Relevant bibliographies by topics / Embedding techniques

Contents

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

Academic literature on the topic 'Embedding techniques'

Author: Grafiati
Published: 17 January 2024
Last updated: 25 July 2025

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Journal articles on the topic "Embedding techniques"

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Meyer, Francois, der Merwe Brink van, and Dirko Coetsee. "Learning Concept Embeddings from Temporal Data." JUCS - Journal of Universal Computer Science 24, no. (10) (2018): 1378–402. https://doi.org/10.3217/jucs-024-10-1378.

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Word embedding techniques can be used to learn vector representations of concepts from temporal datasets. Previous attempts to do this amounted to appling word embedding techniques to event sequences. We propose a concept embedding model that extends existing word embedding techniques to take time into account by explicitly modelling the time between concept occurrences. The model is implemented and evaluated using medical temporal data. It is found that incorporating time into the learning algorithm can improve the quality of the resulting embeddings, as measured by an existing methodological
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Duong, Chi Thang, Trung Dung Hoang, Hongzhi Yin, Matthias Weidlich, Quoc Viet Hung Nguyen, and Karl Aberer. "Scalable robust graph embedding with Spark." Proceedings of the VLDB Endowment 15, no. 4 (2021): 914–22. http://dx.doi.org/10.14778/3503585.3503599.

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Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to large graphs. While several techniques to scale graph embedding using compute clusters have been proposed, they require continuous communication between the compute nodes and cannot handle node failure. We therefore propose a framework for scalable and robust graph embedding based on the MapReduce model, which can distribute any existing embeddin
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Li, Pandeng, Yan Li, Hongtao Xie, and Lei Zhang. "Neighborhood-Adaptive Structure Augmented Metric Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1367–75. http://dx.doi.org/10.1609/aaai.v36i2.20025.

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Most metric learning techniques typically focus on sample embedding learning, while implicitly assume a homogeneous local neighborhood around each sample, based on the metrics used in training ( e.g., hypersphere for Euclidean distance or unit hyperspherical crown for cosine distance). As real-world data often lies on a low-dimensional manifold curved in a high-dimensional space, it is unlikely that everywhere of the manifold shares the same local structures in the input space. Besides, considering the non-linearity of neural networks, the local structure in the output embedding space may not
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Jadon, Anil Kumar, and Suresh Kumar. "Enhancing emotion detection with synergistic combination of word embeddings and convolutional neural networks." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1933. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1933-1941.

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Recognizing emotions in textual data is crucial in a wide range of natural language processing (NLP) applications, from consumer sentiment research to mental health evaluation. The word embedding techniques play a pivotal role in text processing. In this paper, the performance of several well-known word embedding methods is evaluated in the context of emotion recognition. The classification of emotions is further enhanced using a convolutional neural network (CNN) model because of its propensity to capture local patterns and its recent triumphs in text-related tasks. The integration of CNN wit
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Anil, Kumar Jadon Suresh Kumar. "Enhancing emotion detection with synergistic combination of word embeddings and convolutional neural networks." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1933–41. https://doi.org/10.11591/ijeecs.v35.i3.pp1933-1941.

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Recognizing emotions in textual data is crucial in a wide range of natural language processing (NLP) applications, from consumer sentiment research to mental health evaluation. The word embedding techniques play a pivotal role in text processing. In this paper, the performance of several well-known word embedding methods is evaluated in the context of emotion recognition. The classification of emotions is further enhanced using a convolutional neural network (CNN) model because of its propensity to capture local patterns and its recent triumphs in text-related tasks. The integration of CNN wit
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Mao, Yuqing, and Kin Wah Fung. "Use of word and graph embedding to measure semantic relatedness between Unified Medical Language System concepts." Journal of the American Medical Informatics Association 27, no. 10 (2020): 1538–46. http://dx.doi.org/10.1093/jamia/ocaa136.

