Relevant bibliographies by topics / Instance

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Instance'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Instance"

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Cheng, An-Chieh, Chieh Hubert Lin, Da-Cheng Juan, Wei Wei, and Min Sun. "InstaNAS: Instance-Aware Neural Architecture Search." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3577–84. http://dx.doi.org/10.1609/aaai.v34i04.5764.

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Conventional Neural Architecture Search (NAS) aims at finding a single architecture that achieves the best performance, which usually optimizes task related learning objectives such as accuracy. However, a single architecture may not be representative enough for the whole dataset with high diversity and variety. Intuitively, electing domain-expert architectures that are proficient in domain-specific features can further benefit architecture related objectives such as latency. In this paper, we propose InstaNAS—an instance-aware NAS framework—that employs a controller trained to search for a “d
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Smith-Miles, Kate, and Simon Bowly. "Generating new test instances by evolving in instance space." Computers & Operations Research 63 (November 2015): 102–13. http://dx.doi.org/10.1016/j.cor.2015.04.022.

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Zhao, Fei, Yang Xin, Kai Zhang, and Xinxin Niu. "Representativeness-Based Instance Selection for Intrusion Detection." Security and Communication Networks 2021 (March 12, 2021): 1–13. http://dx.doi.org/10.1155/2021/6638134.

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With the continuous development of network technology, an intrusion detection system needs to face detection efficiency and storage requirement when dealing with large data. A reasonable way of alleviating this problem is instance selection, which can reduce the storage space and improve intrusion detection efficiency by selecting representative instances. An instance is representative not only in its class but also in different classes. This representativeness reflects the importance of an instance. Since the existing instance selection algorithm does not take into account the above situation
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Wang, Hua, Feiping Nie, and Heng Huang. "Learning Instance Specific Distance for Multi-Instance Classification." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 507–12. http://dx.doi.org/10.1609/aaai.v25i1.7893.

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Multi-Instance Learning (MIL) deals with problems where each training example is a bag, and each bag contains a set of instances. Multi-instance representation is useful in many real world applications, because it is able to capture more structural information than traditional flat single-instance representation. However, it also brings new challenges. Specifically, the distance between data objects in MIL is a set-to-set distance, which is harder to estimate than vector distances used in single-instance data. Moreover, because in MIL labels are assigned to bags instead of instances, although
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Ren, Lingyu, Youlong Yang, Liqin Sun, and Xu Wu. "Grey-based multiple instance learning with multiple bag-representative." AI Communications 33, no. 2 (2020): 59–73. http://dx.doi.org/10.3233/aic-200628.

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Multiple instance learning is a modification in supervised learning that handles the classification of collection instances, which called bags. Each bag contains a number of instances whose features are extracted. In multiple instance learning, the standard assumption is that a positive bag contains at least one positive instance, whereas a negative bag is only comprised of negative instances. The complexity of multiple instance learning relies heavily on the number of instances in the training datasets. Since we are usually confronted with a large instance space, it is important to design eff
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Tanaka-Ishii, Kumiko. "An Instance vs. The Instance." Minds and Machines 19, no. 1 (2008): 117–28. http://dx.doi.org/10.1007/s11023-008-9128-0.

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Analyti, Anastasia, Nicolas Spyratos, and Panos Constantopoulos. "Deriving and Retrieving Contextual Categorical Information through Instance Inheritance." Fundamenta Informaticae 44, no. 4 (2000): 321–51. https://doi.org/10.3233/fun-2000-44401.

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In semantic and object-oriented data models, each class has one or more typing properties that associate it to other classes, and carry type information about all instances of the class. We introduce a new kind of property that we call instance-typing property. An instance-typing property associates an instance of a class to another class, and carries type information about that particular instance (and not about all instances of the class). Instance-typing properties are important as they allow to represent summary information about an instance, in addition to specific information. In this pa
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SCOTT, STEPHEN, JUN ZHANG, and JOSHUA BROWN. "ON GENERALIZED MULTIPLE-INSTANCE LEARNING." International Journal of Computational Intelligence and Applications 05, no. 01 (2005): 21–35. http://dx.doi.org/10.1142/s1469026805001453.

