Relevant bibliographies by topics / Neural symbolic learning

Contents

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

Academic literature on the topic 'Neural symbolic learning'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Neural symbolic learning"

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Shavlik, Jude W. "Combining symbolic and neural learning." Machine Learning 14, no. 3 (1994): 321–31. http://dx.doi.org/10.1007/bf00993982.

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Li, Xin, Chengli Zhao, Xue Zhang, and Xiaojun Duan. "Symbolic Neural Ordinary Differential Equations." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 18511–19. https://doi.org/10.1609/aaai.v39i17.34037.

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Differential equations are widely used to describe complex dynamical systems with evolving parameters in nature and engineering. Effectively learning a family of maps from the parameter function to the system dynamics is of great significance. In this study, we propose a novel learning framework of symbolic continuous-depth neural networks, termed Symbolic Neural Ordinary Differential Equations (SNODEs), to effectively and accurately learn the underlying dynamics of complex systems. Specifically, our learning framework comprises three stages: initially, pre-training a predefined symbolic neura
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Borges, Rafael V., Artur S. d'Avila Garcez, and Luis C. Lamb. "A neural-symbolic perspective on analogy." Behavioral and Brain Sciences 31, no. 4 (2008): 379–80. http://dx.doi.org/10.1017/s0140525x08004482.

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AbstractThe target article criticises neural-symbolic systems as inadequate for analogical reasoning and proposes a model of analogy as transformation (i.e., learning). We accept the importance of learning, but we argue that, instead of conflicting, integrated reasoning and learning would model analogy much more adequately. In this new perspective, modern neural-symbolic systems become the natural candidates for modelling analogy.
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Fatima, Tuba, and Dr Rehan Muhammad. "The Impact of Neuro-Symbolic AI on Cognitive Linguistics." ACADEMIA International Journal for Social Sciences 4, no. 3 (2025): 455–66. https://doi.org/10.63056/acad.004.03.0386.

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Neuro-Symbolic Artificial Intelligence (AI) is indeed a fascinating domain, merging the structured reasoning of symbolic methods with the learning capabilities of neural networks. Its long-standing history reflects its significance in advancing AI towards achieving more robust and interpretable solutions. Neuro-symbolic AI is such an exciting and transformative field, as it combines the structured reasoning of symbolic AI with the adaptability and learning capabilities of neural networks. Your summary elegantly captures the breadth and depth of this growing discipline. The focus on representat
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Tian, Jidong, Yitian Li, Wenqing Chen, Liqiang Xiao, Hao He, and Yaohui Jin. "Weakly Supervised Neural Symbolic Learning for Cognitive Tasks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (2022): 5888–96. http://dx.doi.org/10.1609/aaai.v36i5.20533.

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Despite the recent success of end-to-end deep neural networks, there are growing concerns about their lack of logical reasoning abilities, especially on cognitive tasks with perception and reasoning processes. A solution is the neural symbolic learning (NeSyL) method that can effectively utilize pre-defined logic rules to constrain the neural architecture making it perform better on cognitive tasks. However, it is challenging to apply NeSyL to these cognitive tasks because of the lack of supervision, the non-differentiable manner of the symbolic system, and the difficulty to probabilistically
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Pacheco, Maria Leonor, and Dan Goldwasser. "Modeling Content and Context with Deep Relational Learning." Transactions of the Association for Computational Linguistics 9 (February 2021): 100–119. http://dx.doi.org/10.1162/tacl_a_00357.

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Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies. Neural-symbolic representations have emerged as a way to combine the reasoning capabilities of symbolic methods, with the expressiveness of neural networks. However, most of the existing frameworks for combining neural and symbolic representations have been designed for classic relational learning tasks that work over a universe of symbolic entities and relations. In this paper, we present DRaiL, an open-source declarative framework for specifying deep r
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Winters, Thomas, Giuseppe Marra, Robin Manhaeve, and Luc De Raedt. "DeepStochLog: Neural Stochastic Logic Programming." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 10090–100. http://dx.doi.org/10.1609/aaai.v36i9.21248.

