Relevant bibliographies by topics / Embedding layer

Contents

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

Academic literature on the topic 'Embedding layer'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Embedding layer"

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Susanty, Meredita, and Sahrul Sukardi. "Perbandingan Pre-trained Word Embedding dan Embedding Layer untuk Named-Entity Recognition Bahasa Indonesia." Petir 14, no. 2 (2021): 247–57. http://dx.doi.org/10.33322/petir.v14i2.1164.

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Named-Entity Recognition (NER) is used to extract information from text by identifying entities such as the name of the person, organization, location, time, and other entities. Recently, machine learning approaches, particularly deep-learning, are widely used to recognize patterns of entities in sentences. Embedding, a process to convert text data into a number or vector of numbers, translates high dimensional vectors into relatively low-dimensional space. Embeddings make it easier to do machine learning on large inputs like sparse vectors representing words. The embedding process can be perf
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Lu, Ruili, Pengfei Jiao, Yinghui Wang, Huaming Wu, and Xue Chen. "Layer Information Similarity Concerned Network Embedding." Complexity 2021 (August 26, 2021): 1–10. http://dx.doi.org/10.1155/2021/2260488.

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Great achievements have been made in network embedding based on single-layer networks. However, there are a variety of scenarios and systems that can be presented as multiplex networks, which can reveal more interesting patterns hidden in the data compared to single-layer networks. In the field of network embedding, in order to project the multiplex network into the latent space, it is necessary to consider richer structural information among network layers. However, current methods for multiplex network embedding mostly focus on the similarity of nodes in each layer of the network, while igno
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Li, Saihan, and Bing Gong. "Word embedding and text classification based on deep learning methods." MATEC Web of Conferences 336 (2021): 06022. http://dx.doi.org/10.1051/matecconf/202133606022.

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Traditional manual text classification method has been unable to cope with the current huge amount of data volume. The improvement of deep learning technology also accelerates the technology of text classification. Based on this background, we presented different word embedding methods such as word2vec, doc2vec, tfidf and embedding layer. After word embedding, we demonstrated 8 deep learning models to classify the news text automatically and compare the accuracy of all the models, the model ‘2 layer GRU model with pretrained word2vec embeddings’ model got the highest accuracy. Automatic text c
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Liu, Haiyang, Yuki Endo, Jinho Lee, and Shunsuke Kamijo. "PREmbed: Balancing Conditional Generative Models with Embedding Pretraining and Regularization." Electronics 14, no. 2 (2025): 280. https://doi.org/10.3390/electronics14020280.

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Recent advancements in conditional generative models, e.g., Conditional Variational AutoEncoder (CVAE) and Conditional Denoising Diffusion Probabilistic Model (CDDPM), which utilize class-specific embeddings for generating images in specific classes, have demonstrated exceptional capabilities in producing high-quality images on balanced datasets. However, their performance decreases with imbalanced real-world data, affecting the fidelity and diversity of the generated images. This paper discusses the reasons and solutions for the imbalance issue in CVAE and CDDPM. By selectively reweighting th
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Sabbeh, Sahar F., and Heba A. Fasihuddin. "A Comparative Analysis of Word Embedding and Deep Learning for Arabic Sentiment Classification." Electronics 12, no. 6 (2023): 1425. http://dx.doi.org/10.3390/electronics12061425.

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Sentiment analysis on social media platforms (i.e., Twitter or Facebook) has become an important tool to learn about users’ opinions and preferences. However, the accuracy of sentiment analysis is disrupted by the challenges of natural language processing (NLP). Recently, deep learning models have proved superior performance over statistical- and lexical-based approaches in NLP-related tasks. Word embedding is an important layer of deep learning models to generate input features. Many word embedding models have been presented for text representation of both classic and context-based word embed
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He, Tao, Lianli Gao, Jingkuan Song, Xin Wang, Kejie Huang, and Yuanfang Li. "SNEQ: Semi-Supervised Attributed Network Embedding with Attention-Based Quantisation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4091–98. http://dx.doi.org/10.1609/aaai.v34i04.5832.

