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Relevant bibliographies by topics / Canonical Representation Extraction

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Academic literature on the topic 'Canonical Representation Extraction'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "Canonical Representation Extraction"

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Fujiwara, Yusuke, Yoichi Miyawaki, and Yukiyasu Kamitani. "Modular Encoding and Decoding Models Derived from Bayesian Canonical Correlation Analysis." Neural Computation 25, no. 4 (2013): 979–1005. http://dx.doi.org/10.1162/neco_a_00423.

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Neural encoding and decoding provide perspectives for understanding neural representations of sensory inputs. Recent functional magnetic resonance imaging (fMRI) studies have succeeded in building prediction models for encoding and decoding numerous stimuli by representing a complex stimulus as a combination of simple elements. While arbitrary visual images were reconstructed using a modular model that combined the outputs of decoder modules for multiscale local image bases (elements), the shapes of the image bases were heuristically determined. In this work, we propose a method to establish m

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VIRPIOJA, SAMI, MARI-SANNA PAUKKERI, ABHISHEK TRIPATHI, TIINA LINDH-KNUUTILA, and KRISTA LAGUS. "Evaluating vector space models with canonical correlation analysis." Natural Language Engineering 18, no. 3 (2011): 399–436. http://dx.doi.org/10.1017/s1351324911000271.

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AbstractVector space models are used in language processing applications for calculating semantic similarities of words or documents. The vector spaces are generated with feature extraction methods for text data. However, evaluation of the feature extraction methods may be difficult. Indirect evaluation in an application is often time-consuming and the results may not generalize to other applications, whereas direct evaluations that measure the amount of captured semantic information usually require human evaluators or annotated data sets. We propose a novel direct evaluation method based on c

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Summers, Joshua D., Bernie Bettig, and Jami J. Shah. "The Design Exemplar: A New Data Structure for Embodiment Design Automation." Journal of Mechanical Design 126, no. 5 (2004): 775–87. http://dx.doi.org/10.1115/1.1767179.

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A key issue in developing new intelligent computer aided design and manufacturing tools (CAD/CAM) is knowledge representation. Engineering knowledge has been represented in many forms, such as rule sets, design procedures, features, frames, and semantic networks. Some of these are domain dependent, some are query dependent, and some require specialized inferencing engines or solving algorithms, thus providing a severe limitation for exchange and re-use of design knowledge. A standard representation structure that is capable of encapsulating different types of knowledge would be a useful tool.

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Lin, Chin-Teng, Chih-Sheng Huang, Wen-Yu Yang, Avinash Kumar Singh, Chun-Hsiang Chuang, and Yu-Kai Wang. "Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering." Journal of Healthcare Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/5081258.

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Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intr

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Li, Xiaochuan, Xiaoyu Yang, Yingjie Yang, Ian Bennett, and David Mba. "An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data." Structural Health Monitoring 19, no. 5 (2019): 1375–90. http://dx.doi.org/10.1177/1475921719884019.

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In this work, a novel diagnostic and prognostic framework is proposed to detect faults and predict remaining service life of large-scale rotating machinery in the presence of scarce failure data. In the proposed framework, a canonical variate residuals–based diagnostic method is developed to facilitate remaining service life prediction by continuously implementing detection of the prediction start time. A novel two-step prognostic feature exploring approach that involves fault identification, feature extraction, feature selection and multi-feature fusion is put forward. Most existing prognosti

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Fleury, Cécile, Jérôme Gracy, Marie-Françoise Gautier, et al. "Comprehensive classification of the plant non-specific lipid transfer protein superfamily towards its sequence–structure–function analysis." PeerJ 7 (August 14, 2019): e7504. http://dx.doi.org/10.7717/peerj.7504.

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Background Non-specific Lipid Transfer Proteins (nsLTPs) are widely distributed in the plant kingdom and constitute a superfamily of related proteins. Several hundreds of different nsLTP sequences—and counting—have been characterized so far, but their biological functions remain unclear. It has been clear for years that they present a certain interest for agronomic and nutritional issues. Deciphering their functions means collecting and analyzing a variety of data from gene sequence to protein structure, from cellular localization to the physiological role. As a huge and growing number of new

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Ingel, Anti, and Raul Vicente. "Information Bottleneck as Optimisation Method for SSVEP-Based BCI." Frontiers in Human Neuroscience 15 (September 7, 2021). http://dx.doi.org/10.3389/fnhum.2021.675091.

