Relevant bibliographies by topics / Super learning

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  2. Dissertations / Theses
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Academic literature on the topic 'Super learning'

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Published: 6 July 2024

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Journal articles on the topic "Super learning"

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Long, Jun, Jinhuan Zhang, and Ping Du. "Super-sampling by learning-based super-resolution." International Journal of Computational Science and Engineering 1, no. 1 (2019): 1. http://dx.doi.org/10.1504/ijcse.2019.10020177.

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Du, Ping, Jinhuan Zhang, and Jun Long. "Super-sampling by learning-based super-resolution." International Journal of Computational Science and Engineering 21, no. 2 (2020): 249. http://dx.doi.org/10.1504/ijcse.2020.105731.

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Haris, Muhammad, M. Rahmat Widyanto, and Hajime Nobuhara. "Inception learning super-resolution." Applied Optics 56, no. 22 (2017): 6043. http://dx.doi.org/10.1364/ao.56.006043.

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GURBYCH, A. "METHOD SUPER LEARNING FOR DETERMINATION OF MOLECULAR RELATIONSHIP." Herald of Khmelnytskyi National University. Technical sciences 307, no. 2 (2022): 14–24. http://dx.doi.org/10.31891/2307-5732-2022-307-2-14-24.

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This paper uses the Super Learning principle to predict the molecular affinity between the receptor (large biomolecule) and ligands (small organic molecules). Meta-models study the optimal combination of individual basic models in two consecutive ensembles – classification and regression. Each costume contains six models of machine learning, which are combined by stacking. Base models include the reference vector method, random forest, gradient boosting, neural graph networks, direct propagation, and transformers. The first ensemble predicts binding probability and classifies all candidate molecules to the selected receptor into active and inactive. Ligands recognized as involved by the first ensemble are fed to the second ensemble, which assumes the degree of their affinity for the receptor in the form of an inhibition factor (Ki). A feature of the method is the rejection of the use of atomic coordinates of individual molecules and their complexes – thus eliminating experimental errors in sample preparation and measurement of nuclear coordinates and the method to determine the affinity of biomolecules with unknown spatial configurations. It is shown that meta-learning increases the response (Recall) of the classification ensemble by 34.9% and the coefficient of determination (R2) of the regression ensemble by 21% compared to the average values. This paper shows that an ensemble with meta-stacking is an asymptotically optimal system for learning. The feature of Super Learning is to use k-fold cross-validation to form first-level predictions that teach second-level models — or meta-models — that combine first-level models optimally. The ability to predict the molecular affinity of six machine learning models is studied, and the efficiency improvement is due to the combination of models in the ensemble by the stacking method. Models that are combined into two consecutive ensembles are shown.
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Aitken, Michael R. F., Mark J. W. Larkin, and Anthony Dickinson. "Super-learning of Causal Judgements." Quarterly Journal of Experimental Psychology B 53, no. 1 (2000): 59–81. http://dx.doi.org/10.1080/027249900392995.

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Lim, Alane. "Machine learning method puts the “super” in super-resolution spectroscopy." Scilight 2021, no. 49 (2021): 491108. http://dx.doi.org/10.1063/10.0009031.

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Han, Tong, Li Zhao, and Chuang Wang. "Research on Super-resolution Image Based on Deep Learning." International Journal of Advanced Network, Monitoring and Controls 8, no. 1 (2023): 58–65. http://dx.doi.org/10.2478/ijanmc-2023-0046.

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Abstract Image super-resolution is a kind of important image processing technology in computer vision and image processing. It refers to the process of recovering high-resolution image from low-resolution image. It has a wide range of real-world applications, such as medical imaging, security and others. In addition to improving image perception quality, it also helps improve other computer vision tasks. Compared with traditional methods, deep learning methods show better reconstruction results in the field of image super-resolution reconstruction, and have gradually developed into the mainstream technology. This article will study the depth in the super resolution direction is important method of types of introduction, combed the main image super-resolution reconstruction method, expounds the depth study of several important super-resolution network model, the advantages and disadvantages of different algorithms and adaptive application scenarios are analyzed and compared, this paper expounds the different ways in the super resolution to liquidate, Finally, the potential problems of current image super-resolution reconstruction techniques are discussed, and the future development direction is prospected.
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Jiang, Jingyu, Li Zhao, and Yan Jiao. "Research on Image Super-resolution Reconstruction Based on Deep Learning." International Journal of Advanced Network, Monitoring and Controls 7, no. 1 (2022): 1–21. http://dx.doi.org/10.2478/ijanmc-2022-0001.

