-data problem was used to demonstrate the RANs modeling. Secondly, we demonstrate the liberty of concept identifier choice in RANs modeling and deep hierarchy generation using the IRIS dataset. Thirdly, data from the IoT’s human activity recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with eight UCI benchmarks and the comparisons with five Machine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RANs hypothesis of abstract concept representation. The experiments demonstrate the RANs ability to simulate psychological processes (like concept creation and learning) and carry out effective classification irrespective of training data size.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts

et al. 2020

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“…Moreover, user fatigue limits the number of search generations [ 3 ]. Genotype-to-phenotype mapping, also known as the representation problem in EArt [ 20 ], greatly influences user fatigue, user satisfaction, and the resulting artifact [ 2 , 10 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Improving Deep Interactive Evolution with a Style-Based Generator for Artistic Expression and Creative Exploration

Tejeda-Ocampo

López-Cuevas

Terashima-Marín

2020

Entropy

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Deep interactive evolution (DeepIE) combines the capacity of interactive evolutionary computation (IEC) to capture a user’s preference with the domain-specific robustness of a trained generative adversarial network (GAN) generator, allowing the user to control the GAN output through evolutionary exploration of the latent space. However, the traditional GAN latent space presents feature entanglement, which limits the practicability of possible applications of DeepIE. In this paper, we implement DeepIE within a style-based generator from a StyleGAN model trained on the WikiArt dataset and propose StyleIE, a variation of DeepIE that takes advantage of the secondary disentangled latent space in the style-based generator. We performed two AB/BA crossover user tests that compared the performance of DeepIE against StyleIE for art generation. Self-rated evaluations of the performance were collected through a questionnaire. Findings from the tests suggest that StyleIE and DeepIE perform equally in tasks with open-ended goals with relaxed constraints, but StyleIE performs better in close-ended and more constrained tasks.

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“…An individual concept is referred to as a concrete concept (or feature) whereas a generalized form of a set of concepts (or features) can be perceived as an abstract concept. There are several conceptual representation theoritical frameworks [1] like modality-specific, localist-distributed, experience-dependent [2].…”

Section: Introductionmentioning

confidence: 99%

Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts

Sharma¹,

Ribeiro²,

Pinto³

et al. 2020

Preprint

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The term Concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. The Concepts are also studied computationally through their symbolic, distributed and hybrid representations. The majority of these approaches focused on addressing concrete concepts notion, but the view of the abstract concept is rarely explored. Moreover, most computational approaches have a predefined structure or configurations. The proposed method, Regulated Activation Network (RAN), has an evolving topology and learns representations of Abstract Concepts by exploiting the geometrical view of Concepts, without supervision. In the article, the IRIS data was used to demonstrate: the RAN's modeling; flexibility in concept identifier choice; and deep hierarchy generation. Data from IoT's Human Activity Recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with 8 UCI benchmarks and the comparisons with 5 Machine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RAN's hypothesis of abstract concept representation. The experiments demonstrate the RANs ability to simulate psychological processes (like concept creation and learning) and carry out effective classification irrespective of training data size.

show abstract

Conceptual Representations for Computational Concept Creation

Cited by 14 publications

References 140 publications

Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts

Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts

Improving Deep Interactive Evolution with a Style-Based Generator for Artistic Expression and Creative Exploration

Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts

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