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Abstract Objective The study sought to explore the use of deep learning techniques to measure the semantic relatedness between Unified Medical Language System (UMLS) concepts. Materials and Methods Concept sentence embeddings were generated for UMLS concepts by applying the word embedding models BioWordVec and various flavors of BERT to concept sentences formed by concatenating UMLS terms. Graph embeddings were generated by the graph convolutional networks and 4 knowledge graph embedding models, using graphs built from UMLS hierarchical relations. Semantic relatedness was measured by the cosin
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Ma, Xingyu, and Bin Yao. "Embedding Numerical Features and Meta-Features in Tabular Deep Learning." Information Technology and Control 54, no. 2 (2025): 662–81. https://doi.org/10.5755/j01.itc.54.2.39134.

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Tabular data is ubiquitous in real-world applications, and an increasing number of deep learning approaches have been developed for tabular data prediction. Among these approaches, embedding techniques serve as both a common and essential component. However, the design of tabular embedding paradigms remains relatively limited, and there is a lack of systematic evaluation regarding the performance of many existing methods in specific scenarios. In this paper, we focus on embedding numerical features and meta-features. To enrich the embedding methods for numerical features, we propose an orderin
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Tan, Eugene, Shannon Algar, Débora Corrêa, Michael Small, Thomas Stemler, and David Walker. "Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology." Chaos: An Interdisciplinary Journal of Nonlinear Science 33, no. 3 (2023): 032101. http://dx.doi.org/10.1063/5.0137223.

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Delay embedding methods are a staple tool in the field of time series analysis and prediction. However, the selection of embedding parameters can have a big impact on the resulting analysis. This has led to the creation of a large number of methods to optimize the selection of parameters such as embedding lag. This paper aims to provide a comprehensive overview of the fundamentals of embedding theory for readers who are new to the subject. We outline a collection of existing methods for selecting embedding lag in both uniform and non-uniform delay embedding cases. Highlighting the poor dynamic
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SAMANTA, SAURAV. "NONCOMMUTATIVITY FROM EMBEDDING TECHNIQUES." Modern Physics Letters A 21, no. 08 (2006): 675–89. http://dx.doi.org/10.1142/s0217732306019037.

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We apply the embedding method of Batalin–Tyutin for revealing noncommutative structures in the generalized Landau problem. Different types of noncommutativity follow from different gauge choices. This establishes a duality among the distinct algebras. An alternative approach is discussed which yields equivalent results as the embedding method. We also discuss the consequences in the Landau problem for a non-constant magnetic field.
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Alkaabi, Hussein, Ali Kadhim Jasim, and Ali Darroudi. "From Static to Contextual: A Survey of Embedding Advances in NLP." PERFECT: Journal of Smart Algorithms 2, no. 2 (2025): 57–66. https://doi.org/10.62671/perfect.v2i2.77.

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Embedding techniques have been a cornerstone of Natural Language Processing (NLP), enabling machines to represent textual data in a form that captures semantic and syntactic relationships. Over the years, the field has witnessed a significant evolution—from static word embeddings, such as Word2Vec and GloVe, which represent words as fixed vectors, to dynamic, contextualized embeddings like BERT and GPT, which generate word representations based on their surrounding context. This survey provides a comprehensive overview of embedding techniques, tracing their development from early methods to st
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Dissertations / Theses on the topic "Embedding techniques"

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Thanh, Trung Huynh. "On leverage embedding techniques for network alignment." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/416055.

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Networks are natural but powerful structures that capture relationships between different entities in many domains, such as social networks, citation networks, bioinformatic networks. In many applications that require multiple networks analysis, network alignment, the task of recognizing node correspondence across different networks, plays an important role. A wellknown application of network alignment is to identify which accounts in different social networks belong to the same person. Given the appeal of network alignment, there is a rich body of researches that aims to tackle this problem.
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Chochlidakis, Georgios. "Mobility-aware virtual network embedding techniques for next-generation mobile networks." Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/mobilityaware-virtual-network-embedding-techniques-for-nextgeneration-mobile-networks(174e714f-2a4a-447a-bcd5-d526170377fd).html.

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Network virtualisation has become one of the most prominent solutions for sus-tainability towards the dramatic increase of data demand in next-generation mobile networks. In addition, apart from increasing the overall infrastructure utilisation, it also greatly improves the manageability, the scalability and the robustness of the network. In order to allow multiple virtual networks to coexist in the same substrate network, the need for efficient network sharing techniques is imperative. The main purpose of this work is to provide a holistic optimization framework for vir-tual network embedding
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Obidallah, Waeal. "Multi-Layer Web Services Discovery using Word Embedding and Clustering Techniques." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/41840.