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Abstract Image super-resolution reconstruction (SR) aims to use a specific algorithm to restore a low-resolution blurred image in the same scene into a high-resolution clear image. Due to its wide application value and theoretical value, image super-resolution reconstruction technology has become a research hotspot in the field of computer vision and image processing, and has attracted widespread attention from researchers. Compared with traditional methods, deep learning methods have shown better reconstruction effects in the field of image super-resolution reconstruction, and have gradually developed into the mainstream technology. Therefore, this paper classifies the image super-resolution reconstruction problem systematically according to the structure of the network model, and divides it into two categories: the super-division method based on the convolutional neural network model and the super-division method based on the generative confrontation network model. The main image super-resolution reconstruction methods are sorted out, several more important deep learning super-resolution reconstruction models are described, the advantages and disadvantages of different algorithms and the applicable application scenarios are analyzed and compared, and the different types of super-resolution algorithms are discussed. The method of mutual fusion and image and video quality evaluation, and a brief introduction to commonly used data sets. Finally, the potential problems faced by the current image super-resolution reconstruction technology are discussed, and a new outlook for the future development direction is made.
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Demontis, Ambra, Marco Melis, Battista Biggio, Giorgio Fumera, and Fabio Roli. "Super-Sparse Learning in Similarity Spaces." IEEE Computational Intelligence Magazine 11, no. 4 (2016): 36–45. http://dx.doi.org/10.1109/mci.2016.2601702.

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Strack, Rita. "Deep learning advances super-resolution imaging." Nature Methods 15, no. 6 (2018): 403. http://dx.doi.org/10.1038/s41592-018-0028-9.

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Dissertations / Theses on the topic "Super learning"

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Lindberg, Magnus. "An Imitation-Learning based Agentplaying Super Mario." Thesis, Blekinge Tekniska Högskola, Institutionen för kreativa teknologier, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4529.

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Context. Developing an Artificial Intelligence (AI) agent that canpredict and act in all possible situations in the dynamic environmentsthat modern video games often consists of is on beforehand nearly im-possible and would cost a lot of money and time to create by hand. Bycreating a learning AI agent that could learn by itself by studying itsenvironment with the help of Reinforcement Learning (RL) it wouldsimplify this task. Another wanted feature that often is required is AIagents with a natural acting behavior and a try to solve that problemcould be to imitating a human by using Imitation Learning (IL). Objectives. The purpose of this investigation is to study if it is pos-sible to create a learning AI agent feasible to play and complete somelevels in a platform game with the combination of the two learningtechniques RL and IL. Methods. To be able to investigate the research question an imple-mentation is done that combines one RL technique and one IL tech-nique. By letting a set of human players play the game their behavioris saved and applied to the agents. The RL is then used to train andtweak the agents playing performance. A couple of experiments areexecuted to evaluate the differences between the trained agents againsttheir respective human teacher. Results. The results of these experiments showed promising indica-tions that the agents during different phases of the experiments hadsimilarly behavior compared to their human trainers. The agents alsoperformed well when comparing them to other already existing ones. Conclusions. To conclude there is promising results of creating dy-namical agents with natural behavior with the combination of RL andIL and that it with additional adjustments would make it performeven better as a learning AI with a more natural behavior.
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Kumar, Sanjeev. "Priors and learning based methods for super-resolution." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3397852.

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Thesis (Ph. D.)--University of California, San Diego, 2010.<br>Title from first page of PDF file (viewed April 14, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 96-102).
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Pickup, Lyndsey C. "Machine learning in multi-frame image super-resolution." Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:88c6968f-1e62-4d89-bd70-604bf1f41007.

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Multi-frame image super-resolution is a procedure which takes several noisy low-resolution images of the same scene, acquired under different conditions, and processes them together to synthesize one or more high-quality super-resolution images, with higher spatial frequency, and less noise and image blur than any of the original images. The inputs can take the form of medical images, surveillance footage, digital video, satellite terrain imagery, or images from many other sources. This thesis focuses on Bayesian methods for multi-frame super-resolution, which use a prior distribution over the super-resolution image. The goal is to produce outputs which are as accurate as possible, and this is achieved through three novel super-resolution schemes presented in this thesis. Previous approaches obtained the super-resolution estimate by first computing and fixing the imaging parameters (such as image registration), and then computing the super-resolution image with this registration. In the first of the approaches taken here, superior results are obtained by optimizing over both the registrations and image pixels, creating a complete simultaneous algorithm. Additionally, parameters for the prior distribution are learnt automatically from data, rather than being set by trial and error. In the second approach, uncertainty in the values of the imaging parameters is dealt with by marginalization. In a previous Bayesian image super-resolution approach, the marginalization was over the super-resolution image, necessitating the use of an unfavorable image prior. By integrating over the imaging parameters rather than the image, the novel method presented here allows for more realistic prior distributions, and also reduces the dimension of the integral considerably, removing the main computational bottleneck of the other algorithm. Finally, a domain-specific image prior, based upon patches sampled from other images, is presented. For certain types of super-resolution problems where it is applicable, this sample-based prior gives a significant improvement in the super-resolution image quality.
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Ouyang, Wei. "Deep Learning for Advanced Microscopy." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC174/document.