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We describe a generalisation of the multiple-instance learning model in which a bag's label is not based on a single instance's proximity to a single target point. Rather, a bag is positive if and only if it contains a collection of instances, each near one of a set of target points. We then adapt a learning-theoretic algorithm for learning in this model and present empirical results on data from robot vision, content-based image retrieval, and protein sequence identification.
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Qurratulain, Safder, Zezhong Zheng, Jun Xia, Yi Ma, and Fangrong Zhou. "Deep learning instance segmentation framework for burnt area instances characterization." International Journal of Applied Earth Observation and Geoinformation 116 (February 2023): 103146. http://dx.doi.org/10.1016/j.jag.2022.103146.

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Gong, Yiping, Fan Zhang, Xiangyang Jia, Zhu Mao, Xianfeng Huang, and Deren Li. "Instance Segmentation in Very High Resolution Remote Sensing Imagery Based on Hard-to-Segment Instance Learning and Boundary Shape Analysis." Remote Sensing 14, no. 1 (2021): 23. http://dx.doi.org/10.3390/rs14010023.

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Although great success has been achieved in instance segmentation, accurate segmentation of instances remains difficult, especially at object edges. This problem is more prominent for instance segmentation in remote sensing imagery due to the diverse scales, variable illumination, smaller objects, and complex backgrounds. We find that most current instance segmentation networks do not consider the segmentation difficulty of different instances and different regions within the instance. In this paper, we study this problem and propose an ensemble method to segment instances from remote sensing
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Dissertations / Theses on the topic "Instance"

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Foulds, James Richard. "Learning Instance Weights in Multi-Instance Learning." The University of Waikato, 2008. http://hdl.handle.net/10289/2460.

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Multi-instance (MI) learning is a variant of supervised machine learning, where each learning example contains a bag of instances instead of just a single feature vector. MI learning has applications in areas such as drug activity prediction, fruit disease management and image classification. This thesis investigates the case where each instance has a weight value determining the level of influence that it has on its bag's class label. This is a more general assumption than most existing approaches use, and thus is more widely applicable. The challenge is to accurately estimate these weights
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BRAUNER, DANIELA FRANCISCO. "INSTANCE-BASED SCHEMA MATCHING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=12573@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Um mediador é um componente de software que auxilia o acesso a fontes de dados. Com o advento da Web, a construção de mediadores impõe desafios importantes, tais como a capacidade de fornecer acesso integrado a fontes de dados independentes e dinâmicas e a habilidade de resolver a heterogeneidade semântica entre os esquemas destas fontes. Para lidar com esses desafios, o alinhamento de esquemas é uma questão fundamental. Nesta tese são propostas abordagens de alinhamento de esquemas de classificação (tesauros) e esquemas c
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Latham, Andrew C. "Multiple-Instance Feature Ranking." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1440642294.

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Hsu, Ikung. "Robust Multiple Instance Learning." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case158648636659484.

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Boulmier, Daniel. "Preuve et instance prud'homale." Orléans, 2000. http://www.theses.fr/2000ORLE0008.

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Les sources internationales, européennes ou nationales, édictant le principe d'un droit à l'accès a un tribunal et a un procès équitable, permettent-elles l'effectivité de l’égalité des parties devant le juge national ? Cette interrogation, soulevée dans le cadre du procès prud'homal, amène à une recherche dépassant la stricte analyse du déroulement du procès, recherche qui démontre la corrélation fondamentale entre la preuve et l'instance prud'homale. Le principe accusatoire de la procédure civile est affirmé en droit français, mais le juge dispose de quelques pouvoirs inquisitoires. En outre
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Wang, Xiaobo. "Collaboration Instance Manager of UbiCollab 2008 : Collaboration Instance Synchronization and Management in P2P network." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9714.

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<p>This report is for my research of Collaboration Instance Manager of UbiCollab project. UbiCollab want to be the platform for ubiquitous collaborative active. UbiCollab project aims to develop a distributed collaborative platform which makes people in distributed space ubiquitous collaborate with friends and colleagues. Collaboration instance manager (CIM) is a core component of the UbiCollab platform, which manage such collaborative activities. My research topics of CIM include in the P2P network development by using JXME, the data synchronization through this P2P network and how to manag
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Cheung, Pak-Ming. "Kernel-based multiple-instance learning /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20CHEUNGP.