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In this study, the information bottleneck method is proposed as an optimisation method for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). The information bottleneck is an information-theoretic optimisation method for solving problems with a trade-off between preserving meaningful information and compression. Its main practical application in machine learning is in representation learning or feature extraction. In this study, we use the information bottleneck to find optimal classification rule for a BCI. This is a novel application for the information bottle

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Lee, Jaehyun, Doheon Lee, and Kwang Hyung Lee. "Literature mining for context-specific molecular relations using multimodal representations (COMMODAR)." BMC Bioinformatics 21, S5 (2020). http://dx.doi.org/10.1186/s12859-020-3396-y.

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Abstract Biological contextual information helps understand various phenomena occurring in the biological systems consisting of complex molecular relations. The construction of context-specific relational resources vastly relies on laborious manual extraction from unstructured literature. In this paper, we propose COMMODAR, a machine learning-based literature mining framework for context-specific molecular relations using multimodal representations. The main idea of COMMODAR is the feature augmentation by the cooperation of multimodal representations for relation extraction. We leveraged biome

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Wu, Cheng-Kun, Xiao-Chen Zhang, Zhi-Jiang Yang, Ai-Ping Lu, Ting-Jun Hou, and Dong-Sheng Cao. "Learning to SMILES: BAN-based strategies to improve latent representation learning from molecules." Briefings in Bioinformatics, August 24, 2021. http://dx.doi.org/10.1093/bib/bbab327.

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Abstract Computational methods have become indispensable tools to accelerate the drug discovery process and alleviate the excessive dependence on time-consuming and labor-intensive experiments. Traditional feature-engineering approaches heavily rely on expert knowledge to devise useful features, which could be costly and sometimes biased. The emerging deep learning (DL) methods deliver a data-driven method to automatically learn expressive representations from complex raw data. Inspired by this, researchers have attempted to apply various deep neural network models to simplified molecular inpu

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Jones, Timothy. "The Black Mass as Play: Dennis Wheatley's The Devil Rides Out." M/C Journal 17, no. 4 (2014). http://dx.doi.org/10.5204/mcj.849.

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Literature—at least serious literature—is something that we work at. This is especially true within the academy. Literature departments are places where workers labour over texts carefully extracting and sharing meanings, for which they receive monetary reward. Specialised languages are developed to describe professional concerns. Over the last thirty years, the productions of mass culture, once regarded as too slight to warrant laborious explication, have been admitted to the academic workroom. Gothic studies—the specialist area that treats fearful and horrifying texts —has embraced the growi

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Dissertations / Theses on the topic "Canonical Representation Extraction"

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Park, Sung Hee. "Discipline-Independent Text Information Extraction from Heterogeneous Styled References Using Knowledge from the Web." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/52860.

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In education and research, references play a key role. They give credit to prior works, and provide support for reviews, discussions, and arguments. The set of references attached to a publication can help describe that publication, can aid with its categorization and retrieval, can support bibliometric studies, and can guide interested readers and researchers. If suitably analyzed, that set can aid with the analysis of the publication itself, especially regarding all its citing passages. However, extracting and parsing references are difficult problems. One concern is that there are many styl

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Conference papers on the topic "Canonical Representation Extraction"

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Park, Sung Hee, Roger W. Ehrich, and Edward A. Fox. "A hybrid two-stage approach for discipline-independent canonical representation extraction from references." In the 12th ACM/IEEE-CS joint conference. ACM Press, 2012. http://dx.doi.org/10.1145/2232817.2232871.

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Watchareeruetai, Ukrit, Yoshinori Takeuchi, Tetsuya Matsumoto, Hiroaki Kudo, and Noboru Ohnishi. "Transformation of redundant representations of linear genetic programming into canonical forms for efficient extraction of image features." In 2008 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2008. http://dx.doi.org/10.1109/cec.2008.4631062.

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Bettig, Bernie, Jami J. Shah, and Joshua D. Summers. "Domain Independent Characterization of Parametric and Geometric Problems in Embodiment Design." In ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/detc2000/dac-14259.

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Abstract In embodiment and detailed design one is often concerned with sizing and geometric arrangement. Constraint problems of satisfying certain relationships in feasible designs are often solved by domain specific procedures in design and manufacturing applications. This paper shows that a variety of such problems are reducible to a small set of generic problems solvable by domain-independent procedures. A taxonomy of constraint problem types is developed. The taxonomy is based on the types of entities, constraints, and tasks (reasoning or inquiry) that are to be performed. The “exemplar” i

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