1,720,979 research outputs found
Exploring the spatial reasoning ability of neural models in human IQ tests
No full textAlthough neural models have performed impressively well on various tasks such as image recognition and question answering, their reasoning ability has been measured in only few studies. In this work, we focus on spatial reasoning and explore the spatial understanding of neural models. First, we describe the following two spatial reasoning IQ tests: rotation and shape composition. Using well-defined rules, we constructed datasets that consist of various complexity levels. We designed a variety of experiments in terms of generalization, and evaluated six different baseline models on the newly generated datasets. We provide an analysis of the results and factors that affect the generalization abilities of models. Also, we analyze how neural models solve spatial reasoning tests with visual aids. We hope that our work can encourage further research into human-level spatial reasoning and provide a new direction for future work. (C) 2021 Elsevier Ltd. All rights reserved.
그래프 표현의 정확한 학습을 위한 연구
No full text학위논문(석사) - 한국과학기술원 : 김재철AI대학원, 2022.2,[iv, 56 pp. :]While machine learning algorithms and models for graph-structured data have been actively studied, the problems of handling new entities (nodes) and representing entire graphs remain challenging. My thesis focuses on how to tackle such problematic issues on a graph. In other words, we develop methods for representing unseen entities and entire graphs more accurately, summarized into two folds:
For the problem of representing unseen entities, we first introduce a realistic task of out-of-graph link prediction that aims to predict missing links for unseen entities. Then, to tackle this, we propose a transductive meta-learning framework that makes it possible to simulate the unseen during training. We validate our method on benchmark datasets for knowledge graph completion and drug-drug interaction prediction. The experimental results show that our method significantly outperforms existing baselines on the out-of-graph link prediction task, due to its effectiveness in accurately representing unseen entities.
For the problem of representing entire graphs, we aim to embed different graphs into distinct vectors. To do so, we consider the graph encoding problem as a multiset encoding problem, which allows for possibly repeating elements, since a graph may have redundant nodes. Then, over the multiset encoding scheme, we propose a graph multiset transformer that captures interaction among nodes, while reducing the size of the given graph, to obtain a compact yet entire graph representation. We theoretically prove that our method is as powerful as the Weisfeiler-Lehman graph isomorphism test, but also empirically show that it outperforms baselines on graph classification, reconstruction, and generation tasks.
We believe both of our approaches contribute to the optimal goal of accurate learning of real-world graphs, often evolving with unseen nodes and having a large number of nodes to capture at once.한국과학기술원 :김재철AI대학원
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Full text linkWe 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
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