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Contexte: La microscopie joue un rôle important en biologie depuis plusieurs siècles, mais sa résolution a longtemps été limitée à environ 250 nm, de sorte que nombre de structures biologiques (virus, vésicules, pores nucléaires, synapses) ne pouvaient être résolues. Au cours de la dernière décennie, plusieurs méthodes de super-résolution ont été développées pour dépasser cette limite. Parmi ces techniques, les plus puissantes et les plus utilisées reposent sur la localisation de molécules uniques (microscopie à localisation de molécule unique, ou SMLM), comme PALM et STORM. En localisant précisément les positions de molécules fluorescentes isolées dans des milliers d'images de basse résolution acquises de manière séquentielle, la SMLM peut atteindre des résolutions de 20 à 50 nm voire mieux. Cependant, cette technique est intrinsèquement lente car elle nécessite l’accumulation d’un très grand nombre d’images et de localisations pour obtenir un échantillonnage super-résolutif des structures fluorescentes. Cette lenteur (typiquement ~ 30 minutes par image super-résolutive) rend difficile l'utilisation de la SMLM pour l'imagerie cellulaire à haut débit ou en cellules vivantes. De nombreuses méthodes ont été proposées pour pallier à ce problème, principalement en améliorant les algorithmes de localisation pour localiser des molécules proches, mais la plupart de ces méthodes compromettent la résolution spatiale et entraînent l’apparition d’artefacts. Méthodes et résultats: Nous avons adopté une stratégie de transformation d’image en image basée sur l'apprentissage profond dans le but de restaurer des images SMLM parcimonieuses et par là d’améliorer la vitesse d’acquisition et la qualité des images super-résolutives. Notre méthode, ANNA-PALM, s’appuie sur des développements récents en apprentissage profond, notamment l’architecture U-net et les modèles génératifs antagonistes (GANs). Nous montrons des validations de la méthode sur des images simulées et des images expérimentales de différentes structures cellulaires (microtubules, pores nucléaires et mitochondries). Ces résultats montrent qu’après un apprentissage sur moins de 10 images de haute qualité, ANNA-PALM permet de réduire le temps d’acquisition d’images SMLM, à qualité comparable, d’un facteur 10 à 100. Nous avons également montré que ANNA-PALM est robuste à des altérations de la structure biologique, ainsi qu’à des changements de paramètres de microscopie. Nous démontrons le potentiel applicatif d’ANNA-PALM pour la microscopie à haut débit en imageant ~ 1000 cellules à haute résolution en environ 3 heures. Enfin, nous avons conçu un outil pour estimer et réduire les artefacts de reconstruction en mesurant la cohérence entre l’image reconstruite et l’image en épi-fluorescence. Notre méthode permet une microscopie super-résolutive plus rapide et plus douce, compatible avec l’imagerie haut débit, et ouvre une nouvelle voie vers l'imagerie super-résolutive des cellules vivantes. La performance des méthodes d'apprentissage profond augmente avec la quantité des données d’entraînement. Le partage d’images au sein de la communauté de microscopie offre en principe un moyen peu coûteux d’augmenter ces données. Cependant, il est souvent difficile d'échanger ou de partager des données de SMLM, car les tables de localisation seules ont souvent une taille de plusieurs gigaoctets et il n'existe pas de plate-forme de visualisation dédiée aux données SMLM. Nous avons développé un format de fichier pour compresser sans perte des tables de localisation, ainsi qu’une plateforme web (https://shareloc.xyz) qui permet de visualiser et de partager facilement des données SMLM 2D ou 3D. A l’avenir, cette plate-forme pourrait grandement améliorer les performances des modèles d'apprentissage en profondeur, accélérer le développement des outils, faciliter la réanalyse des données et promouvoir la recherche reproductible et la science ouverte<br>Background: Microscopy plays