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Relevant bibliographies by topics / Machine Learning, Learning from Constraints, First-Order Logic, Convex Logical Constraints

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Journal articles
Dissertations / Theses
Conference papers

Academic literature on the topic 'Machine Learning, Learning from Constraints, First-Order Logic, Convex Logical Constraints'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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Journal articles on the topic "Machine Learning, Learning from Constraints, First-Order Logic, Convex Logical Constraints"

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Wang, Wenya, and Sinno Jialin Pan. "Integrating Deep Learning with Logic Fusion for Information Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9225–32. http://dx.doi.org/10.1609/aaai.v34i05.6460.

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Information extraction (IE) aims to produce structured information from an input text, e.g., Named Entity Recognition and Relation Extraction. Various attempts have been proposed for IE via feature engineering or deep learning. However, most of them fail to associate the complex relationships inherent in the task itself, which has proven to be especially crucial. For example, the relation between 2 entities is highly dependent on their entity types. These dependencies can be regarded as complex constraints that can be efficiently expressed as logical rules. To combine such logic reasoning capa

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Larson, Jeffrey, Matt Menickelly, and Stefan M. Wild. "Derivative-free optimization methods." Acta Numerica 28 (May 1, 2019): 287–404. http://dx.doi.org/10.1017/s0962492919000060.

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In many optimization problems arising from scientific, engineering and artificial intelligence applications, objective and constraint functions are available only as the output of a black-box or simulation oracle that does not provide derivative information. Such settings necessitate the use of methods for derivative-free, or zeroth-order, optimization. We provide a review and perspectives on developments in these methods, with an emphasis on highlighting recent developments and on unifying treatment of such problems in the non-linear optimization and machine learning literature. We categorize

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Citton, Yves. "Automatic Endo-Attention, Creative ExoAttention: the Egocidal and Ecocidal Logic of Neoliberal Capitalism." New Formations 98, no. 98 (2019): 101–18. http://dx.doi.org/10.3898/newf:98.07.2019.

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The beginning of the twenty-first century could be characterised by the externalisation of attention, following the externalisation of our other faculties: the term 'exo-attention' can be used to refer to the increasing number of electrical devices performing attentional tasks for us outside of our bodies. At the same time, the logic of industrial production continues to demand human beings to develop automated gestures commanded by the planetary assembly line, intellectual gestures being now added to bodily gestures. This automation of our 'endo-attention' cannot be considered as a temporary

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Carpenter, Chris. "Production-Enhancement-Candidate Screening Powered by ML Unlocks Brownfield Potential." Journal of Petroleum Technology 76, no. 10 (2024): 98–100. http://dx.doi.org/10.2118/1024-0098-jpt.

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_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 215244, “Automated Production-Enhancement-Candidates Screening Powered by Machine Learning Unlocks Untapped Potential in Matured Oil Fields: A Case Study,” by Nusheena M. Khair, SPE, M. Farid Zaizakrani, SPE, and Nur A.I.Z. Azhar, Petronas, et al. The paper has not been peer reviewed. _ Field A offshore East Malaysia has been a productive conventional oil field for more than 3 decades. It has encountered several surface and subsurface issues, including sand production, increasing water cut, and r

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Kryvyi, S., and H. Hoherchak. "ANALYZING NATURAL-LANGUAGE KNOWLEDGE IN UNCERTAINTY ON THE BASIS OF DESCRIPTION LOGICS." Kibernetyka ta Systemnyi Analiz, 2024, 32–47. http://dx.doi.org/10.34229/kca2522-9664.24.1.3.

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The article overviews the means for describing and formally analyzing natural- language text knowledge under uncertainty. We consider a family of classic attribute languages and logics based on them, their properties, problems, and solution tools. We also give an overview of propositional n-valued logics and fuzzy logics, their syntax, and semantics. Based on the considered logical constructions, we propose syntax and set-theoretic interpretation of n-valued description logic ALCQn that provides means for describing concept intersection, union, complement, value restrictions, and qualitative a

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Dai, Wang-Zhou, and Zhi-Hua Zhou. "Combining Logical Abduction and Statistical Induction: Discovering Written Primitives with Human Knowledge." Proceedings of the AAAI Conference on Artificial Intelligence 31, no. 1 (2017). http://dx.doi.org/10.1609/aaai.v31i1.11152.

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In many real tasks there are human knowledge expressed in logic formulae as well as data samples described by raw features (e.g., pixels, strings). It is popular to apply SRL or PILPtechniques to exploit human knowledge through learning of symbolic data, or statistical learning techniques to learn from the raw data samples; however, it is often desired to directly exploit these logic formulae on raw data processing, like human beings utilizing knowledge to guide perception. In this paper, we propose an approach, LASIN, which combines Logical Abduction and Statistical Induction. The LASIN appro

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Landes, Juergen, Soroush Rafiee Rad, and Jon Williamson. "Determining Maximal Entropy Functions for Objective Bayesian Inductive Logic." Journal of Philosophical Logic, October 13, 2022. http://dx.doi.org/10.1007/s10992-022-09680-6.

