Relevant bibliographies by topics / Augmentation de tables

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Augmentation de tables'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Augmentation de tables"

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Wu, Junyi, Chen Ye, Haoshi Zhi, and Shihao Jiang. "Column-Type Prediction for Web Tables Powered by Knowledge Base and Text." Mathematics 11, no. 3 (2023): 560. http://dx.doi.org/10.3390/math11030560.

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Web tables are essential for applications such as data analysis. However, web tables are often incomplete and short of some critical information, which makes it challenging to understand the web table content. Automatically predicting column types for tables without metadata is significant for dealing with various tables from the Internet. This paper proposes a CNN-Text method to deal with this task, which fuses CNN prediction and voting processes. We present data augmentation and synthetic column generation approaches to improve the CNN’s performance and use extracted text to get better predi
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Dobra, Adrian, Claudia Tebaldi, and Mike West. "Data augmentation in multi-way contingency tables with fixed marginal totals." Journal of Statistical Planning and Inference 136, no. 2 (2006): 355–72. http://dx.doi.org/10.1016/j.jspi.2004.07.002.

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Chen, Zhiyu. "Dataset Search and Augmentation." ACM SIGIR Forum 56, no. 1 (2022): 1–2. http://dx.doi.org/10.1145/3582524.3582544.

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Data has become an indispensable part of our life. However, current mainstream commercial search engines do not support specialized functions for dataset search. A dataset usually consists of both metadata and data content. Existing information retrieval models designed for Web search cannot efficiently extract semantic information inside structured datasets, even when they contain textual content. Developing new algorithms for next-generation search engines to efficiently find datasets can benefit data practitioners in their data discovery experience. In this dissertation, we consider how to
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Wang, Jiayi, Chengliang Chai, Nan Tang, Jiabin Liu, and Guoliang Li. "Coresets over multiple tables for feature-rich and data-efficient machine learning." Proceedings of the VLDB Endowment 16, no. 1 (2022): 64–76. http://dx.doi.org/10.14778/3561261.3561267.

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Successful machine learning (ML) needs to learn from good data. However, one common issue about train data for ML practitioners is the lack of good features. To mitigate this problem, feature augmentation is often employed by joining with (or enriching features from) multiple tables, so as to become feature-rich ML. A consequent problem is that the enriched train data may contain too many tuples, especially if the feature augmentation is obtained through 1 (or many)-to-many or fuzzy joins. Training an ML model with a very large train dataset is data-inefficient. Coreset is often used to achiev
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Sajid, Tanveer, Muhammad Sagheer, Shafqat Hussain, and Faisal Shahzad. "Impact of double-diffusive convection and motile gyrotactic microorganisms on magnetohydrodynamics bioconvection tangent hyperbolic nanofluid." Open Physics 18, no. 1 (2020): 74–88. http://dx.doi.org/10.1515/phys-2020-0009.

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AbstractThe double-diffusive tangent hyperbolic nanofluid containing motile gyrotactic microorganisms and magnetohydrodynamics past a stretching sheet is examined. By adopting the scaling group of transformation, the governing equations of motion are transformed into a system of nonlinear ordinary differential equations. The Keller box scheme, a finite difference method, has been employed for the solution of the nonlinear ordinary differential equations. The behaviour of the working fluid against various parameters of physical nature has been analyzed through graphs and tables. The behaviour o
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Li, Xiangge, Hong Luo, and Yan Sun. "WordBlitz: An Efficient Hard-Label Textual Adversarial Attack Method Jointly Leveraging Adversarial Transferability and Word Importance." Applied Sciences 14, no. 9 (2024): 3831. http://dx.doi.org/10.3390/app14093831.

