Relevant bibliographies by topics / Geometric learning

Contents

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

Academic literature on the topic 'Geometric learning'

Author: Grafiati
Published: 13 February 2024

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Journal articles on the topic "Geometric learning"

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Omohundro, Stephen M. "Geometric learning algorithms." Physica D: Nonlinear Phenomena 42, no. 1-3 (June 1990): 307–21. http://dx.doi.org/10.1016/0167-2789(90)90085-4.

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Jamshidi, Arta, Michael Kirby, and Dave Broomhead. "Geometric Manifold Learning." IEEE Signal Processing Magazine 28, no. 2 (March 2011): 69–76. http://dx.doi.org/10.1109/msp.2010.939550.

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Gong, Wenjuan, Bin Zhang, Chaoqi Wang, Hanbing Yue, Chuantao Li, Linjie Xing, Yu Qiao, Weishan Zhang, and Faming Gong. "A Literature Review: Geometric Methods and Their Applications in Human-Related Analysis." Sensors 19, no. 12 (June 23, 2019): 2809. http://dx.doi.org/10.3390/s19122809.

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Geometric features, such as the topological and manifold properties, are utilized to extract geometric properties. Geometric methods that exploit the applications of geometrics, e.g., geometric features, are widely used in computer graphics and computer vision problems. This review presents a literature review on geometric concepts, geometric methods, and their applications in human-related analysis, e.g., human shape analysis, human pose analysis, and human action analysis. This review proposes to categorize geometric methods based on the scope of the geometric properties that are extracted: object-oriented geometric methods, feature-oriented geometric methods, and routine-based geometric methods. Considering the broad applications of deep learning methods, this review also studies geometric deep learning, which has recently become a popular topic of research. Validation datasets are collected, and method performances are collected and compared. Finally, research trends and possible research topics are discussed.
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Gao, Huiping, and Zhongchen Ma. "Geometric Metric Learning for Multi-Output Learning." Mathematics 10, no. 10 (May 11, 2022): 1632. http://dx.doi.org/10.3390/math10101632.

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Due to its wide applications, multi-output learning that predicts multiple output values for a single input at the same time is becoming more and more attractive. As one of the most popular frameworks for dealing with multi-output learning, the performance of the k-nearest neighbor (kNN) algorithm mainly depends on the metric used to compute the distance between different instances. In this paper, we propose a novel cost-weighted geometric mean metric learning method for multi-output learning. Specifically, this method learns a geometric mean metric which can make the distance between the input embedding and its correct output be smaller than the distance between the input embedding and the outputs of its nearest neighbors. The learned geometric mean metric can discover output dependencies and move the instances with different outputs far away in the embedding space. In addition, our objective function has a closed solution, and thus the calculation speed is very fast. Compared with state-of-the-art methods, it is easier to explain and also has a faster calculation speed. Experiments conducted on two multi-output learning tasks (i.e., multi-label classification and multi-objective regression) have confirmed that our method provides better results than state-of-the-art methods.
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Gao, Xiaoqing, and Hugh R. Wilson. "Implicit learning of geometric eigenfaces." Vision Research 99 (June 2014): 12–18. http://dx.doi.org/10.1016/j.visres.2013.07.015.

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Goldman, Sally A., Stephen S. Kwek, and Stephen D. Scott. "Agnostic Learning of Geometric Patterns." Journal of Computer and System Sciences 62, no. 1 (February 2001): 123–51. http://dx.doi.org/10.1006/jcss.2000.1723.

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Feng, Zixin, Teligeng Yun, Yu Zhou, Ruirui Zheng, and Jianjun He. "Kernel Geometric Mean Metric Learning." Applied Sciences 13, no. 21 (November 6, 2023): 12047. http://dx.doi.org/10.3390/app132112047.