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Web services discovery is the process of finding the right Web services that best match the end-users’ functional and non-functional requirements. Artificial intelligence, natural language processing, data mining, and text mining techniques have been applied by researchers in Web services discovery to facilitate the process of matchmaking. This thesis contributes to the area of Web services discovery and recommendation, adopting the Design Science Research Methodology to guide the development of useful knowledge, including design theory and artifacts. The lack of a comprehensive review of We
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Ojike, Uzoma. "Combining tools and techniques for embedding an ecosystem approach in spatial planning." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/11196.

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Despite the attention garnered by sustainability in the last three decades and the advances in its tools and techniques, we are no closer to attaining sustainability now than we were at the start. This elusiveness has been attributed to the lack of a clearly defined global method for evaluating sustainability and poor integration into sector, national and international policies and decision-making, amongst others. A clear limitation observed in most concepts/methods is their inability to integrate effectively ecological, economic and social sustainability during assessment. Rather, there is a
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Alain, Martin. "A compact video representation format based on spatio-temporal linear embedding and epitome." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S001/document.

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L'efficacité des services de compression vidéo est de nos jours un enjeu essentiel, et est appelé à le devenir d'autant plus dans le futur, comme l'indique la croissance constante du trafic vidéo et la production de nouveaux formats tels que la vidéo à haute résolution, à gamme de couleur ou dynamique étendues, ou encore à fréquence d'images augmentée. Le standard MPEG HEVC est aujourd'hui un des schémas de compression les plus efficaces, toutefois, il devient nécessaire de proposer de nouvelles méthodes originales pour faire face aux nouveaux besoins de compression. En effet, les principes de
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Raahemi, Mohammad. "Intelligent Prediction of Stock Market Using Text and Data Mining Techniques." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40934.

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The stock market undergoes many fluctuations on a daily basis. These changes can be challenging to anticipate. Understanding such volatility are beneficial to investors as it empowers them to make inform decisions to avoid losses and invest when opportunities are predicted to earn funds. The objective of this research is to use text mining and data mining techniques to discover the relationship between news articles and stock prices fluctuations. There are a variety of sources for news articles, including Bloomberg, Google Finance, Yahoo Finance, Factiva, Thompson Routers, and Twitter. In our
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Ferey, Gaspard. "Higher-Order Confluence and Universe Embedding in the Logical Framework." Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG032.

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La multiplicité des systèmes formels a mis en évidence la nécessité d'un socle logique commun dans lequel les formalismes logiques pourraient être exprimés.L'enjeu principal de ce manuscrit est la définition de techniques d'encodages reposant sur la réécriture de termes et capables de réprésenter les fonctionnalités avancées des systèmes de types modernes.Nos encodages s'appuieront sur le lambda-Pi calcul modulo, un système de types dépendants, communément utilisé comme cadre logique, étendu ici par de laréécriture d'ordre supérieur. On s'intéresse, dans une première partie, aux critères de co
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Senjean, Bruno. "Development of new embedding techniques for strongly correlated electrons : from in-principle-exact formulations to practical approximations." Thesis, Strasbourg, 2018. http://www.theses.fr/2018STRAF035/document.

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Cette thèse traite du développement et de l’implémentation de nouvelles méthodes visant à décrire la corrélation électronique forte dans les molécules et les solides. Après avoir introduit l’état de l’art des méthodes utilisées en chimie quantique et en physique de la matière condensée, une nouvelle méthode hybride combinant théorie de la fonction d’onde et théorie de la fonctionnelle de la densité (DFT) est présentée et s’intitule “site-occupation embedding theory” (SOET). Celle-ci est appliquée au modèle de Hubbard à une dimension. Ensuite, le problème du gap fondamental est revisité en DFT
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Paoli, Roberto. "Cell culture interfaces for different organ-on-chip applications: from photolithography to rapid-prototyping techniques with sensor embedding." Doctoral thesis, Universitat de Barcelona, 2019. http://hdl.handle.net/10803/668376.