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Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many network analytics tasks. Moreover, the trained embeddings often require a significant amount of space to store, making storage and processing a challenge, especially as large-scale networks become more prevalent. In this paper, we present a novel semi-supervised network embedding and compression method, SNEQ, that is competitive with state-of-art embedding methods while being far more space- and time-efficient. SNEQ incorporates a novel quantisation method based on a self-attention layer that is
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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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Chowdhury, Shihabur Rahman, Sara Ayoubi, Reaz Ahmed, et al. "Multi-Layer Virtual Network Embedding." IEEE Transactions on Network and Service Management 15, no. 3 (2018): 1132–45. http://dx.doi.org/10.1109/tnsm.2018.2834315.

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Bao, Shudi, Tiantian Wang, Liliang Zhou, Guilan Dai, Geng Sun, and Jun Shen. "Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation." Applied Sciences 12, no. 15 (2022): 7369. http://dx.doi.org/10.3390/app12157369.

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Service recommendation is key to improving users’ online experience. The development of the Internet has accelerated the creation of many services, and whether users can obtain good experiences among the massive number of services mainly depends on the quality of service recommendation. It is commonly believed that deep learning has excellent nonlinear fitting ability in capturing the complex interactions between users and items. The advantage in learning intricacy relationships enables deep learning to become an important technology for present service recommendation. Recently, it is noticed
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Dissertations / Theses on the topic "Embedding layer"

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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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Dooley, Roger Benesh Greg. "Surface magnetism of Ni(001), Co(001), and Fe(001) an embedding Green function approach /." Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5051.

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Kilinc, Ismail Ozsel. "Graph-based Latent Embedding, Annotation and Representation Learning in Neural Networks for Semi-supervised and Unsupervised Settings." Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7415.

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Machine learning has been immensely successful in supervised learning with outstanding examples in major industrial applications such as voice and image recognition. Following these developments, the most recent research has now begun to focus primarily on algorithms which can exploit very large sets of unlabeled examples to reduce the amount of manually labeled data required for existing models to perform well. In this dissertation, we propose graph-based latent embedding/annotation/representation learning techniques in neural networks tailored for semi-supervised and uns
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Yoho, Jason Jon. "Physically-Based Realizable Modeling and Network Synthesis of Subscriber Loops Utilized in DSL Technology." Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/29738.

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Performance analysis of Digital Subscriber Line (DSL) technologies, which are implemented on existing telephone subscriber loops, is of vital importance to DSL service providers. This type of analysis requires accurate prediction of the local loop structure and precise identification of the cable parameters. These cables are the main components of the loop and are typically comprised of multi-conductor twisted pair type currently being used on existing telephone subscriber loops. This system identification problem was investigated through the application of single port measurements, with pr
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Li, Zhongliang. "Slim Embedding Layers for Recurrent Neural Language Models." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1531950458646138.

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Cheng, Spencer. "Punção ecoendoscópica de massas sólidas pancreáticas por técnica de pressão negativa versus capilaridade: estudo prospectivo e randomizado." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/5/5132/tde-22102018-111404/.

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INTRODUÇÃO: A punção aspirativa por agulha fina guiada por ecoendoscopia (PAAF-EE) é método consagrado para o diagnóstico de lesões sólidas de pâncreas. A aplicação de pressão negativa (PN) durante a PAAF-EE tem objetivo de manter o tecido junto à ponta cortante da agulha durante sua passagem através da lesão, resultando no desprendimento e retenção de células. A capilaridade (CP) é outra técnica que utiliza a retirada gradual do estilete durante a PAAF-EE e parece ser igualmente eficaz, além de evitar aspiração indesejada de hemácias e coágulos. OBJETIVO: Analisar o rendimento da ecopunção de
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Bopaiah, Jeevith. "A recurrent neural network architecture for biomedical event trigger classification." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/73.

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A “biomedical event” is a broad term used to describe the roles and interactions between entities (such as proteins, genes and cells) in a biological system. The task of biomedical event extraction aims at identifying and extracting these events from unstructured texts. An important component in the early stage of the task is biomedical trigger classification which involves identifying and classifying words/phrases that indicate an event. In this thesis, we present our work on biomedical trigger classification developed using the multi-level event extraction dataset. We restrict the scope of o
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Ekeberg, Tomas. "Flash Diffractive Imaging in Three Dimensions." Doctoral thesis, Uppsala universitet, Molekylär biofysik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-179643.