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Existing textual attacks mostly perturb keywords in sentences to generate adversarial examples by relying on the prediction confidence of victim models. In practice, attackers can only access the prediction label, meaning that the victim model can easily defend against such hard-label attacks by denying access based on the attack’s frequency. In this paper, we propose an efficient hard-label attack approach, called WordBlitz. First, based on the adversarial transferability, we train a substitute model to initialize the attack parameter set, including a candidate pool and two weight tables of k
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Chen, Kaiwen, and Nick Koudas. "Unstructured Data Fusion for Schema and Data Extraction." Proceedings of the ACM on Management of Data 2, no. 3 (2024): 1–26. http://dx.doi.org/10.1145/3654984.

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Recently, there has been significant interest in extracting actionable insights from the abundance of unstructured textual data. In this paper, we introduce a novel problem, which we term Semistructured Schema and Data Extraction (SDE). This task aims to enhance and complete tables using information discovered from textual repositories, given partial table specifications in the form of queries. To effectively solve SDE, several challenges must be overcome, which involve transforming the partial table specifications into effective queries, retrieving relevant documents, discerning values for pa
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Castelo, Sonia, Rémi Rampin, Aécio Santos, Aline Bessa, Fernando Chirigati, and Juliana Freire. "Auctus." Proceedings of the VLDB Endowment 14, no. 12 (2021): 2791–94. http://dx.doi.org/10.14778/3476311.3476346.

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The large volumes of structured data currently available, from Web tables to open-data portals and enterprise data, open up new opportunities for progress in answering many important scientific, societal, and business questions. However, finding relevant data is difficult. While search engines have addressed this problem for Web documents, there are many new challenges involved in supporting the discovery of structured data. We demonstrate how the Auctus dataset search engine addresses some of these challenges. We describe the system architecture and how users can explore datasets through a ri
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Bussotti, Jean-Flavien, Enzo Veltri, Donatello Santoro, and Paolo Papotti. "Generation of Training Examples for Tabular Natural Language Inference." Proceedings of the ACM on Management of Data 1, no. 4 (2023): 1–27. http://dx.doi.org/10.1145/3626730.

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Tabular data is becoming increasingly important in Natural Language Processing (NLP) tasks, such as Tabular Natural Language Inference (TNLI). Given a table and a hypothesis expressed in NL text, the goal is to assess if the former structured data supports or refutes the latter. In this work, we focus on the role played by the annotated data in training the inference model. We introduce a system, Tenet, for the automatic augmentation and generation of training examples for TNLI. Given the tables, existing approaches are either based on human annotators, and thus expensive, or on methods that p
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Ismail, Nur Hilwani, Siti Fatimah Ibrahim, Farah Hanan Fathihah Jaffar, Mohd Helmy Mokhtar, Kok Yong Chin, and Khairul Osman. "Augmentation of the Female Reproductive System Using Honey: A Mini Systematic Review." Molecules 26, no. 3 (2021): 649. http://dx.doi.org/10.3390/molecules26030649.

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Phytochemical contents of honey are presumed to be beneficial to the female reproductive system (FRS). However, the biological effects of honey supplementation (HS) in vivo on the FRS remain unclear. This review aims to investigate the current literature on the effects of HS on the FRS, particularly on the sex hormone profile and reproductive organs (uterus and vagina). A systematic literature search using Scopus, MEDLINE via Ovid and Cochrane Library databases was conducted. Records were screened and identified for preclinical and clinical studies addressing the effects of HS on the FRS. Data
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Dissertations / Theses on the topic "Augmentation de tables"

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Liu, Jixiong. "Semantic Annotations for Tabular Data Using Embeddings : Application to Datasets Indexing and Table Augmentation." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS529.

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Avec le développement de l'Open Data, un grand nombre de sources de données sont mises à disposition des communautés (notamment les data scientists et les data analysts). Ces données constituent des sources importantes pour les services numériques sous réserve que les données soient nettoyées, non biaisées, et combinées à une sémantique explicite et compréhensible par les algorithmes afin de favoriser leur exploitation. En particulier, les sources de données structurées (CSV, JSON, XML, etc.) constituent la matière première de nombreux processus de science des données. Cependant, ces données p
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Lehmberg, Oliver [Verfasser], and Christian [Akademischer Betreuer] Bizer. "Web table integration and profiling for knowledge base augmentation / Oliver Lehmberg ; Betreuer: Christian Bizer." Mannheim : Universitätsbibliothek Mannheim, 2019. http://d-nb.info/1197143866/34.