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Varges, Sebastian. "Instance-based Natural Language Generation." Thesis, University of Edinburgh, 2003. http://hdl.handle.net/1842/27574.

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In recent years, ranking approaches to Natural Language Generation have become increasingly popular. They abandon the idea of generation as a deterministic decision-making process in favour of approaches that combine overgeneration with ranking at some stage in processing. In this thesis, we investigate the use of instance-based ranking methods for surface realization in Natural Language Generation. Our approach to instance-based Natural Language Generation employs two basic components: a rule system that generates a number of realization candidates from a meaning representation and an instanc
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Martin, Richard George. "Instance-based symbolic pattern association." Thesis, University of Ulster, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388880.

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Doran, Gary Brian Jr. "Multiple-Instance Learning from Distributions." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1417736923.

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Books on the topic "Instance"

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Kirk, Andy. Instance Chart. SAGE Publications, Ltd., 2016. http://dx.doi.org/10.4135/9781529777178.

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illustrator, Osterweil Wendy, and Moore Michael illustrator, eds. For instance. Chax Press, 2019.

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Herrera, Francisco, Sebastián Ventura, Rafael Bello, et al. Multiple Instance Learning. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47759-6.

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Howells, William Dean. A modern instance. Transaction Publishers, 2000.

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Howells, William Dean. A modern instance. Dover Publications, 2008.

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Malitsky, Yuri. Instance-Specific Algorithm Configuration. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11230-5.

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Buckeridge, Anthony. Take Jennings for instance. Macmillan Children's Books, 1994.

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Archives, London Metropolitan, and City of London (England). Records Office, eds. Instance Act book, 1575-1577. London Metropolitan Archives, 2008.

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Martin, Richard George. Instance-based symbolic pattern association. The Author], 1997.

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Pears, Iain. An instance of the fingerpost. G.K. Hall, 1998.

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Book chapters on the topic "Instance"

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Akgün, Özgür, Nguyen Dang, Ian Miguel, András Z. Salamon, and Christopher Stone. "Instance Generation via Generator Instances." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30048-7_1.

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Utgoff, Paul E., James Cussens, Stefan Kramer, et al. "Instance." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_406.

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Weik, Martin H. "instance." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_9148.

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Brighton, Henry, and Chris Mellish. "Identifying Competence-Critical Instances for Instance-Based Learners." In Instance Selection and Construction for Data Mining. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3359-4_5.

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Zhang, Duzhou, and Xibin Cao. "Using Similarity between Paired Instances to Improve Multiple-Instance Learning via Embedded Instance Selection." In Neural Information Processing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42042-9_44.

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Kimms, Alf. "Instance Generation." In Multi-Level Lot Sizing and Scheduling. Physica-Verlag HD, 1997. http://dx.doi.org/10.1007/978-3-642-50162-3_4.

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Png, Adrian, and Luc Demanche. "Compute Instance." In Getting Started with Oracle Cloud Free Tier. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6011-1_4.

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Spendolini, Scott. "Instance Settings." In Expert Oracle Application Express Security. Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-4732-6_4.

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García, Salvador, Julián Luengo, and Francisco Herrera. "Instance Selection." In Intelligent Systems Reference Library. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10247-4_8.

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Weik, Martin H. "generic instance." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_7933.

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Conference papers on the topic "Instance"

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Baručić, Denis, and Jan Kybic. "Multiple instance learning: attention to instance classification." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3045059.

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Lei, Xiaohan, Min Wang, Wengang Zhou, Li Li, and Houqiang Li. "Instance-Aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01545.

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Ekanayake, Sachini Piyoni, and Daphney–Stavroula Zois. "Instance–wise Feature Acquisition with Classifier Selection Option for Structured Data Instances." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889339.

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Zhang, Ya-Lin, and Zhi-Hua Zhou. "Multi-Instance Learning with Key Instance Shift." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/481.