an important role in biology since several centuries, but its resolution has long been limited to ~250nm due to diffraction, leaving many important biological structures (e.g. viruses, vesicles, nuclear pores, synapses) unresolved. Over the last decade, several super-resolution methods have been developed that break this limit. Among the most powerful and popular super-resolution techniques are those based on single molecular localization (single molecule localization microscopy, or SMLM) such as PALM and STORM. By precisely localizing positions of isolated fluorescent molecules in thousands or more sequentially acquired diffraction limited images, SMLM can achieve resolutions of 20-50 nm or better. However, SMLM is inherently slow due to the necessity to accumulate enough localizations to achieve high resolution sampling of the fluorescent structures. The drawback in acquisition speed (typically ~30 minutes per super-resolution image) makes it difficult to use SMLM in high-throughput and live cell imaging. Many methods have been proposed to address this issue, mostly by improving the localization algorithms to localize overlapping spots, but most of them compromise spatial resolution and cause artifacts.Methods and results: In this work, we applied deep learning based image-to-image translation framework for improving imaging speed and quality by restoring information from rapidly acquired low quality SMLM images. By utilizing recent advances in deep learning including the U-net and Generative Adversarial Networks, we developed our method Artificial Neural Network Accelerated PALM (ANNA-PALM) which is capable of learning structural information from training images and using the trained model to accelerate SMLM imaging by tens to hundreds folds. With experimentally acquired images of different cellular structures (microtubules, nuclear pores and mitochondria), we demonstrated that deep learning can efficiently capture the structural information from less than 10 training samples and reconstruct high quality super-resolution images from sparse, noisy SMLM images obtained with much shorter acquisitions than usual for SMLM. We also showed that ANNA-PALM is robust to possible variations between training and testing conditions, due either to changes in the biological structure or to changes in imaging parameters. Furthermore, we take advantage of the acceleration provided by ANNA-PALM to perform high throughput experiments, showing acquisition of ~1000 cells at high resolution in ~3 hours. Additionally, we designed a tool to estimate and reduce possible artifacts is designed by measuring the consistency between the reconstructed image and the experimental wide-field image. Our method enables faster and gentler imaging which can be applied to high-throughput, and provides a novel avenue towards live cell high resolution imaging. Deep learning methods rely on training data and their performance can be improved even further with more training data. One cheap way to obtain more training data is through data sharing within the microscopy community. However, it often difficult to exchange or share localization microscopy data, because localization tables alone are typically several gigabytes in size, and there is no dedicated platform for localization microscopy data which provide features such as rendering, visualization and filtering. To address these issues, we developed a file format that can losslessly compress localization tables into smaller files, alongside with a web platform called ShareLoc (https://shareloc.xyz) that allows to easily visualize and share 2D or 3D SMLM data. We believe that this platform can greatly improve the performance of deep learning models, accelerate tool development, facilitate data re-analysis and further promote reproducible research and open science
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Yelibi, Lionel. "Introduction to fast Super-Paramagnetic Clustering." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31332.