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Recent advances in neural-symbolic learning, such as DeepProbLog, extend probabilistic logic programs with neural predicates. Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard. We propose DeepStochLog, an alternative neural-symbolic framework based on stochastic definite clause grammars, a kind of stochastic logic program. More specifically, we introduce neural grammar rules into stochastic definite clause grammars to create a framework that can be trained end-to-end. We show that infere
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Akanbi, Olawale Basheer, and Hameed Olamilekan Ajasa. "Predicting Food Prices in Nigeria Using Machine Learning: Symbolic Regression." International Journal of Research and Innovation in Applied Science X, no. VI (2025): 979–95. https://doi.org/10.51584/ijrias.2025.10060074.

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The aim of this study is to predict the prices of local rice, beans, and Garri in the South West (SW) and North Central (NC), Nigeria using economic indicators such as exchange rate, inflation rate, crude oil price, past one month price (lag 1) and past five-month price (lag 5) of the food prices as the predictor variables. The data used were extracted from the website of the National Bureau of Statistics from January 2017 to July 2024. The data were split into training set and testing set. The study proposed four machine learning techniques; random forest, decision tree, neural network and sy
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Modak, Sadanand, Noah Tobias Patton, Isil Dillig, and Joydeep Biswas. "SYNAPSE: SYmbolic Neural-Aided Preference Synthesis Engine." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 26 (2025): 27529–37. https://doi.org/10.1609/aaai.v39i26.34965.

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This paper addresses the problem of preference learning, which aims to align robot behaviors through learning user-specific preferences (e.g. “good pull-over location”) from visual demonstrations. Despite its similarity to learning factual concepts (e.g. “red door”), preference learning is a fundamentally harder problem due to its subjective nature and the paucity of person-specific training data. We address this problem using a novel framework called SYNAPSE, which is a neuro-symbolic approach designed to efficiently learn preferential concepts from limited data. SYNAPSE represents preference
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Shavlik, Jude W., Raymond J. Mooney, and Geoffrey G. Towell. "Symbolic and neural learning algorithms: An experimental comparison." Machine Learning 6, no. 2 (1991): 111–43. http://dx.doi.org/10.1007/bf00114160.

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Dissertations / Theses on the topic "Neural symbolic learning"

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Xiao, Chunyang. "Neural-Symbolic Learning for Semantic Parsing." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0268/document.

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Notre but dans cette thèse est de construire un système qui réponde à une question en langue naturelle (NL) en représentant sa sémantique comme une forme logique (LF) et ensuite en calculant une réponse en exécutant cette LF sur une base de connaissances. La partie centrale d'un tel système est l'analyseur sémantique qui transforme les questions en formes logiques. Notre objectif est de construire des analyseurs sémantiques performants en apprenant à partir de paires (NL, LF). Nous proposons de combiner des réseaux neuronaux récurrents (RNN) avec des connaissances préalables symboliques exprim
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Xiao, Chunyang. "Neural-Symbolic Learning for Semantic Parsing." Electronic Thesis or Diss., Université de Lorraine, 2017. http://www.theses.fr/2017LORR0268.

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Notre but dans cette thèse est de construire un système qui réponde à une question en langue naturelle (NL) en représentant sa sémantique comme une forme logique (LF) et ensuite en calculant une réponse en exécutant cette LF sur une base de connaissances. La partie centrale d'un tel système est l'analyseur sémantique qui transforme les questions en formes logiques. Notre objectif est de construire des analyseurs sémantiques performants en apprenant à partir de paires (NL, LF). Nous proposons de combiner des réseaux neuronaux récurrents (RNN) avec des connaissances préalables symboliques exprim
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Chen, Hsinchun. "Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms." Wiley Periodicals, Inc, 1995. http://hdl.handle.net/10150/106427.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence- based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newe
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Chen, Hsinchun, P. Buntin, Linlin She, S. Sutjahjo, C. Sommer, and D. Neely. "Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing." IEEE, 1994. http://hdl.handle.net/10150/105472.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>For our research, we investigated a different problem-solving scenario called game playing, which is unstructured, complex, and seldom-studied. We considered several real-life game-playing scenarios and decided on greyhound racing. The large amount of historical information involved in the search poses a challenge for both human experts and machine-learning algorithms. The questions then become: Can machine-learning techniques reduce the uncertainty in a complex game-playing scenario? Can these methods outperform human ex
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Galassi, Andrea <1992&gt. "Deep Networks and Knowledge: from Rule Learning to Neural-Symbolic Argument Mining." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9842/1/TESI_PHDv2.pdf.