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Geometric mean metric learning (GMML) algorithm is a novel metric learning approach proposed recently. It has many advantages such as unconstrained convex objective function, closed form solution, faster computational speed, and interpretability over other existing metric learning technologies. However, addressing the nonlinear problem is not effective enough. The kernel method is an effective method to solve nonlinear problems. Therefore, a kernel geometric mean metric learning (KGMML) algorithm is proposed. The basic idea is to transform the input space into a high-dimensional feature space through nonlinear transformation, and use the integral representation of the weighted geometric mean and the Woodbury matrix identity in new feature space to generalize the analytical solution obtained in the GMML algorithm as a form represented by a kernel matrix, and then the KGMML algorithm is obtained through operations. Experimental results on 15 datasets show that the proposed algorithm can effectively improve the accuracy of the GMML algorithm and other metric algorithms.
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AKARSU, Murat. "Understanding of Geometric Reflection: John’s learning path for geometric reflection." Kuramsal Eğitimbilim 15, no. 1 (January 31, 2022): 64–89. http://dx.doi.org/10.30831/akukeg.952022.

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Townshend, Raphael, Ligia Melo, David Liu, and Ron O. Dror. "Geometric Deep Learning on Biomolecular Structure." Biophysical Journal 120, no. 3 (February 2021): 290a. http://dx.doi.org/10.1016/j.bpj.2020.11.1863.

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Kaplan, Haim, Yishay Mansour, Yossi Matias, and Uri Stemmer. "Differentially Private Learning of Geometric Concepts." SIAM Journal on Computing 51, no. 4 (July 7, 2022): 952–74. http://dx.doi.org/10.1137/21m1406428.

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Dissertations / Theses on the topic "Geometric learning"

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Sturz, Bradley R. Katz Jeffrey S. "Geometric rule learning by pigeons." Auburn, Ala., 2007. http://repo.lib.auburn.edu/2006%20Fall/Dissertations/STURZ_BRADLEY_52.pdf.

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Saive, Yannick. "DirCNN: Rotation Invariant Geometric Deep Learning." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252573.

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Recently geometric deep learning introduced a new way for machine learning algorithms to tackle point cloud data in its raw form. Pioneers like PointNet and many architectures building on top of its success realize the importance of invariance to initial data transformations. These include shifting, scaling and rotating the point cloud in 3D space. Similarly to our desire for image classifying machine learning models to classify an upside down dog as a dog, we wish geometric deep learning models to succeed on transformed data. As such, many models employ an initial data transform in their models which is learned as part of a neural network, to transform the point cloud into a global canonical space. I see weaknesses in this approach as they are not guaranteed to perform completely invariant to input data transformations, but rather approximately. To combat this I propose to use local deterministic transformations which do not need to be learned. The novelty layer of this project builds upon Edge Convolutions and is thus dubbed DirEdgeConv, with the directional invariance in mind. This layer is slightly altered to introduce another layer by the name of DirSplineConv. These layers are assembled in a variety of models which are then benchmarked against the same tasks as its predecessor to invite a fair comparison. The results are not quite as good as state of the art results, however are still respectable. It is also my belief that the results can be improved by improving the learning rate and its scheduling. Another experiment in which ablation is performed on the novel layers shows that the layers  main concept indeed improves the overall results.
Nyligen har ämnet geometrisk deep learning presenterat ett nytt sätt för maskininlärningsalgoritmer att arbeta med punktmolnsdata i dess råa form.Banbrytande arkitekturer som PointNet och många andra som byggt på dennes framgång framhåller vikten av invarians under inledande datatransformationer. Sådana transformationer inkluderar skiftning, skalning och rotation av punktmoln i ett tredimensionellt rum. Precis som vi önskar att klassifierande maskininlärningsalgoritmer lyckas identifiera en uppochnedvänd hund som en hund vill vi att våra geometriska deep learning-modeller framgångsrikt ska kunna hantera transformerade punktmoln. Därför använder många modeller en inledande datatransformation som tränas som en del av ett neuralt nätverk för att transformera punktmoln till ett globalt kanoniskt rum. Jag ser tillkortakommanden i detta tillgångavägssätt eftersom invariansen är inte fullständigt garanterad, den är snarare approximativ. För att motverka detta föreslår jag en lokal deterministisk transformation som inte måste läras från datan. Det nya lagret i det här projektet bygger på Edge Convolutions och döps därför till DirEdgeConv, namnet tar den riktningsmässiga invariansen i åtanke. Lagret ändras en aning för att introducera ett nytt lager vid namn DirSplineConv. Dessa lager sätts ihop i olika modeller som sedan jämförs med sina efterföljare på samma uppgifter för att ge en rättvis grund för att jämföra dem. Resultaten är inte lika bra som toppmoderna resultat men de är ändå tillfredsställande. Jag tror även resultaten kan förbättas genom att förbättra inlärningshastigheten och dess schemaläggning. I ett experiment där ablation genomförs på de nya lagren ser vi att lagrens huvudkoncept förbättrar resultaten överlag.
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Lamma, Tommaso. "A mathematical introduction to geometric deep learning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23886/.