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We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm. This is directly compared to a modified implementation of a maximum likelihood approach to fast-Super-Paramagnetic Clustering (f-SPC). The methods are first applied standard toy test-case problems, and then to a dataset of 447 stocks traded on the New York Stock Exchange (NYSE) over 1249 days. The signal to noise ratio of stock market correlation matrices is briefly considered. Our result recover approximately clusters representative of standard economic sectors and mixed clusters whose dynamics shine light on the adaptive nature of financial markets and raise concerns relating to the effectiveness of industry based static financial market classification in the world of real-time data-analytics. A key result is that we show that the standard maximum likelihood methods are confirmed to converge to solutions within a Super-Paramagnetic (SP) phase. We use insights arising from this to discuss the implications of using a Maximum Entropy Principle (MEP) as opposed to the Maximum Likelihood Principle (MLP) as an optimization device for this class of problems.
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Bégin, Isabelle. "Camera-independent learning and image quality assessment for super-resolution." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102957.

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An increasing number of applications require high-resolution images in situations where the access to the sensor and the knowledge of its specifications are limited. In this thesis, the problem of blind super-resolution is addressed, here defined as the estimation of a high-resolution image from one or more low-resolution inputs, under the condition that the degradation model parameters are unknown. The assessment of super-resolved results, using objective measures of image quality, is also addressed.<br>Learning-based methods have been successfully applied to the single frame super-resolution problem in the past. However, sensor characteristics such as the Point Spread Function (PSF) must often be known. In this thesis, a learning-based approach is adapted to work without the knowledge of the PSF thus making the framework camera-independent. However, the goal is not only to super-resolve an image under this limitation, but also to provide an estimation of the best PSF, consisting of a theoretical model with one unknown parameter.<br>In particular, two extensions of a method performing belief propagation on a Markov Random Field are presented. The first method finds the best PSF parameter by performing a search for the minimum mean distance between training examples and patches from the input image. In the second method, the best PSF parameter and the super-resolution result are found simultaneously by providing a range of possible PSF parameters from which the super-resolution algorithm will choose from. For both methods, a first estimate is obtained through blind deconvolution and an uncertainty is calculated in order to restrict the search.<br>Both camera-independent adaptations are compared and analyzed in various experiments, and a set of key parameters are varied to determine their effect on both the super-resolution and the PSF parameter recovery results. The use of quality measures is thus essential to quantify the improvements obtained from the algorithms. A set of measures is chosen that represents different aspects of image quality: the signal fidelity, the perceptual quality and the localization and scale of the edges.<br>Results indicate that both methods improve similarity to the ground truth and can in general refine the initial PSF parameter estimate towards the true value. Furthermore, the similarity measure results show that the chosen learning-based framework consistently improves a measure designed for perceptual quality.
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Jain, Vinit. "Deep Learning based Video Super- Resolution in Computer Generated Graphics." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292687.

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Super-Resolution is a widely studied problem in the field of computer vision, where the purpose is to increase the resolution of, or super-resolve, image data. In Video Super-Resolution, maintaining temporal coherence for consecutive video frames requires fusing information from multiple frames to super-resolve one frame. Current deep learning methods perform video super-resolution, yet most of them focus on working with natural datasets. In this thesis, we use a recurrent back-projection network for working with a dataset of computer-generated graphics, with example applications including upsampling low-resolution cinematics for the gaming industry. The dataset comes from a variety of gaming content, rendered in (3840 x 2160) resolution. The objective of the network is to produce the upscaled version of the low-resolution frame by learning an input combination of a low-resolution frame, a sequence of neighboring frames, and the optical flow between each neighboring frame and the reference frame. Under the baseline setup, we train the model to perform 2x upsampling from (1920 x 1080) to (3840 x 2160) resolution. In comparison against the bicubic interpolation method, our model achieved better results by a margin of 2dB for Peak Signal-to-Noise Ratio (PSNR), 0.015 for Structural Similarity Index Measure (SSIM), and 9.3 for the Video Multi-method Assessment Fusion (VMAF) metric. In addition, we further demonstrate the susceptibility in the performance of neural networks to changes in image compression quality, and the inefficiency of distortion metrics to capture the perceptual details accurately.<br>Superupplösning är ett allmänt studerat problem inom datorsyn, där syftet är att öka upplösningen på eller superupplösningsbilddata. I Video Super- Resolution kräver upprätthållande av tidsmässig koherens för på varandra följande videobilder sammanslagning av information från flera bilder för att superlösa en bildruta. Nuvarande djupinlärningsmetoder utför superupplösning i video, men de flesta av dem fokuserar på att arbeta med naturliga datamängder. I denna avhandling använder vi ett återkommande bakprojektionsnätverk för att arbeta med en datamängd av datorgenererad grafik, med exempelvis applikationer inklusive upsampling av film med låg upplösning för spelindustrin. Datauppsättningen kommer från en mängd olika spelinnehåll, återgivna i (3840 x 2160) upplösning. Målet med nätverket är att producera en uppskalad version av en ram med låg upplösning genom att lära sig en ingångskombination av en lågupplösningsram, en sekvens av intilliggande ramar och det optiska flödet mellan varje intilliggande ram och referensramen. Under grundinställningen tränar vi modellen för att utföra 2x uppsampling från (1920 x 1080) till (3840 x 2160) upplösning. Jämfört med den bicubiska interpoleringsmetoden uppnådde vår modell bättre resultat med en marginal på 2 dB för Peak Signal-to-Noise Ratio (PSNR), 0,015 för Structural Similarity Index Measure (SSIM) och 9.3 för Video Multimethod Assessment Fusion (VMAF) mätvärde. Dessutom demonstrerar vi vidare känsligheten i neuronal nätverk för förändringar i bildkomprimeringskvaliteten och ineffektiviteten hos distorsionsmätvärden för att fånga de perceptuella detaljerna exakt.
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Donnot, Benjamin. "Deep learning methods for predicting flows in power grids : novel architectures and algorithms." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS060/document.