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Deep Learning has revolutionized the whole discipline of machine learning, heavily impacting fields such as Computer Vision, Natural Language Processing, and other domains concerned with the processing of raw inputs. Nonetheless, Deep Networks are still difficult to interpret, and their inference process is all but transparent. Moreover, there are still challenging tasks for Deep Networks: contexts where the success depends on structured knowledge that can not be easily provided to the networks in a standardized way. We aim to investigate the behavior of Deep Networks, assessing whether th
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Bennetot, Adrien. "A Neural-Symbolic learning framework to produce interpretable predictions for image classification." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS418.

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L'intelligence artificielle s'est développée de manière exponentielle au cours de la dernière décennie. Son évolution est principalement liée aux progrès des processeurs des cartes graphiques des ordinateurs, permettant d'accélérer le calcul des algorithmes d'apprentissage, et à l'accès à des volumes massifs de données. Ces progrès ont été principalement motivés par la recherche de modèles de prédiction de qualité, rendant ces derniers extrêmement précis mais opaques. Leur adoption à grande échelle est entravée par leur manque de transparence, ce qui provoque l'émergence de l'intelligence arti
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Borges, Rafael. "A neural-symbolic system for temporal reasoning with application to model verification and learning." Thesis, City University London, 2012. http://openaccess.city.ac.uk/1303/.

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The effective integration of knowledge representation, reasoning and learning into a robust computational model is one of the key challenges in Computer Science and Artificial Intelligence. In particular, temporal models have been fundamental in describing the behaviour of Computational and Neural-Symbolic Systems. Furthermore, knowledge acquisition of correct descriptions of the desired system’s behaviour is a complex task in several domains. Several efforts have been directed towards the development of tools that are capable of learning, describing and evolving software models. This thesis c
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Galassi, Andrea. "Symbolic versus sub-symbolic approaches: a case study on training Deep Networks to play Nine Men’s Morris game." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/12859/.

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Le reti neurali artificiali, grazie alle nuove tecniche di Deep Learning, hanno completamente rivoluzionato il panorama tecnologico degli ultimi anni, dimostrandosi efficaci in svariati compiti di Intelligenza Artificiale e ambiti affini. Sarebbe quindi interessante analizzare in che modo e in quale misura le deep network possano sostituire le IA simboliche. Dopo gli impressionanti risultati ottenuti nel gioco del Go, come caso di studio è stato scelto il gioco del Mulino, un gioco da tavolo largamente diffuso e ampiamente studiato. È stato quindi creato il sistema completamente sub-simbolico
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FALCIONELLI, NICOLA. "From Symbolic Artificial Intelligence to Neural Networks Universality with Event-based Modeling." Doctoral thesis, Università Politecnica delle Marche, 2020. http://hdl.handle.net/11566/274620.

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Rappresentare la conoscenza, modellare il ragionamento umano e comprendere i processi di pensiero sono sempre state parti centrali delle attività intellettuali, fin dai primi tentativi dei filosofi greci. Non è solo un caso che, non appena i computer hanno iniziato a diffondersi, scienziati e matematici straordinari come John McCarthy, Marvin Minsky e Claude Shannon hanno iniziato a creare sistemi Artificialmente Intelligenti con una prospettiva orientata al simbolismo. Anche se questo è stato un percorso parzialmente forzato a causa delle capacità di calcolo molto limitate dell'epoca, ha se
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Borges, Rafael Vergara. "Investigações sobre raciocínio e aprendizagem temporal em modelos conexionistas." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/11488.

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A inteligência computacional é considerada por diferentes autores da atualidade como o destino manifesto da Ciência da Computação. A modelagem de diversos aspectos da cognição, tais como aprendizagem e raciocínio, tem sido a motivação para o desenvolvimento dos paradigmas simbólico e conexionista da inteligência artificial e, mais recentemente, para a integração de ambos com o intuito de unificar as vantagens de cada abordagem em um modelo único. Para o desenvolvimento de sistemas inteligentes, bem como para diversas outras áreas da Ciência da Computação, o tempo é considerado como um componen
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Books on the topic "Neural symbolic learning"

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d’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. Neural-Symbolic Learning Systems. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3.