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Lo scopo del geometric deep learning è quello di estendere l'algoritmo di deep learning sviluppato per la classificazione di immagini a domini non euclidei come grafi e complessi simpliciali.In questa tesi ci proponiamo di dare una definizione matematica dei concetti cardine utilizzati nel geometric deep learning quali equivarianza e convoluzione sui grafi. Vedremo inoltre come definire una rete convoluzionale invariante rispetto all'azione di gruppi.
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Hold-Geoffroy, Yannick. "Learning geometric and lighting priors from natural images." Doctoral thesis, Université Laval, 2018. http://hdl.handle.net/20.500.11794/31264.

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Comprendre les images est d’une importance cruciale pour une pléthore de tâches, de la composition numérique au ré-éclairage d’une image, en passant par la reconstruction 3D d’objets. Ces tâches permettent aux artistes visuels de réaliser des chef-d’oeuvres ou d’aider des opérateurs à prendre des décisions de façon sécuritaire en fonction de stimulis visuels. Pour beaucoup de ces tâches, les modèles physiques et géométriques que la communauté scientifique a développés donnent lieu à des problèmes mal posés possédant plusieurs solutions, dont généralement une seule est raisonnable. Pour résoudre ces indéterminations, le raisonnement sur le contexte visuel et sémantique d’une scène est habituellement relayé à un artiste ou un expert qui emploie son expérience pour réaliser son travail. Ceci est dû au fait qu’il est généralement nécessaire de raisonner sur la scène de façon globale afin d’obtenir des résultats plausibles et appréciables. Serait-il possible de modéliser l’expérience à partir de données visuelles et d’automatiser en partie ou en totalité ces tâches ? Le sujet de cette thèse est celui-ci : la modélisation d’a priori par apprentissage automatique profond pour permettre la résolution de problèmes typiquement mal posés. Plus spécifiquement, nous couvrirons trois axes de recherche, soient : 1) la reconstruction de surface par photométrie, 2) l’estimation d’illumination extérieure à partir d’une seule image et 3) l’estimation de calibration de caméra à partir d’une seule image avec un contenu générique. Ces trois sujets seront abordés avec une perspective axée sur les données. Chacun de ces axes comporte des analyses de performance approfondies et, malgré la réputation d’opacité des algorithmes d’apprentissage machine profonds, nous proposons des études sur les indices visuels captés par nos méthodes.
Understanding images is needed for a plethora of tasks, from compositing to image relighting, including 3D object reconstruction. These tasks allow artists to realize masterpieces or help operators to safely make decisions based on visual stimuli. For many of these tasks, the physical and geometric models that the scientific community has developed give rise to ill-posed problems with several solutions, only one of which is generally reasonable. To resolve these indeterminations, the reasoning about the visual and semantic context of a scene is usually relayed to an artist or an expert who uses his experience to carry out his work. This is because humans are able to reason globally on the scene in order to obtain plausible and appreciable results. Would it be possible to model this experience from visual data and partly or totally automate tasks? This is the topic of this thesis: modeling priors using deep machine learning to solve typically ill-posed problems. More specifically, we will cover three research axes: 1) surface reconstruction using photometric cues, 2) outdoor illumination estimation from a single image and 3) camera calibration estimation from a single image with generic content. These three topics will be addressed from a data-driven perspective. Each of these axes includes in-depth performance analyses and, despite the reputation of opacity of deep machine learning algorithms, we offer studies on the visual cues captured by our methods.
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Xia, Baiqiang. "Learning 3D geometric features for soft-biometrics recognition." Thesis, Lille 1, 2014. http://www.theses.fr/2014LIL10132/document.