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Cette thèse porte sur les problèmes de sécurité sur le réseau électrique français exploité par RTE, le Gestionnaire de Réseau de Transport (GRT). Les progrès en matière d'énergie durable, d'efficacité du marché de l'électricité ou de nouveaux modes de consommation poussent les GRT à exploiter le réseau plus près de ses limites de sécurité. Pour ce faire, il est essentiel de rendre le réseau plus "intelligent". Pour s'attaquer à ce problème, ce travail explore les avantages des réseaux neuronaux artificiels. Nous proposons de nouveaux algorithmes et architectures d'apprentissage profond pour aider les opérateurs humains (dispatcheurs) à prendre des décisions que nous appelons " guided dropout ". Ceci permet de prévoir les flux électriques consécutifs à une modification volontaire ou accidentelle du réseau. Pour se faire, les données continues (productions et consommations) sont introduites de manière standard, via une couche d'entrée au réseau neuronal, tandis que les données discrètes (topologies du réseau électrique) sont encodées directement dans l'architecture réseau neuronal. L’architecture est modifiée dynamiquement en fonction de la topologie du réseau électrique en activant ou désactivant des unités cachées. Le principal avantage de cette technique réside dans sa capacité à prédire les flux même pour des topologies de réseau inédites. Le "guided dropout" atteint une précision élevée (jusqu'à 99% de précision pour les prévisions de débit) tout en allant 300 fois plus vite que des simulateurs de grille physiques basés sur les lois de Kirchoff, même pour des topologies jamais vues, sans connaissance détaillée de la structure de la grille. Nous avons également montré que le "guided dropout" peut être utilisé pour classer par ordre de gravité des évènements pouvant survenir. Dans cette application, nous avons démontré que notre algorithme permet d'obtenir le même risque que les politiques actuellement mises en œuvre tout en n'exigeant que 2 % du budget informatique. Le classement reste pertinent, même pour des cas de réseau jamais vus auparavant, et peut être utilisé pour avoir une estimation globale de la sécurité globale du réseau électrique<br>This thesis addresses problems of security in the French grid operated by RTE, the French ``Transmission System Operator'' (TSO). Progress in sustainable energy, electricity market efficiency, or novel consumption patterns push TSO's to operate the grid closer to its security limits. To this end, it is essential to make the grid ``smarter''. To tackle this issue, this work explores the benefits of artificial neural networks. We propose novel deep learning algorithms and architectures to assist the decisions of human operators (TSO dispatchers) that we called “guided dropout”. This allows the predictions on power flows following of a grid willful or accidental modification. This is tackled by separating the different inputs: continuous data (productions and consumptions) are introduced in a standard way, via a neural network input layer while discrete data (grid topologies) are encoded directly in the neural network architecture. This architecture is dynamically modified based on the power grid topology by switching on or off the activation of hidden units. The main advantage of this technique lies in its ability to predict the flows even for previously unseen grid topologies. The "guided dropout" achieves a high accuracy (up to 99% of precision for flow predictions) with a 300 times speedup compared to physical grid simulators based on Kirchoff's laws even for unseen contingencies, without detailed knowledge of the grid structure. We also showed that guided dropout can be used to rank contingencies that might occur in the order of severity. In this application, we demonstrated that our algorithm obtains the same risk as currently implemented policies while requiring only 2% of today's computational budget. The ranking remains relevant even handling grid cases never seen before, and can be used to have an overall estimation of the global security of the power grid
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Kim, Max. "Improving Knee Cartilage Segmentation using Deep Learning-based Super-Resolution Methods." Thesis, KTH, Medicinteknik och hälsosystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297900.

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Segmentation of the knee cartilage is an important step for surgery planning and manufacturing patient-specific prostheses. What has been a promising technology in recent years is deep learning-based super-resolution methods that are composed of feed-forward models which have been successfully applied on natural and medical images. This thesis aims to test the feasibility to super-resolve thick slice 2D sequence acquisitions and acquire sufficient segmentation accuracy of the articular cartilage in the knee. The investigated approaches are single- and multi-contrast super-resolution, where the contrasts are either based on the 2D sequence, 3D sequence, or both. The deep learning models investigated are based on predicting the residual image between the high- and low-resolution image pairs, finding the hidden latent features connecting the image pairs, and approximating the end-to-end non-linear mapping between the low- and high-resolution image pairs. The results showed a slight improvement in segmentation accuracy with regards to the baseline bilinear interpolation for the single-contrast super-resolution, however, no notable improvements in segmentation accuracy were observed for the multi-contrast case. Although the multi-contrast approach did not result in any notable improvements, there are still unexplored areas not covered in this work that are promising and could potentially be covered as future work.<br>Segmentering av knäbrosket är ett viktigt steg för planering inför operationer och tillverkning av patientspecifika proteser. Idag segmenterar man knäbrosk med hjälp av MR-bilder tagna med en 3D-sekvens som både tidskrävande och rörelsekänsligt, vilket kan vara obehagligt för patienten. I samband med 3D-bildtagningar brukar även thick slice 2D-sekvenser tas för diagnostiska skäl, däremot är de inte anpassade för segmentering på grund av för tjocka skivor. På senare tid har djupinlärningsbaserade superupplösningsmetoder uppbyggda av så kallade feed-forwardmodeller visat sig vara väldigt framgångsrikt när det applicerats på verkliga- och medicinska bilder. Syftet med den här rapporten är att testa hur väl superupplösta thick slice 2D-sekvensbildtagningar fungerar för segmentering av ledbrosket i knät. De undersökta tillvägagångssätten är superupplösning av enkel- och flerkontrastbilder, där kontrasten är antingen baserade på 2D-sekvensen, 3D-sekvensen eller både och. Resultaten påvisar en liten förbättring av segmenteringnoggrannhet vid segmentering av enkelkontrastbilderna över baslinjen linjär interpolering. Däremot var det inte någon märkvärdig förbättring i superupplösning av flerkontrastbilderna. Även om superupplösning av flerkontrastmetoden inte gav någon märkbar förbättring segmenteringsresultaten så finns det fortfarande outforskade områden som inte tagits upp i det här arbetet som potentiellt skulle kunna utforskas i framtida arbeten.
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Ceccarelli, Mattia. "Optimization and applications of deep learning algorithms for super-resolution in MRI." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21694/.

