1,720,979 research outputs found
Deep learning in data scarcity scenarios
No full textThe availability of data serves as the fundamental pillar upon which deep learning models rely. The performance of such models is highly dependent on the quality of the data fed to train them. In a perfect scenario, the data would consist of input-output pairs that capture all possible aspects of the task that the deep learning model needs to solve. Devoid of this perfect data, these models would neither perform as effectively nor come into existence.
Nevertheless, the process of acquiring data can often evolve into a laborious and expensive undertaking, fraught with challenges and offering no assurance of data quality. Therefore, research has been directed toward circumventing this data-scarcity environment while still enabling the training of deep learning models.
In response to the challenge of data scarcity, researchers pioneered techniques aimed at training DL models robustly across diverse domains. Some of these techniques work by imparting models with the ability to generalize their learning from one domain to another, thereby improving their performance on diverse data distributions during inference. Others implement data manipulation, introducing diversity into training data and enhancing the model’s robustness. Another set of approaches exploits representation learning, enabling models to autonomously acquire meaningful features. However, many implementations have primarily focused on scenarios without a specific target domain, meaning the models are designed to generalize across all possible distributions. Although generalizing to many domains may sound like a perfect goal, typically, such domains retain high similarity with the source domains. Consequently, if a target domain were to have characteristics so unique that it barely resembles the source, then domain generalization may not perform as well as intended. This leads to a new question: What can be done to train a model for a specific target?
When training for a specific target domain, various methodologies are tailored to address this scenario, employing different techniques based on the available information. In certain cases, the information from the target domain might be limited to class descriptions existing within both the source and target domains. Approaches designed for this scenario, such as Zero-Shot Learning, utilize this description information to establish relationships between the source and target domains. Other methodologies, like in Domain Adaptation, seek to map the target distributions to a shared feature space in which the confusion between domains is maximized.
However, this approach is generally used when there are enough data samples from both domains. In the unsupervised scenario (UDA), some approaches exploit adversarial training to maximize the confusion between domains, as this procedure does not require labeled samples. However, in the extreme case where only a target sample is available (One-Shot UDA), the approach is to leverage data augmentation to generate more samples. Although this last scenario has not been thoroughly researched.
In this thesis, our primary focus is on addressing the aforementioned question. We achieve this by delineating various methodologies, each tailored to specific aspects of the problem at hand. These aspects include scenarios with an absence of available input-output pairs for training, such as in Zero-Shot Learning. In this scenario, we define a data augmentation approach to reduce bias towards the source domain. Additionally, we explore the extreme case where only one input without output (i.e., unannotated) exists, as seen in One-Shot UDA. In One-Shot UDA, we leverage data augmentation and style transfer to generate samples of the target domain. Through a diverse set of experiments, these novel methodologies have demonstrated their efficiency in tackling complex scenarios characterized by data scarcity
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Author Under Sail The Imagination of Jack London, 1893-1902
No full textIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Intro -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Introduction -- 1. Spirit Truth -- 2. From Absorption to Theatricality and Back Again -- 3. "I Will Build a New Present" -- 4. Sons as Authors -- 5. Fathers as Publishers -- 6. The Daughter as Author -- 7. Lovers as Authors -- 8. At Sea with the Family -- 9. Yellow News, Yellow Stories -- 10. The Return Home -- Notes -- Bibliography -- Index -- About Jay WilliamsIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
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