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Heyder, Jakob Wendelin. "Knowledge Base Augmentation from Spreadsheet Data : Combining layout inference with multimodal candidate classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278824.

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Spreadsheets compose a valuable and notably large dataset of documents within many enterprise organizations and on the Web. Although spreadsheets are intuitive to use and equipped with powerful functionalities, extraction and transformation of the data remain a cumbersome and mostly manual task. The great flexibility they provide to the user results in data that is arbitrarily structured and hard to process for other applications. In this paper, we propose a novel architecture that combines supervised layout inference and multimodal candidate classification to allow knowledge base augmentation
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Books on the topic "Augmentation de tables"

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United States. National Aeronautics and Space Administration., ed. Experimental and computational investigation of lift-enhancing tabs on a multi-element airfoil. Joint Institute for Aeronautics and Acoustics, National Aeronautics and Space Administration, Ames Research Center, 1996.

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United States. National Aeronautics and Space Administration., ed. Experimental and computational investigation of lift-enhancing tabs on a multi-element airfoil. Joint Institute for Aeronautics and Acoustics, National Aeronautics and Space Administration, Ames Research Center, 1996.

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Book chapters on the topic "Augmentation de tables"

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Del Bimbo, Davide, Andrea Gemelli, and Simone Marinai. "Data Augmentation on Graphs for Table Type Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-23028-8_25.

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Khan, Umar, Sohaib Zahid, Muhammad Asad Ali, Adnan Ul-Hasan, and Faisal Shafait. "TabAug: Data Driven Augmentation for Enhanced Table Structure Recognition." In Document Analysis and Recognition – ICDAR 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86331-9_38.

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Chen, Bangdong, Dezhi Peng, Jiaxin Zhang, Yujin Ren, and Lianwen Jin. "Complex Table Structure Recognition in the Wild Using Transformer and Identity Matrix-Based Augmentation." In Frontiers in Handwriting Recognition. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21648-0_37.

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Kouwenhoven, Jonas, Lucas Lageweg, and Benno Kruit. "Constrained LLM-Based Query Generation for Question Answering on Official Statistics." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia241052.

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This research explores the development of a knowledge graph statistical question answering system for Statistics Netherlands. Aimed at efficiently retrieving single statistical values from their extensive database, which encompasses over a billion values across more than 4,000 tables, we propose a comprehensive three-component framework consisting of: (1) a data augmentation method to generate synthetic data, (2) an entity retrieval system that leverages various encoder networks along with different hard negative mining techniques for the effective retrieval of tables, measures, and dimensions
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Buchholz Margret, Mattsson Müller Ingrid, Olsson Maria, and Gani Ulrika. "Apps for Augmentative and Alternative Communication – A Forum on the Web." In Assistive Technology Research Series. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-304-9-311.

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Smartphones and tablets have taken place in the field of assistive technology and there are now many apps to use for people with communication difficulties. There are websites, blogs and Facebook groups discussing different apps, but it can be hard to navigate between different forums and get the information you are looking for. The longterm goal with this project is to increase knowledge and compile data on how smartphones and tablets may be equipped with apps to support the every-day life of people with communication difficulties. In the first part of the three-year long project, a web forum
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Mehl-Schneider, Toby B. "Recent Advances in Augmentative and Alternative Communication." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8395-2.ch006.