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La reconnaissance des biomètries douces (genre, âge, etc.)trouve ses applications dans plusieurs domaines. Les approches proposéesse basent sur l’analyse de l’apparence (images 2D), très sensiblesaux changements de la pose et à l’illumination, et surtout pauvre en descriptionsmorphologiques. Dans cette thèse, nous proposons d’exploiterla forme 3D du visage. Basée sur une approche Riemannienne d’analysede formes 3D, nous introduisons quatre descriptions denses à savoir: lasymétrie bilatérale, la moyenneté, la configuration spatiale et les variationslocales de sa forme. Les évaluations faites sur la base FRGCv2 montrentque l’approche proposée est capable de reconnaître des biomètries douces.A notre connaissance, c’est la première étude menée sur l’estimation del’âge, et c’est aussi la première étude qui propose d’explorer les corrélationsentre les attributs faciaux, à partir de formes 3D
Soft-Biometric (gender, age, etc.) recognition has shown growingapplications in different domains. Previous 2D face based studies aresensitive to illumination and pose changes, and insufficient to representthe facial morphology. To overcome these problems, this thesis employsthe 3D face in Soft-Biometric recognition. Based on a Riemannian shapeanalysis of facial radial curves, four types of Dense Scalar Field (DSF) featuresare proposed, which represent the Averageness, the Symmetry, theglobal Spatiality and the local Gradient of 3D face. Experiments with RandomForest on the 3D FRGCv2 dataset demonstrate the effectiveness ofthe proposed features in Soft-Biometric recognition. Furtherly, we demonstratethe correlations of Soft-Biometrics are useful in the recognition. Tothe best of our knowledge, this is the first work which studies age estimation,and the correlations of Soft-Biometrics, using 3D face
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Liberatore, Lorenzo. "Introduction to geometric deep learning and graph neural networks." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25339/.

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This thesis proposes an introduction to the fundamental concepts of supervised deep learning. Starting from Rosemblatt's Perceptron we will discuss the architectures that, in recent years, have revolutioned the world of deep learning: graph neural networks, which led to the formulation of geometric deep learning. We will then give a simple example of graph neural network, discussing the code that composes it and then test our architecture on the MNISTSuperpixels dataset, which is a variation of the benchmark dataset MNIST.
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Araya, Valdivia Ernesto. "Kernel spectral learning and inference in random geometric graphs." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM020.

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Cette thèse comporte deux objectifs. Un premier objectif concerne l’étude des propriétés de concentration des matrices à noyau, qui sont fondamentales dans l’ensemble des méthodes à noyau. Le deuxième objectif repose quant à lui sur l’étude des problèmes d’inférence statistique dans le modèle des graphes aléatoires géométriques. Ces deux objectifs sont liés entre eux par le formalisme du graphon, qui permet représenter un graphe par un noyau. Nous rappelons les rudiments du modèle du graphon dans le premier chapitre. Le chapitre 2 présente des bornes précises pour les valeurs propres individuelles d’une matrice à noyau, où notre principale contribution est d’obtenir des inégalités à l’échelle de la valeur propre en considération. Ceci donne des vitesses de convergence qui sont meilleures que la vitesse paramétrique et, en occasions, exponentielles. Jusqu’ici cela n’avait été établi qu’avec des hypothèses contraignantes dans le contexte des graphes. Nous spécialisons les résultats au cas de noyaux de produit scalaire, en soulignant sa relation avec le modèle des graphes géométriques. Le chapitre 3 étudie le problème d’estimation des distances latentes pour le modèle des graphes aléatoires géométriques dans la sphère Euclidienne. Nous proposons un algorithme spectral efficace qui utilise la matrice d’adjacence pour construire un estimateur de la matrice des distances latentes, et des garanties théoriques pour l’erreur d’estimation, ainsi que la vitesse de convergence, sont montrées. Le chapitre 4 étend les méthodes développées dans le chapitre précédent au cas des graphes aléatoires géométriques dans la boule Euclidienne, un modèle qui, en dépit des similarités formelles avec le cas sphérique, est plus flexible en termes de modélisation. En particulier, nous montrons que pour certains choix des paramètres le profil des dégrées est distribué selon une loi de puissance, ce qui a été vérifié empiriquement dans plusieurs réseaux réels. Tous les résultats théoriques des deux derniers chapitres sont confirmés par des expériences numériques
This thesis has two main objectives. The first is to investigate the concentration properties of random kernel matrices, which are central in the study of kernel methods. The second objective is to study statistical inference problems on random geometric graphs. Both objectives are connected by the graphon formalism, which allows to represent a graph by a kernel function. We briefly recall the basics of the graphon model in the first chapter. In chapter two, we present a set of accurate concentration inequalities for individual eigenvalues of the kernel matrix, where our main contribution is to obtain inequalities that scale with the eigenvalue in consideration, implying convergence rates that are faster than parametric and often exponential, which hitherto has only been establish under assumptions which are too restrictive for graph applications. We specialized our results to the case of dot products kernels, highlighting its relation with the random geometric graph model. In chapter three, we study the problem of latent distances estimation on random geometric graphs on the Euclidean sphere. We propose an efficient spectral algorithm that use the adjacency matrix to construct an estimator for the latent distances, and prove finite sample guaranties for the estimation error, establishing its convergence rate. In chapter four, we extend the method developed in the previous chapter to the case of random geometric graphs on the Euclidean ball, a model that despite its formal similarities with the spherical case it is more flexible for modelling purposes. In particular, we prove that for certain parameter choices its degree profile is power law distributed, which has been observed in many real life networks. All the theoretical findings of the last two chapters are verified and complemented by numerical experiments
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Masters, Jennifer Ellen. "Investigations in geometric thinking : young children learning with technology." Thesis, Queensland University of Technology, 1997. https://eprints.qut.edu.au/36544/1/36544_Masters_1997.pdf.