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Despite the last 60 years have seen major advances in many scientific and technological inputs of drug Research and Development, the number of new molecules hitting the market per billion US dollars of R&D spending has been declined steadily during the same period. The current scenario highlights the need for new research tools to enable reduce costly animal and clinical trials while providing a better prediction about drug efficacy and security in humans A recent emerging approach to improve the current models is emerging from the field of microfluidics, which studies systems that process o
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Antici, Francesco. "Advanced techniques for cross-language annotation projection in legal texts." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23884/.

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Nowadays, the majority of the services we benefit from, are provided online and their use is regulated by the acceptance to the terms of service by the users. All our data are handled accordingly with the clauses of such document and all our behaviours must comply with it. Given so, it would be very useful to find automated techniques to ensure fairness of the document or inform the users about possible threats. The focus of this work, is to create resources aimed to the development of such tools in languages other than English, which may lack in linguistic resources and annotated corpus. T
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Books on the topic "Embedding techniques"

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Guattery, Stephen. Graph embedding techniques for bounding condition numbers of incomplete factor preconditioners. Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1997.

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Center, Langley Research, ed. Graph embedding techniques for bounding condition numbers of incomplete factor preconditioners. National Aeronautics and Space Administration, Langley Research Center, 1997.

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Lin, Yan. Novel Techniques in Recovering, Embedding, and Enforcing Policies for Control-Flow Integrity. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73141-0.

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Kuixiong, Gao, ed. Polyethylene glycol as an embedment for microscopy and histochemistry. CRC Press, 1993.

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Newman, G. R. Resin microscopy and on-section immunocytochemistry. Springer-Verlag, 1993.

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Leahy, Siobhán. Embedding Formative Assessment: Practical Techniques for K-12 Classrooms. Solution Tree, 2024.

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Jr, Wiliam Dylan, and Siobhan Leahy. Embedding Formative Assessment: Practical Techniques for K-12 Classrooms. Learning Sciences International, 2015.

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Embedding Formative Assessment: Practical Techniques for K-12 Classrooms. Learning Sciences International, 2015.

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Behrens, Stefan, Boldizsar Kalmar, Min Hoon Kim, Mark Powell, and Arunima Ray, eds. The Disc Embedding Theorem. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198841319.001.0001.

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The disc embedding theorem provides a detailed proof of the eponymous theorem in 4-manifold topology. The theorem, due to Michael Freedman, underpins virtually all of our understanding of 4-manifolds in the topological category. Most famously, this includes the 4-dimensional topological Poincaré conjecture. Combined with the concurrent work of Simon Donaldson, the theorem reveals a remarkable disparity between the topological and smooth categories for 4-manifolds. A thorough exposition of Freedman’s proof of the disc embedding theorem is given, with many new details. A self-contained account o
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Lin, Yan. Novel Techniques in Recovering, Embedding, and Enforcing Policies for Control-Flow Integrity. Springer International Publishing AG, 2021.

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Book chapters on the topic "Embedding techniques"

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Armbruster, Barbara L., and Edward Kellenberger. "Low-Temperature Embedding." In Ultrastructure Techniques for Microorganisms. Springer US, 1986. http://dx.doi.org/10.1007/978-1-4684-5119-1_10.

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Maddalena, L., I. Manipur, M. Manzo, and M. R. Guarracino. "On Whole-Graph Embedding Techniques." In Trends in Biomathematics: Chaos and Control in Epidemics, Ecosystems, and Cells. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73241-7_8.

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Hayat, M. A. "Rinsing, Dehydration and Embedding." In Principles and Techniques of Electron Microscopy. Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-09857-6_2.

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Reynoso Hernández, J. Apolinar, and Manuel Alejandro Pulido Gaytan. "Microwave De-embedding." In Principles and Applications of Vector Network Analyzer Calibration Techniques. River Publishers, 2024. http://dx.doi.org/10.1201/9788770046350-3.

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Aldrich, H. C., and H. H. Mollenhauer. "Secrets of Successful Embedding, Sectioning, and Imaging." In Ultrastructure Techniques for Microorganisms. Springer US, 1986. http://dx.doi.org/10.1007/978-1-4684-5119-1_4.