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Besold, Tarek R., Artur d’Avila Garcez, Ernesto Jimenez-Ruiz, Roberto Confalonieri, Pranava Madhyastha, and Benedikt Wagner, eds. Neural-Symbolic Learning and Reasoning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71170-1.

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Besold, Tarek R., Artur d’Avila Garcez, Ernesto Jimenez-Ruiz, Roberto Confalonieri, Pranava Madhyastha, and Benedikt Wagner, eds. Neural-Symbolic Learning and Reasoning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71167-1.

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Garcez, Artur S. D'Avila. Neural-Symbolic Learning Systems: Foundations and Applications. Springer London, 2002.

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Apolloni, Bruno. From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data. Springer US, 2002.

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International School on Neural Nets "E.R. Caianiello" Fifth Course: From Synapses to Rules: Discovering Symbolic Rules From Neural Processed Data (2002 Erice, Italy). From synapses to rules: Discovering symbolic rules from neural processed data. Kluwer Academic/Plenum Pub., 2002.

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Conference on Data Analysis, Learning Symbolic and Numeric Knowledge (1989 Antibes, France). Data analysis, learning symbolic and numeric knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, Antibes, September 11-14, 1989. Nova Science Publishers, 1989.

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Neural-Symbolic Learning Systems. Springer, 2002.

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Hammer, Barbara, and Pascal Hitzler. Perspectives of Neural-Symbolic Integration. Springer Berlin / Heidelberg, 2010.

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(Editor), Bruno Apolloni, and Franz Kurfess (Editor), eds. From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data. Springer, 2002.

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Book chapters on the topic "Neural symbolic learning"

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d’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Introduction and Overview." In Neural-Symbolic Learning Systems. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_1.

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d’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Background." In Neural-Symbolic Learning Systems. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_2.

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d’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Theory Refinement in Neural Networks." In Neural-Symbolic Learning Systems. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_3.

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d’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Experiments on Theory Refinement." In Neural-Symbolic Learning Systems. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_4.

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d’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Knowledge Extraction from Trained Networks." In Neural-Symbolic Learning Systems. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_5.

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d’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Experiments on Knowledge Extraction." In Neural-Symbolic Learning Systems. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_6.

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d’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Handling Inconsistencies in Neural Networks." In Neural-Symbolic Learning Systems. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_7.

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d’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Experiments on Handling Inconsistencies." In Neural-Symbolic Learning Systems. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_8.

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d’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Neural-Symbolic Integration: The Road Ahead." In Neural-Symbolic Learning Systems. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_9.

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Shakarian, Paulo, Chitta Baral, Gerardo I. Simari, Bowen Xi, and Lahari Pokala. "LNN: Logical Neural Networks." In Neuro Symbolic Reasoning and Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39179-8_6.

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Conference papers on the topic "Neural symbolic learning"

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Zafaranchi, Arman, Francesca Lizzi, Alessandra Retico, Camilla Scapicchio, and Maria Fantacci. "Explainability Applied to a Deep-Learning Based Algorithm for Lung Nodule Segmentation." In 1st International Conference on Explainable AI for Neural and Symbolic Methods. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0013014600003886.

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Logemann, Torben, and Eric Veith. "Analyzing Exact Output Regions of Reinforcement Learning Policy Neural Networks for High-Dimensional Input-Output Spaces." In 1st International Conference on Explainable AI for Neural and Symbolic Methods. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012928000003886.

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Miglionico, Giustino Claudio, Pietro Ducange, Francesco Marcelloni, and Witold Pedrycz. "Deep Learning and Multi-Objective Evolutionary Fuzzy Classifiers: A Comparative Analysis for Brain Tumor Classification in MRI Images." In 1st International Conference on Explainable AI for Neural and Symbolic Methods. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012940500003886.

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Rogers, Alexander W., Amanda Lane, Philip Martin, and Dongda Zhang. "AI-Driven Automatic Mechanistic Model Transfer Learning for Accelerating Process Development." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.167600.