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While it is usually assumed that the implementation of computers in the classroom will enhance teaching and learning, research has suggested that too often the use of computers does not meet this assumption. This thesis investigated the implementation of a technology-based mathematics curriculum unit that was characterised by tasks designed to promote exploration and investigation of geometric concepts. In particular it focused on the children's application of prior mathematical knowledge while they worked in pairs on computer -based tasks. The study found that children could often apply prior mathematical knowledge to solve problems in a new context, however, on other occasions they were unable to do so or they choose to apply less sophisticated mathematical strategies (such as visual approximation). Other evidence suggested that at times the children appeared to be constructing new mathematical ideas or at least, implementing concepts not formally presented in a school context. A further observation of this study was that the results of this type of technological project seemed to be highly dependent on dynamic group structures and teacher support mechanisms such as scaffolding. Consequently it was recommended that further research wa
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Peng, Liz Shihching. "p5.Polar - Programming For Geometric Patterns." Digital WPI, 2020. https://digitalcommons.wpi.edu/etd-theses/1353.

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Traditional teaching methods are often passive and do not interactively engage students, and this is even more challenging when teaching programming to beginners. In recent years, tech companies such as Google, and academic institutions like MIT, have introduced online learning environments to schools for teaching programming. Most of these learning environments are web-based, interactive, and provide visual feedback. Our project follows these trends and builds on p5.js, a JavaScript library that provides software sketching features and rapid visual feedback to reduce the barrier for learning programming languages. We designed and implemented a new library for drawing geometric patterns using polar coordinate systems, p5.Polar. We then developed a game that incrementally teaches our library to players, and evaluated it with an online user study.
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Batt, Kathleen J. "The Implementation of kinesthetic learning activities to identify geometric shapes with preschool students." Defiance College / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=def1281535832.

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Books on the topic "Geometric learning"

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Omohundro, Stephen M. Fundamentals of geometric learning. Urbana, IL (1304 W. Springfield Ave., Urbana 61801): Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1988.

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Vlassis, Nikolaos Napoleon. Towards Trustworthy Geometric Deep Learning for Elastoplasticity. [New York, N.Y.?]: [publisher not identified], 2021.

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Ron, Brown. Learning guide: The infinite geometric progression puzzles. Marshall, AR (HC 79, Box 192A, Marshall 72650): Mountain Spring Woodworking, 1994.

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Metropolitan Museum of Art (New York, N.Y.) and Metropolitan Museum of Art (New York, N.Y.). Education Dept., eds. Islamic art and geometric design: Activities for learning. New York, N.Y: Metropolitan Museum of Art, 2004.