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Multi-instance learning (MIL) deals with the tasks where each example is represented by a bag of instances. A bag is positive if it contains at least one positive instance, and negative otherwise. The positive instances are also called key instances. Only bag labels are observed, whereas specific instance labels are not available in MIL. Previous studies typically assume that training and test data follow the same distribution, which may be violated in many real-world tasks. In this paper, we address the problem that the distribution of key instances varies between training and test phase. We
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Zhang, Weijia. "Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels with Variational Auto-Encoder." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/465.

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Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An important advantage of multi-instance learning is that by representing objects as a bag of instances, it is able to preserve the inherent dependencies among parts of the objects. Unfortunately, most existing algorithms assume all instances to be identically and independently distributed, which violates real-world scenarios since the instances within a bag are
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Wang, Qifan, Gal Chechik, Chen Sun, and Bin Shen. "Instance-Level Label Propagation with Multi-Instance Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/410.

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Label propagation is a popular semi-supervised learning technique that transfers information from labeled examples to unlabeled examples through a graph. Most label propagation methods construct a graph based on example-to-example similarity, assuming that the resulting graph connects examples that share similar labels. Unfortunately, example-level similarity is sometimes badly defined. For instance, two images may contain two different objects, but have similar overall appearance due to large similar background. In this case, computing similarities based on whole-image would fail propagating
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Herman, Gunawan, Getian Ye, Yang Wang, Jie Xu, and Bang Zhang. "Multi-instance learning with relational information of instances." In 2009 Workshop on Applications of Computer Vision (WACV). IEEE, 2009. http://dx.doi.org/10.1109/wacv.2009.5403078.

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Li, Zecheng, Zening Zeng, Yuqi Liang, and Jin-Gang Yu. "Complete Instances Mining for Weakly Supervised Instance Segmentation." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/127.

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Weakly supervised instance segmentation (WSIS) using only image-level labels is a challenging task due to the difficulty of aligning coarse annotations with the finer task. However, with the advancement of deep neural networks (DNNs), WSIS has garnered significant attention. Following a proposal-based paradigm, we encounter a redundant segmentation problem resulting from a single instance being represented by multiple proposals. For example, we feed a picture of a dog and proposals into the network and expect to output only one proposal containing a dog, but the network outputs multiple propos
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Chou, Yi-Ting, Shui-Lung Chuang, and Xuanhui Wang. "Instant Web Retrieval for Instance-Attribute Queries." In IEEE/WIC/ACM International Conference on Web Intelligence (WI'07). IEEE, 2007. http://dx.doi.org/10.1109/wi.2007.117.

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Jiao, Changzhe, and Alina Zare. "Multiple Instance Dictionary Learning using Functions of Multiple Instances." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7900041.

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Reports on the topic "Instance"

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Mogul, J., and A. Van. Instance Digests in HTTP. RFC Editor, 2002. http://dx.doi.org/10.17487/rfc3230.

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Lindem, A., A. Roy, and S. Mirtorabi. OSPFv2 Multi-Instance Extensions. RFC Editor, 2012. http://dx.doi.org/10.17487/rfc6549.

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Ginsberg, L., M. Shand, A. Roy, and D. Ward. IS-IS Multi-Instance. Edited by S. Previdi. RFC Editor, 2012. http://dx.doi.org/10.17487/rfc6822.

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Ginsberg, L., S. Previdi, and W. Henderickx. IS-IS Multi-Instance. RFC Editor, 2017. http://dx.doi.org/10.17487/rfc8202.

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Clamen, Stewart M. Type Evolution and Instance Adaptation. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada248435.

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Clamen, Stewart M. Type Evolution and Instance Adaptation. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada256372.

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Retana, A., and D. Cheng. OSPFv3 Instance ID Registry Update. RFC Editor, 2013. http://dx.doi.org/10.17487/rfc6969.

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Jirka, Simon, and Daniel Nuest, eds. Sensor Instance Registry Discussion Paper. Open Geospatial Consortium, Inc., 2010. http://dx.doi.org/10.62973/10-171.

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Bryan, A. Additional Hash Algorithms for HTTP Instance Digests. RFC Editor, 2010. http://dx.doi.org/10.17487/rfc5843.

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Lengyel, B., and B. Claise. A File Format for YANG Instance Data. RFC Editor, 2022. http://dx.doi.org/10.17487/rfc9195.

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