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Accurate mechanistic models provide valuable physical insight and are crucial for efficient process scale-up and optimisation, but their identification requires lengthy experimental data collection, model construction, validation and discrimination. Traditional black-box machine learning transfer methods leverage prior knowledge but lack interpretability and physical insights. To address this, we propose a novel approach using artificial neural network feature attribution to automatically locate corrections and symbolic regression to make structural modifications to an inaccurate or low-fideli
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Lee, Jaewook, Ethan Errington, and Miao Guo. "A White-Box AI Framework for Interpretable Global Warming Potential Prediction." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.177555.

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Accurate yet interpretable prediction of Global Warming Potential (GWP) is essential for the sustainable design of chemical products and processes. However, existing studies that utilize molecular structure and physicochemical properties for GWP prediction often suffer from low interpretability, relying on black-box models that obscure the underlying relationships between molecular descriptors and environmental impact. To address this limitation, this study employs a Kolmogorov�Arnold Network (KAN) to derive symbolic equations that establish explicit relationships between molecular properties
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Daniele, Alessandro, Tommaso Campari, Sagar Malhotra, and Luciano Serafini. "Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/400.

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Neuro-Symbolic (NeSy) integration combines symbolic reasoning with Neural Networks (NNs) for tasks requiring perception and reasoning. Most NeSy systems rely on continuous relaxation of logical knowledge, and no discrete decisions are made within the model pipeline. Furthermore, these methods assume that the symbolic rules are given. In this paper, we propose Deep Symboilic Learning (DSL), a NeSy system that learns NeSy-functions, i.e., the composition of a (set of) perception functions which map continuous data to discrete symbols, and a symbolic function over the set of symbols. DSL simultan
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Pryor, Connor, Charles Dickens, Eriq Augustine, Alon Albalak, William Yang Wang, and Lise Getoor. "NeuPSL: Neural Probabilistic Soft Logic." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/461.

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In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks. To model the boundary between neural and symbolic representations, we propose a family of energy-based models, NeSy Energy-Based Models, and show that they are general enough to include NeuPSL and many other NeSy approaches. Using this framework, we show how to seamlessly integrate neural and symbolic parameter learning and inference in NeuPSL. Through an extensive empirical evaluatio
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Sheikh, Hassam Ullah, Shauharda Khadka, Santiago Miret, Somdeb Majumdar, and Mariano Phielipp. "Learning Intrinsic Symbolic Rewards in Reinforcement Learning." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892256.

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Cunnington, Daniel, Mark Law, Jorge Lobo, and Alessandra Russo. "Neuro-Symbolic Learning of Answer Set Programs from Raw Data." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/399.

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One of the ultimate goals of Artificial Intelligence is to assist humans in complex decision making. A promising direction for achieving this goal is Neuro-Symbolic AI, which aims to combine the interpretability of symbolic techniques with the ability of deep learning to learn from raw data. However, most current approaches require manually engineered symbolic knowledge, and where end-to-end training is considered, such approaches are either restricted to learning definite programs, or are restricted to training binary neural networks. In this paper, we introduce Neuro-Symbolic Inductive Learn
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Wang, Xiaomei, Lin Ma, Yanwei Fu, and Xiangyang Xue. "Neural Symbolic Representation Learning for Image Captioning." In ICMR '21: International Conference on Multimedia Retrieval. ACM, 2021. http://dx.doi.org/10.1145/3460426.3463637.

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Reports on the topic "Neural symbolic learning"

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O'Brien, Beth A., Chee Soon Tan, and Luca Onnis. Technology-based tools for teaching early literacy skills. National Institute of Education, Nanyang Technological University, Singapore, 2024. https://doi.org/10.32658/10497/27453.

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Abstract:
This project focuses on improving literacy development for young learners who are struggling with learning to read English by investigating the process of learning grapheme-phoneme correspondences (GPCs). Learning GPC is foundational to learning to read alphabetic languages, and is a core problem for struggling readers. In this project, two methods are used in two studies to understand the process of learning English GPCs as the crux of acquiring literacy. First, a machine learning neural network modelling approach is used to study the effect of sound-symbol grain size and consistency and trai
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