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Barbaresco, Frédéric, and Frank Nielsen, eds. Geometric Structures of Statistical Physics, Information Geometry, and Learning. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77957-3.

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Shapes at home: Learning to recognize basic geometric shapes. New York: Rosen Classroom Books & Materials, 2004.

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Corrochano, Eduardo Bayro. Geometric computing: Or wavelet transforms, robot vision, learning, control and action. London: Springer, 2010.

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1956-, Williams Kim, ed. Infinite measure: Learning to design in geometric harmony with art, architecture, and nature. Staunton, VA: George F. Thompson Publishing, 2013.

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Markinson, Mara P. The Teaching and Learning of Geometric Proof: Roles of the Textbook and the Teacher. [New York, N.Y.?]: [publisher not identified], 2021.

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Battista, Michael T. Cognition-based assessment and teaching of geometric shapes: Building on students' reasoning. Portsmouth, NH: Heinemann, 2012.

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Book chapters on the topic "Geometric learning"

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Hajiabadi, Hamideh, Reza Godaz, Morteza Ghasemi, and Reza Monsefi. "Layered Geometric Learning." In Artificial Intelligence and Soft Computing, 571–82. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20912-4_52.

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Clements, Douglas H., and Julie Sarama. "Geometric Measurement." In Learning and Teaching Early Math, 246–59. Third edition. | New York, NY : Routledge, 2021. |: Routledge, 2020. http://dx.doi.org/10.4324/9781003083528-10.

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Dobkin, David, and Dimitrios Gunopulos. "Geometric problems in machine learning." In Applied Computational Geometry Towards Geometric Engineering, 121–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0014490.

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Whiteley, Walter. "Representing Geometric Configurations." In Learning and Geometry: Computational Approaches, 143–78. Boston, MA: Birkhäuser Boston, 1996. http://dx.doi.org/10.1007/978-1-4612-4088-4_7.

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Clements, Douglas H., and Julie Sarama. "Geometric Measurement 1." In Learning and Teaching Early Math, 260–78. Third edition. | New York, NY : Routledge, 2021. |: Routledge, 2020. http://dx.doi.org/10.4324/9781003083528-11.

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Lindquist, Anders, and Giorgio Picci. "Geometric Methods for State Space Identification." In Identification, Adaptation, Learning, 1–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03295-4_1.

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Sra, Suvrit, and Reshad Hosseini. "Geometric Optimization in Machine Learning." In Algorithmic Advances in Riemannian Geometry and Applications, 73–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45026-1_3.

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Mendelson, S. "Geometric Parameters in Learning Theory." In Lecture Notes in Mathematics, 193–235. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44489-3_17.

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Fondevilla, Amélie, Géraldine Morin, and Kathryn Leonard. "Towards Learning Geometric Shape Parts." In Association for Women in Mathematics Series, 95–111. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79891-8_5.

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Sharma, Rahul, Saurabh Gupta, Bharath Hariharan, Alex Aiken, and Aditya V. Nori. "Verification as Learning Geometric Concepts." In Static Analysis, 388–411. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38856-9_21.

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Conference papers on the topic "Geometric learning"

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Masci, Jonathan, Emanuele Rodolà, Davide Boscaini, Michael M. Bronstein, and Hao Li. "Geometric deep learning." In SA '16: SIGGRAPH Asia 2016. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2988458.2988485.

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Koishekenov, Yeskendir, Sharvaree Vadgama, Riccardo Valperga, and Erik J. Bekkers. "Geometric Contrastive Learning." In 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2023. http://dx.doi.org/10.1109/iccvw60793.2023.00028.

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Khoury, Marc, Qian-Yi Zhou, and Vladlen Koltun. "Learning Compact Geometric Features." In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017. http://dx.doi.org/10.1109/iccv.2017.26.

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Fiser, Marek, Bedrich Benes, Jorge Garcia Galicia, Michel Abdul-Massih, Daniel G. Aliaga, and Vojtech Krs. "Learning geometric graph grammars." In SCCG'16: Spring Conference on Computer Graphics. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2948628.2948635.