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AbstractAccording to the objective Bayesian approach to inductive logic, premisses inductively entail a conclusion just when every probability function with maximal entropy, from all those that satisfy the premisses, satisfies the conclusion. When premisses and conclusion are constraints on probabilities of sentences of a first-order predicate language, however, it is by no means obvious how to determine these maximal entropy functions. This paper makes progress on the problem in the following ways. Firstly, we introduce the concept of a limit in entropy and show that, if the set of probabilit

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De Boisboissel, G. "Արհեստական բանականություն. կիրառման նոր ձևերը և ազդեցությունը զորքերի մարտական կառավարման վրա / Artificial intelligence: new uses and impacts on military command and control". Հայկական բանակ / Armenian Army, 2024, 36–70. https://doi.org/10.61760/18290108-ehb24.2-36.

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General information and background on AI 1.1 The three battlefield revolutions The digitisation of the battlefield is a major revolution in combat, which needs to be assessed on a long-term scale as it will profoundly change military operating methods. First of all, it will mean that all the equipment deployed in the field will be interconnected with a tactical bubble that enables secure data exchanges to reduce the fog of war. What is already true for many armoured vehicles* will be true in the future for the dismounted soldier himself, who will be carrying advanced technologies. Processing t

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Mussinelli, Elena. "Editorial." TECHNE - Journal of Technology for Architecture and Environment, July 29, 2021, 10–15. http://dx.doi.org/10.36253/techne-11533.

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Every crisis at the same time reveals, forewarns and implies changes with cyclical trends that can be analyzed from different disciplinary perspectives, building scenarios to anticipate the future, despite uncertainties and risks. And the current crisis certainly appears as one of the most problematic of the modern era: recently, Luigi Ferrara, Director of the School of Design at the George Brown College in Toronto and of the connected Institute without Boundaries, highlighted how the pandemic has simply accelerated undergoing dynamics, exacerbating other crises – climatic, environmental, soci

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Pedersen, Isabel, and Kirsten Ellison. "Startling Starts: Smart Contact Lenses and Technogenesis." M/C Journal 18, no. 5 (2015). http://dx.doi.org/10.5204/mcj.1018.

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On 17 January 2013, Wired chose the smart contact lens as one of “7 Massive Ideas That Could Change the World” describing a Google-led research project. Wired explains that the inventor, Dr. Babak Parviz, wants to build a microsystem on a contact lens: “Using radios no wider than a few human hairs, he thinks these lenses can augment reality and incidentally eliminate the need for displays on phones, PCs, and widescreen TVs”. Explained further in other sources, the technology entails an antenna, circuits embedded into a contact lens, GPS, and an LED to project images on the eye, creating a virt

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Dissertations / Theses on the topic "Machine Learning, Learning from Constraints, First-Order Logic, Convex Logical Constraints"

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Giannini, Francesco. "On the Integration of Logic and Learning." Doctoral thesis, Università di Siena, 2019. http://hdl.handle.net/11365/1072603.

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A key point in the success of machine learning, and in particular deep learning, has been the availability of high-performance computing architectures allowing to process a large amount of data. However, this potentially prevents a wider application of machine learning in real world applications, where the collection of training data is often a slow and expensive process, requiring an extensive human intervention. This suggests to look at possible ways to overcome this limitation, for instance injecting prior knowledge into a learning problem to express some desired behaviors for the function

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Conference papers on the topic "Machine Learning, Learning from Constraints, First-Order Logic, Convex Logical Constraints"

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Mitterer, Felix, Christian Burmer, and Konstantin Schekotihin. "Automating Routing of Product Returns for Failure Analysis with Neuro-Symbolic AI." In ISTFA 2024. ASM International, 2024. http://dx.doi.org/10.31399/asm.cp.istfa2024p0047.

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Abstract Before failure analysis (FA) can start, a product must get from the customer to the correct location, which is not always trivial, especially in larger companies with many FA labs. Automating and optimizing this routing, therefore reducing manual labor, misrouting, and turnaround time, requires the development of problem-solving methods utilizing both explicit and implicit knowledge. The first type refers to known routing rules, e.g., based on lab equipment or certifications, whereas the second type must be induced from available data, e.g., by analyzing customer descriptions using ma

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Donadello, Ivan, Luciano Serafini, and Artur d'Avila Garcez. "Logic Tensor Networks for Semantic Image Interpretation." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/221.

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Semantic Image Interpretation (SII) is the task of extracting structured semantic descriptions from images. It is widely agreed that the combined use of visual data and background knowledge is of great importance for SII. Recently, Statistical Relational Learning (SRL) approaches have been developed for reasoning under uncertainty and learning in the presence of data and rich knowledge. Logic Tensor Networks (LTNs) are a SRL framework which integrates neural networks with first-order fuzzy logic to allow (i) efficient learning from noisy data in the presence of logical constraints, and (ii) re

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