496 research outputs found
Linguistic Geometries for Unsupervised Dimensionality Reduction
Text documents are complex high dimensional objects. To effectively visualize
such data it is important to reduce its dimensionality and visualize the low
dimensional embedding as a 2-D or 3-D scatter plot. In this paper we explore
dimensionality reduction methods that draw upon domain knowledge in order to
achieve a better low dimensional embedding and visualization of documents. We
consider the use of geometries specified manually by an expert, geometries
derived automatically from corpus statistics, and geometries computed from
linguistic resources.Comment: 13 pages, 15 figure
Self-Organizing Word Map for Context-Based Document Classification
In this paper, a novel SOM-based system for document organization is presented. The purpose of the system is the classification of a document collection in terms of document content. The system possesses a two-level hybrid connectionist architecture that comprises (i) an automatically created word map using a SOM, which functions as a feature extraction module and (ii) a supervised MLP-based classifier, which provides the final classification result. The experiments, which have been performed on Modern Greek text documents, indicate that the proposed system separates effectively the different types of text
Domain knowledge, uncertainty, and parameter constraints
Ph.D.Committee Chair: Guy Lebanon; Committee Member: Alex Shapiro; Committee Member: Alexander Gray; Committee Member: Chin-Hui Lee; Committee Member: Hongyuan Zh
Neural Embeddings of Graphs in Hyperbolic Space
Neural embeddings have been used with great success in Natural Language
Processing (NLP). They provide compact representations that encapsulate word
similarity and attain state-of-the-art performance in a range of linguistic
tasks. The success of neural embeddings has prompted significant amounts of
research into applications in domains other than language. One such domain is
graph-structured data, where embeddings of vertices can be learned that
encapsulate vertex similarity and improve performance on tasks including edge
prediction and vertex labelling. For both NLP and graph based tasks, embeddings
have been learned in high-dimensional Euclidean spaces. However, recent work
has shown that the appropriate isometric space for embedding complex networks
is not the flat Euclidean space, but negatively curved, hyperbolic space. We
present a new concept that exploits these recent insights and propose learning
neural embeddings of graphs in hyperbolic space. We provide experimental
evidence that embedding graphs in their natural geometry significantly improves
performance on downstream tasks for several real-world public datasets.Comment: 7 pages, 5 figure
Self-Organizing Word Map for Context-Based Document Classification
In this paper, a novel SOM-based system for document organization is presented. The purpose of the system is the classification of a document collection in terms of document content. The system possesses a two-level hybrid connectionist architecture that comprises (i) an automatically created word map using a SOM, which functions as a feature extraction module and (ii) a supervised MLP-based classifier, which provides the final classification result. The experiments, which have been performed on Modern Greek text documents, indicate that the proposed system separates effectively the different types of text
Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey
Major assumptions in computational intelligence and machine learning consist of the availability of a historical dataset for model development, and that the resulting model will, to some extent, handle similar instances during its online operation. However, in many real world applications, these assumptions may not hold as the amount of previously available data may be insufficient to represent the underlying system, and the environment and the system may change over time. As the amount of data increases, it is no longer feasible to process data efficiently using iterative algorithms, which typically require multiple passes over the same portions of data. Evolving modeling from data streams has emerged as a framework to address these issues properly by self-adaptation, single-pass learning steps and evolution as well as contraction of model components on demand and on the fly. This survey focuses on evolving fuzzy rule-based models and neuro-fuzzy networks for clustering, classification and regression and system identification in online, real-time environments where learning and model development should be performed incrementally. (C) 2019 Published by Elsevier Inc.Igor Škrjanc, Jose Antonio Iglesias and Araceli Sanchis would like to thank to the Chair of Excellence of Universidad Carlos III de Madrid, and the Bank of Santander Program for their support. Igor Škrjanc is grateful to Slovenian Research Agency with the research program P2-0219, Modeling, simulation and control. Daniel Leite acknowledges the Minas Gerais Foundation for Research and Development (FAPEMIG), process APQ-03384-18. Igor Škrjanc and Edwin Lughofer acknowledges the support by the ”LCM — K2 Center for Symbiotic Mechatronics” within the framework of the Austrian COMET-K2 program. Fernando Gomide is grateful to the Brazilian National Council for Scientific and Technological Development (CNPq) for grant
305906/2014-3
Newly Formed Cities: an AI Curation
Art curatorial processes are characterized by the presentation of a
collection of artworks in a knowledgeable way. Machine processes are
characterized by their capacity to manage and analyze large amounts of data.
This paper envisages machine curation and audience interaction as a means to
explore the implications of contemporary AI models for the curatorial world.
This project was developed for the occasion of the 2023 Helsinki Art Biennial,
entitled New Directions May Emerge. We use the Helsinki Art Museum (HAM)
collection to re-imagine the city of Helsinki through the lens of machine
perception. We use visual-textual models to place artworks currently hosted
inside the museum in outdoor public spaces of the city, assigning fictional
coordinates based on similarity scores. Synthetic 360{\deg} art panoramas are
generated using diffusion-based models to propose a machinic visual style
guided by the artworks. The result of this project will be virtually presented
as a web-based installation, where such a re-contextualization allows the
navigation of an alternative version of the city while exploring its artistic
heritage. Finally, we discuss our contributions to machine curation and the
ethical implications that such a process entails. The web-based installation is
available at this link: http://newlyformedcity.com/
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