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Collas, Antoine, Arnaud Breloy, Guillaume Ginolhac, Chengfang Ren, and Jean-Philippe Ovarlez. "Robust Geometric Metric Learning." In 2022 30th European Signal Processing Conference (EUSIPCO). IEEE, 2022. http://dx.doi.org/10.23919/eusipco55093.2022.9909973.

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Jawanpuria, Pratik, Satya Dev N T V, Anoop Kunchukuttan, and Bamdev Mishra. "Learning Geometric Word Meta-Embeddings." In Proceedings of the 5th Workshop on Representation Learning for NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.repl4nlp-1.6.

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Unal, Inan, and Özal Yildirim. "Geometric Methods in Deep Learning." In 2018 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 2018. http://dx.doi.org/10.1109/asyu.2018.8554021.

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Lee, Jinhwi, Jungtaek Kim, Hyunsoo Chung, Jaesik Park, and Minsu Cho. "Learning to Assemble Geometric Shapes." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/146.

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Assembling parts into an object is a combinatorial problem that arises in a variety of contexts in the real world and involves numerous applications in science and engineering. Previous related work tackles limited cases with identical unit parts or jigsaw-style parts of textured shapes, which greatly mitigate combinatorial challenges of the problem. In this work, we introduce the more challenging problem of shape assembly, which involves textureless fragments of arbitrary shapes with indistinctive junctions, and then propose a learning-based approach to solving it. We demonstrate the effectiveness on shape assembly tasks with various scenarios, including the ones with abnormal fragments (e.g., missing and distorted), the different number of fragments, and different rotation discretization.
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Dobkin, David P., and Dimitrios Gunopulos. "Concept learning with geometric hypotheses." In the eighth annual conference. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/225298.225338.

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Afifah, Arum Hikmahtul, Susanto, and Nurcholif Diah Sri Lestari. "Geometric reasoning of analysis level students in classifying quadrilateral." In MATHEMATICS EDUCATION AND LEARNING. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0105224.

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Reports on the topic "Geometric learning"

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Willsky, Alan S. Multiresolution, Geometric, and Learning Methods in Statistical Image Processing, Object Recognition, and Sensor Fusion. Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada425745.

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Schoening, Timm. OceanCV. GEOMAR, 2022. http://dx.doi.org/10.3289/sw_5_2022.

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OceanCV provides computer vision algorithms and tools for underwater image analysis. This includes image processing, pattern recognition, machine learning and geometric algorithms but also functionality for navigation data processing, data provenance etc.
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Rashevska, Natalya V., Serhiy O. Semerikov, Natalya O. Zinonos, Viktoriia V. Tkachuk, and Mariya P. Shyshkina. Using augmented reality tools in the teaching of two-dimensional plane geometry. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4116.

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One of the successful components of quality assimilation of educational material and its further use in the learning process is visualization of material in secondary education institutions. Visualizations need the subjects of the school course, which are the most difficult to understand and essentially do not have at the beginning of the study of widespread practical application, mostly mathematical objects. That is why this study aimed to analyze mobile tools that can be used to visualize teaching geometry. The object of the study is the process of teaching geometry in the middle classes of secondary schools. The subject of the study is the use of augmented reality tools in teaching geometry to students in grades 7-9. The study used such research methods as the analysis and justification of the choice of mobile augmented reality for the study of mathematics. Analyses displayed two augmented reality tools: ArloonGeometry and Geometry AR. In order to gain geometry instruction’s academic success for the students, these tools can be used by teachers to visualize training material and create a problematic situation. The use of augmented reality means in the geometry lessons creates precisely such conditions for positive emotional interaction between the student and the teacher. It also provided support to reduce fear and anxiety attitudes towards geometry classes. The emotional component of learning creates the conditions for better memorization of the educational material, promotes their mathematical interest, realizes their creative potential, creates the conditions for finding different ways of solving geometric problems.
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Wardhaugh, Benjamin. Learning Geometry in Georgian England. Washington, DC: The MAA Mathematical Sciences Digital Library, September 2012. http://dx.doi.org/10.4169/loci003930.

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Stanley, Kenneth O. Scalable Heterogeneous Multiagent Teams Through Learning Policy Geometry. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada551086.