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The increasing amount of data produced by modern infrastructures requires instruments of analysis more and more precise, quick, and efficient. For these reasons in the last decades, Machine Learning (ML) and Deep Learning (DL) techniques saw exponential growth in publications and research from the scientific community. In this work are proposed two new frameworks for Deep Learning: Byron written in C++, for fast analysis in a parallelized CPU environment, and NumPyNet written in Python, which provides a clear and understandable interface on deep learning tailored around readability. Byron will be tested on the field of Single Image Super-Resolution for NMR imaging of brains (Nuclear Magnetic Resonance) using pre-trained models for x2 and x4 upscaling which exhibit greater performance than most common non-learning-based algorithms. The work will show that the reconstruction ability of DL models surpasses the interpolation of a bicubic algorithm even with images totally different from the dataset in which they were trained, indicating that the generalization abilities of those deep learning models can be sufficient to perform well even on biomedical data, which contain particular shapes and textures. Ulterior studies will focus on how the same algorithms perform with different conditions for the input, showing a large variance between results.
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Books on the topic "Super learning"

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Managing attention & learning disorders: Super survival strategies. Harold Shaw Publishers, 1997.

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1942-, McDonough Christopher James, ed. Commentum super Martianum. SISMEL edizioni del Galluzzo, 2006.

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Kenyon, Mary Potter. Home schooling from scratch: Simple living, super learning. Gazelle Publications, 1996.

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Dunford, Franco Elizabeth, and Teachers College Reading and Writing Project (Columbia University), eds. Super powers: Reading with print strategies and sight word power. Heinemann, 2015.

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Super teaching: Over 1,000 practical teaching strategies. 3rd ed. Brain Store, 1998.

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The amazing 7-day, super-simple, scripted guide to teaching or learning decimals. Nova Press, 2014.

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The critters, colors, & clouds learning center book: Literacy-based activities for super science fun. Dr. Maggie Allen's Learning Express, 2004.

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English language learning with super support: A workbook for ESL/ESOL/EFL/ELL students : Beginners. Griselda Califa, LLC, 2016.

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Super Learning. Brain Sync, 1995.

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Super Speed Learning. Zygon International, Inc., 1994.

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Book chapters on the topic "Super learning"

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Polley, Eric C., Sherri Rose, and Mark J. van der Laan. "Super Learning." In Targeted Learning. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9782-1_3.

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van der Laan, Mark J., and David Benkeser. "Online Super Learning." In Springer Series in Statistics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65304-4_18.

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Rose, Sherri, and Mark J. van der Laan. "Sequential Super Learning." In Springer Series in Statistics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65304-4_3.

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Du, Ping, Jinhuan Zhang, and Jun Long. "Super-Sampling by Learning-Based Super-Resolution." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05234-8_10.

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Polley, Eric C., and Mark J. van der Laan. "Super Learning for Right-Censored Data." In Targeted Learning. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9782-1_16.

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Ye, Chuyang, Yu Qin, Chenghao Liu, Yuxing Li, Xiangzhu Zeng, and Zhiwen Liu. "Super-Resolved q-Space Deep Learning." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32248-9_65.

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Sui, Yao, Onur Afacan, Ali Gholipour, and Simon K. Warfield. "MRI Super-Resolution Through Generative Degradation Learning." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87231-1_42.