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During the last years we have seen the birth of free-electron lasers, a new type of light source ten billion times brighter than syncrotrons and able to produce pulses only a few femtoseconds long. One of the main motivations for building these multi-million dollar machines was the prospect of imaging biological samples such as proteins and viruses in 3D without the need for crystallization or staining. This thesis contains some of the first biological results from free-electron lasers. These results include 2D images, both of whole cells and of the giant mimivirus and also con- tains a 3D den
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Yung-SungLin and 林永崧. "Effects of embedding Au layer on the resistive switching characteristics of NiO memory devices." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/04981670917424214367.

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碩士<br>國立成功大學<br>材料科學及工程學系碩博士班<br>98<br>In this research, nickel oxide(NiO) thin films were deposited by reactive sputtering from nickel(Ni) target. Resistance random access memory(RRAM) devices were fabricated using NiO film without embedded Au layer(Al/NiO/Pt), NiO film without embedded Au layer after thermal treatment in oxygen ambient(Al/anneal_NiO/Pt), and NiO film with embedded Au layer(Al/NiO/Au/NiO/Pt). The mechanism of resistive switching is explored based on the current-voltage(I-V) and capacitance-voltage(C-V) measurements of the devices, in combination with material characterization
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Chia-TienChang and 張嘉恬. "A Context-Aware Chatbot Using Multi-Layer Embedding and NTN-based Pattern Matching for Elderly Care." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/x8nkf9.

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碩士<br>國立成功大學<br>資訊工程學系<br>105<br>According to demographic changes, the services designed for elderly are more needed and important. Previous studies show that there is a high relationship between loneliness, gloom, and disability for elderly. In order to increase the affective interaction and reduce the loneliness and isolation of elderly, this thesis develops a context-aware chatbot system for elderly care. In previous work, social media or community-based question-answer data were generally used to build the chatbots. This kind of data is different from our daily conversation and lack of con
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Books on the topic "Embedding layer"

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G, Ramirez, Pei K. C, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Discrete-layer piezoelectric plate and shell models for active tip-clearance control. National Aeronautics and Space Administration, Scientific and Technical Information Division, 1994.

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G, Ramirez, Pei K. C, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Discrete-layer piezoelectric plate and shell models for active tip-clearance control. National Aeronautics and Space Administration, Scientific and Technical Information Division, 1994.

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G, Ramirez, Pei K. C, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Discrete-layer piezoelectric plate and shell models for active tip-clearance control. National Aeronautics and Space Administration, Scientific and Technical Information Division, 1994.

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Wilson, Donald. Practical Kantian Ethics. Bloomsbury Publishing Plc, 2025. https://doi.org/10.5040/9781350501300.

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Reversing the usual order of interpretation, Donald Wilson reinterprets Kant’s moral theory through his later practical works offering a new “inner freedom” account informing obscure aspects of Kant’s formal moral philosophy and the practical focus of ideals of proper respect.This account transcends the narrow rational asceticism often associated with Kant’s view, embedding morality in our humanity, recognizing the vital role of emotion in moral life, and prioritizing framing moral commitments and questions of character over obedience to formal rules. In doing so, it makes community and collec
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Gaposchkin, M. Cecilia. Invisible Weapons. Cornell University Press, 2017. http://dx.doi.org/10.7591/cornell/9781501705151.001.0001.

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In 1098, three years into the First Crusade and after a brutal eight-month siege, the Franks captured the city of Antioch. Two days later, Muslim forces arrived with a relief army, and the victors became the besieged. Exhausted and ravaged by illness and hunger, the Franks were exhorted by their religious leaders to supplicate God, and for three days they performed a series of liturgical exercises, beseeching God through ritual prayer to forgive their sins and grant them victory. The following day, the Christian army, accompanied by bishops and priests reciting psalms and hymns, marched out of
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Book chapters on the topic "Embedding layer"

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Twiname, E. R., C. A. Randall, and G. L. Messing. "Embedding a Passive Material Layer in Low Temperature Co-Fired Packing." In Recent Developments in Electronic Materials and Devices. The American Ceramic Society, 2012. http://dx.doi.org/10.1002/9781118371107.ch28.

