2,700 research outputs found
Conceptual Representations for Computational Concept Creation
Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this article, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial and connectionist representations. The other is between descriptive and procedural representations. Additionally, conceptual representations used in particular creative domains, such as language, music, image and emotion, are reviewed separately. For every representation reviewed, we cover the inference it affords, the computational means of building it, and its application in concept creation.Peer reviewe
Long Text Generation via Adversarial Training with Leaked Information
Automatically generating coherent and semantically meaningful text has many
applications in machine translation, dialogue systems, image captioning, etc.
Recently, by combining with policy gradient, Generative Adversarial Nets (GAN)
that use a discriminative model to guide the training of the generative model
as a reinforcement learning policy has shown promising results in text
generation. However, the scalar guiding signal is only available after the
entire text has been generated and lacks intermediate information about text
structure during the generative process. As such, it limits its success when
the length of the generated text samples is long (more than 20 words). In this
paper, we propose a new framework, called LeakGAN, to address the problem for
long text generation. We allow the discriminative net to leak its own
high-level extracted features to the generative net to further help the
guidance. The generator incorporates such informative signals into all
generation steps through an additional Manager module, which takes the
extracted features of current generated words and outputs a latent vector to
guide the Worker module for next-word generation. Our extensive experiments on
synthetic data and various real-world tasks with Turing test demonstrate that
LeakGAN is highly effective in long text generation and also improves the
performance in short text generation scenarios. More importantly, without any
supervision, LeakGAN would be able to implicitly learn sentence structures only
through the interaction between Manager and Worker.Comment: 14 pages, AAAI 201
Conceiving God: Literal and Figurative Prompt for a More Tectonic Distinction
John Sanders’ Theology in the Flesh, the first comprehensive overview of the toolkit that contemporary cognitive linguistics offers for theological appropriation, despite its remarkable success, gives rather minimal attention to blending theory, one of the discipline’s most formidable tools. This paper draws on blending theory to offer an alternative to Sanders’ chapter on conceiving God. Central to the proposal is claim that God-talk, like many of the advances in science, technology, and art, entails a kind of tectonic understanding and conceptual mapping that is neither literal nor figurative
Application of machine learning to predict quality of Portuguese wine based on sensory preferences
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceTechnology has been broadly used in the wine industry, from vineyards to purchases, improving means or understanding customers' preferences. Numerous companies are using machine learning solutions to leverage their business. Henceforth, the sensory properties of wines constitute a significant element to determine wine quality, that combined with the accuracy of predictive models attained by classification methods, could be helpful to support winemakers enhance their outcomes. This research proposes a supervised machine learning approach to predict the quality of Portuguese wines based on sensory characteristics such as acidity, intensity, sweetness, and tannin. Additionally, this study includes red and white wines, implements, and compare the effectiveness of three classification algorithms. The conclusions promote understanding the importance of the sensory characteristics that influence the wine quality throughout customers' perception.Tecnologia vem sendo amplamente empregada na indústria do vinho. Desde melhoria em processos de cultivo à compreensão de mercado por meio da análise de preferência de consumidores. Tendo em vista à atual dinâmica dos mercados, empresas estão gradualmente a considerar soluções que implementam conceitos de aprendizagem de máquina e tragam diferencial competitivo para potencializar o negócio. Doravante, propriedades sensoriais são importantes elementos para determinação da qualidade do vinho, que aliado à precisão obtida por modelos preditivos podem auxiliar produtores de vinho a melhorar produtos e resultados. O presente estudo propõe a elaboração de modelos de aprendizado supervisionado, baseado em algoritmos de classificação a fim de prever qualidade de vinhos portugueses a partir de dados sensoriais detetados por consumidores como acidez, intensidade, açúcar e taninos. A pesquisa inclui vinhos tintos e brancos; implementa e compara a efetividade de três algoritmos de classificação. Não obstante, o estudo permite compreender como dados sensoriais fornecidos por consumidores podem determinar a qualidade de vinhos, bem como perceber quais características contribuem no processo de avaliação
Winning, Losing, and Changing the Rules: The Rhetoric of Poetry Contests and Competition
This dissertation attempts to trace the shifting relationship between the fields of Rhetoric and Poetry in Western culture by focusing on poetry contests and competitions during several different historical eras. In order to examine how the distinction between the two fields is contingent on a variety of local factors, this study makes use of research in contemporary cognitive neuroscience, particularly work in categorization and cognitive linguistics, to emphasize the provisional nature of conceptual thought; that is, on the type of mental activity that gives rise to conceptualizations such as “Rhetoric” and “Poetry.” The final portions of the research attempt to use some modeling techniques derived from cognitive linguistics as invention strategies for producing stylistically idiosyncratic academic knowledge, and for examining the relationship between the stylistic markers we associate with each of the two aforementioned fields
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