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Puel, Laurence. "Embedding with patterns and associated recursive path ordering." In Rewriting Techniques and Applications. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/3-540-51081-8_120.

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Bartal, Yair, Douglas E. Carroll, Adam Meyerson, and Ofer Neiman. "Bandwidth and Low Dimensional Embedding." In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22935-0_5.

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Dey, Pranab. "Embedding of Tissue in Histopathology." In Basic and Advanced Laboratory Techniques in Histopathology and Cytology. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6616-3_3.

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Dey, Pranab. "Embedding of Tissue in Histopathology." In Basic and Advanced Laboratory Techniques in Histopathology and Cytology. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8252-8_3.

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Bockmayr, Alexander. "Embedding OR Techniques in Constraint Logic Programming." In Operations Research ’92. Physica-Verlag HD, 1993. http://dx.doi.org/10.1007/978-3-662-12629-5_74.

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Conference papers on the topic "Embedding techniques"

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Zhang, Guodong, Zhuo Jin, Tianyu Yao, and Jiawei Qin. "ECET: Enhanced Vulnerability Detection through Code Embedding Techniques." In 2024 Sixth International Conference on Next Generation Data-driven Networks (NGDN). IEEE, 2024. http://dx.doi.org/10.1109/ngdn61651.2024.10744066.

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Johny, Alphonsa, and P. P. Joby. "Deriving Insights from Graph Databases using Embedding Techniques." In 2024 International Conference on Expert Clouds and Applications (ICOECA). IEEE, 2024. http://dx.doi.org/10.1109/icoeca62351.2024.00025.

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Salboukh, Fatemeh, Hesam Saki, and Lance Fiondella. "Optimizing Software Defect Classification with Locally Linear Embedding Techniques." In 2025 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2025. https://doi.org/10.1109/rams48127.2025.10935043.

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Soundariya, S., S. Ramakrishnan, S. K. Devadharshini, S. Madhumita, and L. Promodini. "Cross Domain Recommendation System Using Graph Embedding and Attention Techniques." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725254.

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Prabha, Sneh, Aditya Yadav, Aman Yadav, Akash Kumar, and Manu Tyagi. "Toxic Comments Classification using Machine Learning and Word Embedding Techniques." In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). IEEE, 2025. https://doi.org/10.1109/cictn64563.2025.10932491.

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K V, Nanda, Vidyalakshmi K, and N. Achyutha Prasad. "Comprehensive Analysis of Virtual Network Embedding: Techniques and Future Directions." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11070492.

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Reddy Anreddy, Shanmukha, K. Sri Ram Charan, N. Naga Sai, Thanmai Gaddam, and Peeta Basa Pati. "Embedding-Driven Legal Outcome Prediction: Explainable AI and Dimensionality Reduction Techniques." In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE, 2025. https://doi.org/10.1109/icicacs65178.2025.10967905.

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Pallaw, Vijay Krishna, Kamred Udham Singh, and Ashok Kumar Yadav. "Performance Analysis of Different Data Embedding Techniques: Cryptography, Steganography and Watermarking." In 2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI). IEEE, 2025. https://doi.org/10.1109/iccsai64074.2025.11063976.

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Kalaivani, K. S., C. S. Kanimozhi Selvi, N. Sandeep, T. Vasanthasri, and S. VarshiniShilin. "A Comparison of Word Embedding Techniques for Stress Identification using Machine Learning." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724149.

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Attri, Vikas, Ankush Kapoor, Simranjeet Singh, and Payal Sharma. "Scientometric Analysis and Implementation of Word Embedding Techniques in Natural Language Processing." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10840467.

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Reports on the topic "Embedding techniques"

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Datta, Bithin, Jean Beegle, M. Kavvas, and Gerald Orlob. Development of an expert-system embedding pattern-recognition techniques for pollution-source identification. Report for 30 September 1987-29 November 1989. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/6855981.

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Buurma, Andrew W., and Roberto G. Rojas. Aerospace Sensor Component and Subsystem Investigation And Innovation-2 Component Exploration and Development (ASCII-2 CED). Delivery Order 0002: Volume 3 - Reconfigurable Aperture Antenna Virtual Prototyping (General Techniques for De-Embedding RF Passive Devices). Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada445437.

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