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With the increased development of mobile technologies, such as smartphones and tablets (i.e. iPhone, iPad), the field of augmentative and alternative communication (AAC) has changed rapidly over the last few years. Recent advances in technology have introduced applications (apps) for AAC purposes. These novel technologies could provide numerous benefits to individuals with complex communication needs. Nevertheless, introducing mobile technology apps is not without risk. Since these apps can be purchased and retrieved with relative ease, AAC assessments and collaborative evaluations have been c
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Krivoshei Andrei, Lamp Jürgen, Min Mart, Uuetoa Tiina, Uuetoa Hasso, and Annus Paul. "Wearable System for Non-Invasive and Continuous Monitoring Central Aortic Pressure Curve and Augmentation Index." In Studies in Health Technology and Informatics. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-268-4-101.

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The paper presents a non-invasive method and system for a long-term and continuous monitoring of the central aortic pressure (CAP) waveform and the augmentation index (AI). The CAP curve is estimated from the measured radial electrical bio-impedance (EBI) using spectral domain transfer functions (TF), which are established on the basis of data analysis during clinical experiments. Experiments were carried out on 3 volunteers by now. During the experiment, a 0.5 mg sublingual nitroglycerin tablet was administrated to each volunteer. Both, the reconstructed CAP curve and the AI have very good co
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Zhu, Wei, Charles B. Owen, Hairong Li, and Joo-Hyun Lee. "Design of the PromoPad." In Advances in End User Computing. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-687-7.ch011.

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Augmented-reality technologies as a new way of human-computer interaction make possible real-time modification of our perception of reality without active user interference. This article introduces the prototype of an augmented-reality shopping-assistant device, the PromoPad, based on a handheld tablet PC allowing see-through vision with augmentations. While this new interaction utilizing augmented reality that places products into contextual settings can enhance shopping experience and suggest complementary products, it also has challenges and issues when used in a public environment such as
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"Faithful extension of a relation, bivalent tableau, faithful augmentation, Szpilrajn chain." In Theory of Relations. Elsevier, 2000. http://dx.doi.org/10.1016/s0049-237x(00)80055-5.

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Wunder, Iris, and Ruth Maloszek. "Perspective Chapter: iPEAR-MOOC." In Massive Open Online Courses - Current Practice and Future Trends [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.1001463.

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Augmented Reality (AR) has been developing rapidly in the past years, and the acceptance of this technology is growing amongst the users of smartphones and tablets. Educational applications and resources that use AR technology are also growing in both number and quality. However, most educators lack sufficient digital skills and are unaware of the pedagogical approaches necessary to use the available digital tools to advance their teaching practices and their professional development. An international team of researchers from the Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany), NTN
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Conference papers on the topic "Augmentation de tables"

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Zhang, Han, Yuheng Ma, and Hanfang Yang. "ALTER: Augmentation for Large-Table-Based Reasoning." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-long.9.

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Xia, Hao, Ren Li, Yu Chen, and Wenjing Wang. "Few-Shot Table Question Answering for Bridge Intelligent Management via Domain Adaptation and Retrieval Augmentation." In 2024 9th International Conference on Information Science, Computer Technology and Transportation (ISCTT). IEEE, 2024. https://doi.org/10.1109/isctt62319.2024.10875650.

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Myren, Jason, and Jake Weaver. "Methods for Avionics Systems to Support Third Party Development." In Vertical Flight Society 74th Annual Forum & Technology Display. The Vertical Flight Society, 2018. http://dx.doi.org/10.4050/f-0074-2018-12731.

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Today's modern aircraft are built with highly integrated avionics solutions that provide pilots enhanced situational awareness and reduced workload. This paper researches how highly integrated avionics can be architected in a way to simplify modification and augmentation of avionics systems with new features developed by third parties (in addition to the original avionics provider). Our objective is to determine the best technical solution(s) to enhancing avionics system architectures to support third party development. We reviewed technical solutions to enable third party integration, includi
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Weger, Marian, Thomas Hermann, and Robert Höldrich. "AltAR/Table: A Platform for Plausible Auditory Augmentation." In ICAD 2022: The 27th International Conference on Auditory Display. International Community for Auditory Display, 2022. http://dx.doi.org/10.21785/icad2022.005.