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Huang, Yi, Weixin Bian, Biao Jie, Zhiqiang Zhu, and Wenhu Li. "Image Super-Resolution via Deep Dictionary Learning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46314-3_2.

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Schneider, Jan, Dirk Börner, Peter van Rosmalen, and Marcus Specht. "The Booth: Bringing Out the Super Hero in You." In Adaptive and Adaptable Learning. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45153-4_56.

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Huang, Yongsong, Qingzhong Wang, and Shinichiro Omachi. "Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN." In Machine Learning in Medical Imaging. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21014-3_5.

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Conference papers on the topic "Super learning"

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Landsborough, Jason, Stephen Harding, and Sunny Fugate. "Learning from super-mutants." In GECCO '17: Genetic and Evolutionary Computation Conference. ACM, 2017. http://dx.doi.org/10.1145/3067695.3082525.

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Karambelkar, Dattatray L., and P. J. Kulkarni. "Super-Resolution Using Manifold Learning." In 2011 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2011. http://dx.doi.org/10.1109/cicn.2011.154.

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Changhyun Kim, Kyuha Choi, Ho-young Lee, Kyuyoung Hwang, and Jong Beom Ra. "Robust learning-based super-resolution." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5651057.

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McDonald, Andrew W. E., and Ali Shokoufandeh. "Sparse Super-Regular Networks." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00286.

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Chiapputo, Nicholas, and Colleen P. Bailey. "Memory-efficient single-image super-resolution." In Big Data IV: Learning, Analytics, and Applications, edited by Fauzia Ahmad, Panos P. Markopoulos, and Bing Ouyang. SPIE, 2022. http://dx.doi.org/10.1117/12.2619142.

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Suzuki, Kaiyu, Yasushi Kambayashi, and Tomofumi Matsuzawa. "CrossSiam: k-Fold Cross Representation Learning." In Special Session on Super Distributed and Multi-agent Intelligent Systems. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010972500003116.

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He, Huayong, Ze Li, Jianhong Li, and Xiaocui Peng. "Image Super-Resolution through Pyramid Learning." In 2012 4th International Conference on Digital Home (ICDH). IEEE, 2012. http://dx.doi.org/10.1109/icdh.2012.76.

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Pickup, L. C., S. J. Roberts, and A. Zisserman. "Optimizing and Learning for Super-resolution." In British Machine Vision Conference 2006. British Machine Vision Association, 2006. http://dx.doi.org/10.5244/c.20.46.

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Lu, Xiaoqiang, Haoliang Yuan, Yuan Yuan, Pingkun Yan, Luoqing Li, and Xuelong Li. "Local learning-based image super-resolution." In 2011 IEEE 13th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2011. http://dx.doi.org/10.1109/mmsp.2011.6093843.

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Li, Juan, Jin Wu, Shen Yang, and Jin Liu. "Dictionary learning for image super-resolution." In 2014 33rd Chinese Control Conference (CCC). IEEE, 2014. http://dx.doi.org/10.1109/chicc.2014.6896189.

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Reports on the topic "Super learning"

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Rosencrans, David, Brandon Benton, Grant Buster, et al. Wind Resource Data for Southeast Asia Using a Hybrid Numerical Weather Prediction with Machine Learning Super Resolution Approach. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1984839.

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Withers, Denissia. Engaging Community Food Systems through Learning Garden Programs: Oregon Food Bank's Seed to Supper Program. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.609.

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SUPER-RESOLUTION RECONSTRUCTION AND HIGH-PRECISION TEMPERATURE MEASUREMENT OF THERMAL IMAGES UNDER HIGH- TEMPERATURE SCENES BASED ON NEURAL NETWORK. The Hong Kong Institute of Steel Construction, 2024. http://dx.doi.org/10.18057/ijasc.2024.20.2.9.

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Accurate temperature readings are vital in fire resistance tests, but conventional thermal imagers often lack sufficient resolution, and applying super-resolution algorithms can disrupt the temperature and color correspondence, leading to limited efficiency. To address these issues, a convolutional network tailored for high-temperature scenes is designed for image super-resolution with the internal joint attention sub-residual blocks (JASRB) efficiently integrating channel, spatial attention mechanisms, and convolutional modules. Furthermore, a segmented method is developed for predicting thermal image temperature using color temperature measurements and an interpretable artificial neural network. This approach predicts temperatures in super-resolution thermal images ranging from 400 to 1200°C. Through comparative validation, it is found that the three-neuron neural network approach demonstrates superior prediction accuracy compared to other machine learning methods. The seamlessly combined proposed super-resolution architecture with the temperature measurement method has a predicted RMSE of 20°C for the whole temperature range with over 85% of samples falling within errors of 30°C.
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