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Cranefield, Jocelyn, and Pak Yoong. "Embedding Professional Knowledge: The ‘Middle Layer’ in an Online Community Ecosystem." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02276-0_6.

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Wang, Qian, Guorong Wu, and Dinggang Shen. "Dual-Layer $$\ell _1$$ -Graph Embedding for Semi-supervised Image Labeling." In Patch-Based Techniques in Medical Imaging. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28194-0_6.

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Wang, Junxiang, Jiangqun Ni, and Jinwei Pan. "A High Performance Multi-layer Reversible Data Hiding Scheme Using Two-Step Embedding." In Digital Forensics and Watermarking. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32205-1_6.

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Pal, Sayan, Indranil Das, Suvajit Majumder, Amit Kr Gupta, and Indrajit Bhattacharya. "Embedding an Extra Layer of Data Compression Scheme for Efficient Management of Big-Data." In Advances in Intelligent Systems and Computing. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2247-7_71.

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Yin, Wen, Zhaoxia Yin, Xinpeng Zhang, and Bin Luo. "On the Robustness of “Robust Reversible Data Hiding Scheme Based on Two-Layer Embedding Strategy”." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0856-1_38.

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Posegay, Nick. "Some Notes on Melody: Saʿadya Gaon and Why ‘naghma’ Means ‘vowel’ in Judaeo-Arabic." In Interconnected Traditions: Semitic Languages, Literatures, Cultures—A Festschrift for Geoffrey Khan. Open Book Publishers, 2025. https://doi.org/10.11647/obp.0464.10.

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The article explores the historical and linguistic contexts behind the use of the term naghma in medieval Judaeo-Arabic to signify ‘vowel’, a meaning distinct from its Classical Arabic usage denoting ‘melody’, ‘tone’, or ‘sound’. Focusing on Saʿadya Gaon’s work, particularly his Al-Qawl fī al-Nagham and his Hebrew grammar, the study highlights how the term evolved in Jewish linguistic traditions. Saʿadya used naghma to describe the seven vowels of Tiberian Hebrew, connecting linguistic analysis with musicological concepts prevalent in Arabic and Jewish intellectual circles of the medieval peri
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Habekost, Jan-Gerrit, Erik Strahl, Philipp Allgeuer, Matthias Kerzel, and Stefan Wermter. "CycleIK: Neuro-inspired Inverse Kinematics." In Artificial Neural Networks and Machine Learning – ICANN 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44207-0_38.

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AbstractThe paper introduces CycleIK, a neuro-robotic approach that wraps two novel neuro-inspired methods for the inverse kinematics (IK) task—a Generative Adversarial Network (GAN), and a Multi-Layer Perceptron architecture. These methods can be used in a standalone fashion, but we also show how embedding these into a hybrid neuro-genetic IK pipeline allows for further optimization via sequential least-squares programming (SLSQP) or a genetic algorithm (GA). The models are trained and tested on dense datasets that were collected from random robot configurations of the new Neuro-Inspired COLl
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Khellat-Kihel, Souad, Zhenan Sun, and Massimo Tistarelli. "An Hybrid Attention-Based System for the Prediction of Facial Attributes." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_9.

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AbstractRecent research on face analysis has demonstrated the richness of information embedded in feature vectors extracted from a deep convolutional neural network. Even though deep learning achieved a very high performance on several challenging visual tasks, such as determining the identity, age, gender and race, it still lacks a well grounded theory which allows to properly understand the processes taking place inside the network layers. Therefore, most of the underlying processes are unknown and not easy to control. On the other hand, the human visual system follows a well understood proc
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Pflueger, Maximilian, David J. Tena Cucala, and Egor V. Kostylev. "GNNQ: A Neuro-Symbolic Approach to Query Answering over Incomplete Knowledge Graphs." In The Semantic Web – ISWC 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19433-7_28.