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Kulkarni, S. R., S. K. Mitter, J. N. Tsitsiklis, and O. Zeitouni. PAC Learning with Generalized Samples and an Application to Stochastic Geometry. Fort Belvoir, VA: Defense Technical Information Center, June 1991. http://dx.doi.org/10.21236/ada459600.

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Rockmore, Daniel. Dynamic Information Networks: Geometry, Topology and Statistical Learning for the Articulation of Structure. Fort Belvoir, VA: Defense Technical Information Center, June 2015. http://dx.doi.org/10.21236/ada624183.

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Hlushak, Oksana M., Volodymyr V. Proshkin, and Oksana S. Lytvyn. Using the e-learning course “Analytic Geometry” in the process of training students majoring in Computer Science and Information Technology. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3268.

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As a result of literature analysis the expediency of free access of bachelors majoring in Computer Sciences and Information Technologies to modern information educational resources, in particular, e-learning courses in the process of studying mathematical disciplines is substantiated. It was established that the e-learning course is a complex of teaching materials and educational services created for the organization of individual and group training using information and communication technologies. Based on the outlined possibilities of applying the e-learning course, as well as its didactic functions, the structure of the certified e-learning course “Analytic Geometry” based on the Moodle platform was developed and described. Features of application of cloud-oriented resources are considered: Desmos, Geogebra, Wolfram|Alpha, Sage in the study of the discipline “Analytic Geometry”. The results of the pedagogical experiment on the basis of Borys Grinchenko Kyiv University and A. S. Makarenko Sumy State Pedagogical University are presented. The experiment was conducted to verify the effectiveness of the implementation of the e-learning course “Analytic Geometry”. Using the Pearson criterion it is proved that there are significant differences in the level of mathematical preparation of experimental and control group of students. The prospect of further scientific research is outlined through the effectiveness of the use of e-learning courses for the improvement of additional professional competences of students majoring in Computer Sciences and Information Technologies (specialization “Programming”, “Internet of Things”).
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Babkin, Vladyslav V., Viktor V. Sharavara, Volodymyr V. Sharavara, Vladyslav V. Bilous, Andrei V. Voznyak, and Serhiy Ya Kharchenko. Using augmented reality in university education for future IT specialists: educational process and student research work. CEUR Workshop Proceedings, July 2021. http://dx.doi.org/10.31812/123456789/4632.

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The article substantiates the feature of using augmented reality (AR) in university training of future IT specialists in the learning process and in the research work of students. The survey of university teachers analyzed the most popular AR applications for training future IT specialists (AR Ruler, AR Physics, Nicola Tesla, Arloon Geometry, AR Geometry, GeoGebra 3D Graphing Calculator, etc.), disclose the main advantages of the applications. The methodological basis for the implementation of future IT specialists research activities towards the development and use of AR applications is substantiated. The content of the activities of the student’s scientific club “Informatics studios” of Borys Grinchenko Kyiv University is developed. Students as part of the scientific club activity updated the mobile application, and the model bank corresponding to the topics: “Polyhedrons” for 11th grade, as well as “Functions, their properties and graphs” for 10th grade. The expediency of using software tools to develop a mobile application (Android Studio, SDK, NDK, QR Generator, FTDS Dev, Google Sceneform, Poly) is substantiated. The content of the stages of development of a mobile application is presented. As a result of a survey of students and pupils the positive impact of AR on the learning process is established.
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Bilousova, Liudmyla I., Liudmyla E. Gryzun, Daria H. Sherstiuk, and Ekaterina O. Shmeltser. Cloud-based complex of computer transdisciplinary models in the context of holistic educational approach. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3259.

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The paper represents the authors’ cloud-based complex of computer dynamic models and their transdisciplinary facilities. Proper theoretical background for the complex design is elaborated and the process of the computer models development is covered. The models in the complex are grouped in the sections according to the curriculum subjects (Physics, Algebra, Geometry, Biology, Geography, and Informatics). Each of the sections includes proper models along with their description and transdisciplinary didactic support. The paper also presents recommendations as for using of the complex to provide holistic learning of Mathematics, Science and Informatics at secondary school. The prospects of further research are outlined.
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