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Auditory feedback from everyday interactions can be augmented to project digital information in the physical world. For that purpose, auditory augmentation modulates irrelevant aspects of already existing sounds while at the same time preserving relevant ones. A strategy for maintaining a certain level of plausibility is to metaphorically modulate the physical object itself. By mapping information to physical parameters instead of arbitrary sound parameters, it is assumed that even untrained users can draw on prior knowledge. Here we present AltAR/table, a hard- and software platform for plaus
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Dreossi, Tommaso, Shromona Ghosh, Xiangyu Yue, Kurt Keutzer, Alberto Sangiovanni-Vincentelli, and Sanjit A. Seshia. "Counterexample-Guided Data Augmentation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/286.

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We present a novel framework for augmenting data sets for machine learning based on counterexamples. Counterexamples are misclassified examples that have important properties for retraining and improving the model. Key components of our framework include a \textit{counterexample generator}, which produces data items that are misclassified by the model and error tables, a novel data structure that stores information pertaining to misclassifications. Error tables can be used to explain the model's vulnerabilities and are used to efficiently generate counterexamples for augmentation. We show the
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Kavitha, K. M., Vaishnavi Naik, Sahana Angadi, Sandra Satish, and Suman Nayak. "Hybrid Approaches for Augmentation of Translation Tables for Indian Languages." In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2020. http://dx.doi.org/10.1109/icmla51294.2020.00157.

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Qi, Danrui, Weiling Zheng, and Jiannan Wang. "FeatAug: Automatic Feature Augmentation From One-to-Many Relationship Tables." In 2024 IEEE 40th International Conference on Data Engineering (ICDE). IEEE, 2024. http://dx.doi.org/10.1109/icde60146.2024.00146.

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Chen, Haipeng, Sushil Jajodia, Jing Liu, Noseong Park, Vadim Sokolov, and V. S. Subrahmanian. "FakeTables: Using GANs to Generate Functional Dependency Preserving Tables with Bounded Real Data." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/287.

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In many cases, an organization wishes to release some data, but is restricted in the amount of data to be released due to legal, privacy and other concerns. For instance, the US Census Bureau releases only 1% of its table of records every year, along with statistics about the entire table. However, the machine learning (ML) models trained on the released sub-table are usually sub-optimal. In this paper, our goal is to find a way to augment the sub-table by generating a synthetic table from the released sub-table, under the constraints that the generated synthetic table (i) has similar statisti
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Cao, Jianhao, Tamara Munzner, and Rachel Pottinger. "Visualizing a Tabular Data Repository to Facilitate Descriptive Tag Augmentation for New Tables." In SIGMOD/PODS '23: International Conference on Management of Data. ACM, 2023. http://dx.doi.org/10.1145/3597465.3605226.

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Pham, Minh, Craig A. Knoblock, Muhao Chen, Binh Vu, and Jay Pujara. "SPADE: A Semi-supervised Probabilistic Approach for Detecting Errors in Tables." 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.2021/488.

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Error detection is one of the most important steps in data cleaning and usually requires extensive human interaction to ensure quality. Existing supervised methods in error detection require a significant amount of training data while unsupervised methods rely on fixed inductive biases, which are usually hard to generalize, to solve the problem. In this paper, we present SPADE, a novel semi-supervised probabilistic approach for error detection. SPADE introduces a novel probabilistic active learning model, where the system suggests examples to be labeled based on the agreements between user lab
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Reports on the topic "Augmentation de tables"

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DEPARTMENT OF THE ARMY WASHINGTON DC. Army Policies and Procedures for Establishing Multiple Component Modification Table of Organization and Equipment (MTOE) and Augmentation Tables of Distribution (AUGTDAs) Units. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada402529.

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