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AbstractReal-world knowledge graphs (KGs) are usually incomplete—that is, miss some facts representing valid information. So, when applied to such KGs, standard symbolic query engines fail to produce answers that are expected but not logically entailed by the KGs. To overcome this issue, state-of-the-art ML-based approaches first embed KGs and queries into a low-dimensional vector space, and then produce query answers based on the proximity of the candidate entity and the query embeddings in the embedding space. This allows embedding-based approaches to obtain expected answers that are not log
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Conference papers on the topic "Embedding layer"

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Wu, Peng, Wei Huang, Bo Liu, and Zhenlin Liu. "iProm-EC: Identifying Promoters Using Embedding Layer and Convolutional Neural Network." In 2024 5th International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI). IEEE, 2024. https://doi.org/10.1109/iccbd-ai65562.2024.00035.

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Meng, Liang, and Yang Minxuan. "Research on medical images security based on multi-layer embedding and encryption." In 2024 6th International Conference on Natural Language Processing (ICNLP). IEEE, 2024. http://dx.doi.org/10.1109/icnlp60986.2024.10692382.

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Sooksatra, Korn, Alejandro Rodriguez Perez, and Pablo Rivas. "Resilient Word-Embedding Alignment for BERT with Character-Based Layer in Noisy Environments." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651468.

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Kwok, Chin Yuen, Jia Qi Yip, and Eng Siong Chng. "Continual Learning With Embedding Layer Surgery and Task-Wise Beam Search Using Whisper." In 2024 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2024. https://doi.org/10.1109/slt61566.2024.10832213.

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Murugesan, R., Anil Kumar M, Hemanth Kumar C, Sasidhar U, and Shiva Kumar G. "Analyzing the Impact of Meteorological Factors on New Energy Power Prediction using Capsule Networks with Hybrid Embedding Layer." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11070717.

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Karia, Tanuj, Giacomo Lastrucci, and Artur M. Schweidtmann. "Kolmogorov Arnold Networks (KANs) as surrogate models for global process optimization." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.195815.

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Surrogate models are widely used to improve the tractability of process optimization. Some commonly used surrogate models are obtained via machine learning such as multi-layer perceptrons (MLPs), Gaussian processes, and decision trees. Recently, a new class of machine learning models named Kolmogorov Arnold Networks (KANs) have been proposed. Broadly, KANs are similar to MLPs, yet they are based on the Kolmogorov representation theorem instead of the universal approximation theorem for the MLPs. Compared to MLPs, it was reported that KANs require significantly fewer parameters to approximate a
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Vo, Nam, and James Hays. "Generalization in Metric Learning: Should the Embedding Layer Be Embedding Layer?" In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2019. http://dx.doi.org/10.1109/wacv.2019.00068.

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Rodriguez, Alejandro, Korn Sooksatra, Pablo Rivas, et al. "An Empirical Analysis Towards Replacing Vocabulary-Rigid Embeddings by a Vocabulary-Free Mechanism." In LatinX in AI at International Conference on Machine Learning 2023. Journal of LatinX in AI Research, 2023. http://dx.doi.org/10.52591/lxai202307237.

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This paper addresses the limitations of subword based models in NLP by aligning the word embedding layer of a vocabulary-rigid transformer model to a vocabulary-free one. In order to do so, a CNN is trained to mimic the word embeddings layer of a BERT model, using a sequence of byte tokens as input. The study compares cosine-based and Euclidean-based loss functions for training the student network and finds better results with cosine-based metrics. The research contributes techniques for re-training transformer embedding layers and provides insights into loss function selection. The findings h
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Pei, Zhongyue, Xi Zhang, Fangwei Zhang, and Binxing Fang. "Attributed Multi-layer Network Embedding." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8621900.

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Chowdhury, Shihabur Rahman, Sara Ayoubi, Reaz Ahmed, et al. "MULE: Multi-layer virtual network embedding." In 2017 13th International Conference on Network and Service Management (CNSM). IEEE, 2017. http://dx.doi.org/10.23919/cnsm.2017.8256005.

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