Relevant bibliographies by topics / Deep learning with uncertainty

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

Academic literature on the topic 'Deep learning with uncertainty'

Author: Grafiati
Published: 7 July 2024

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Journal articles on the topic "Deep learning with uncertainty"

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Liu, Wei, Xiaodong Yue, Yufei Chen, and Thierry Denoeux. "Trusted Multi-View Deep Learning with Opinion Aggregation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7585–93. http://dx.doi.org/10.1609/aaai.v36i7.20724.

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Multi-view deep learning is performed based on the deep fusion of data from multiple sources, i.e. data with multiple views. However, due to the property differences and inconsistency of data sources, the deep learning results based on the fusion of multi-view data may be uncertain and unreliable. It is required to reduce the uncertainty in data fusion and implement the trusted multi-view deep learning. Aiming at the problem, we revisit the multi-view learning from the perspective of opinion aggregation and thereby devise a trusted multi-view deep learning method. Within this method, we adopt evidence theory to formulate the uncertainty of opinions as learning results from different data sources and measure the uncertainty of opinion aggregation as multi-view learning results through evidence accumulation. We prove that accumulating the evidences from multiple data views will decrease the uncertainty in multi-view deep learning and facilitate to achieve the trusted learning results. Experiments on various kinds of multi-view datasets verify the reliability and robustness of the proposed multi-view deep learning method.
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Oh, Dongpin, and Bonggun Shin. "Improving Evidential Deep Learning via Multi-Task Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7895–903. http://dx.doi.org/10.1609/aaai.v36i7.20759.

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The Evidential regression network (ENet) estimates a continuous target and its predictive uncertainty without costly Bayesian model averaging. However, it is possible that the target is inaccurately predicted due to the gradient shrinkage problem of the original loss function of the ENet, the negative log marginal likelihood (NLL) loss. In this paper, the objective is to improve the prediction accuracy of the ENet while maintaining its efficient uncertainty estimation by resolving the gradient shrinkage problem. A multi-task learning (MTL) framework, referred to as MT-ENet, is proposed to accomplish this aim. In the MTL, we define the Lipschitz modified mean squared error (MSE) loss function as another loss and add it to the existing NLL loss. The Lipschitz modified MSE loss is designed to mitigate the gradient conflict with the NLL loss by dynamically adjusting its Lipschitz constant. By doing so, the Lipschitz MSE loss does not disturb the uncertainty estimation of the NLL loss. The MT-ENet enhances the predictive accuracy of the ENet without losing uncertainty estimation capability on the synthetic dataset and real-world benchmarks, including drug-target affinity (DTA) regression. Furthermore, the MT-ENet shows remarkable calibration and out-of-distribution detection capability on the DTA benchmarks.
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Bajorath, Jürgen. "Understanding uncertainty in deep learning builds confidence." Artificial Intelligence in the Life Sciences 2 (December 2022): 100033. http://dx.doi.org/10.1016/j.ailsci.2022.100033.

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van den Berg, Cornelis A. T., and Ettore F. Meliadò. "Uncertainty Assessment for Deep Learning Radiotherapy Applications." Seminars in Radiation Oncology 32, no. 4 (October 2022): 304–18. http://dx.doi.org/10.1016/j.semradonc.2022.06.001.

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Zheng, Rui, Shulin Zhang, Lei Liu, Yuhao Luo, and Mingzhai Sun. "Uncertainty in Bayesian deep label distribution learning." Applied Soft Computing 101 (March 2021): 107046. http://dx.doi.org/10.1016/j.asoc.2020.107046.

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Lockwood, Owen, and Mei Si. "A Review of Uncertainty for Deep Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 18, no. 1 (October 11, 2022): 155–62. http://dx.doi.org/10.1609/aiide.v18i1.21959.

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Uncertainty is ubiquitous in games, both in the agents playing games and often in the games themselves. Working with uncertainty is therefore an important component of successful deep reinforcement learning agents. While there has been substantial effort and progress in understanding and working with uncertainty for supervised learning, the body of literature for uncertainty aware deep reinforcement learning is less developed. While many of the same problems regarding uncertainty in neural networks for supervised learning remain for reinforcement learning, there are additional sources of uncertainty due to the nature of an interactable environment. In this work, we provide an overview motivating and presenting existing techniques in uncertainty aware deep reinforcement learning. These works show empirical benefits on a variety of reinforcement learning tasks. This work serves to help to centralize the disparate results and promote future research in this area.
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Karimi, Hamed, and Reza Samavi. "Quantifying Deep Learning Model Uncertainty in Conformal Prediction." Proceedings of the AAAI Symposium Series 1, no. 1 (October 3, 2023): 142–48. http://dx.doi.org/10.1609/aaaiss.v1i1.27492.

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Precise estimation of predictive uncertainty in deep neural networks is a critical requirement for reliable decision-making in machine learning and statistical modeling, particularly in the context of medical AI. Conformal Prediction (CP) has emerged as a promising framework for representing the model uncertainty by providing well-calibrated confidence levels for individual predictions. However, the quantification of model uncertainty in conformal prediction remains an active research area, yet to be fully addressed. In this paper, we explore state-of-the-art CP methodologies and their theoretical foundations. We propose a probabilistic approach in quantifying the model uncertainty derived from the produced prediction sets in conformal prediction and provide certified boundaries for the computed uncertainty. By doing so, we allow model uncertainty measured by CP to be compared by other uncertainty quantification methods such as Bayesian (e.g., MC-Dropout and DeepEnsemble) and Evidential approaches.
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Caldeira, João, and Brian Nord. "Deeply uncertain: comparing methods of uncertainty quantification in deep learning algorithms." Machine Learning: Science and Technology 2, no. 1 (December 4, 2020): 015002. http://dx.doi.org/10.1088/2632-2153/aba6f3.

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Da Silva, Felipe Leno, Pablo Hernandez-Leal, Bilal Kartal, and Matthew E. Taylor. "Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5792–99. http://dx.doi.org/10.1609/aaai.v34i04.6036.

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Although Reinforcement Learning (RL) has been one of the most successful approaches for learning in sequential decision making problems, the sample-complexity of RL techniques still represents a major challenge for practical applications. To combat this challenge, whenever a competent policy (e.g., either a legacy system or a human demonstrator) is available, the agent could leverage samples from this policy (advice) to improve sample-efficiency. However, advice is normally limited, hence it should ideally be directed to states where the agent is uncertain on the best action to execute. In this work, we propose Requesting Confidence-Moderated Policy advice (RCMP), an action-advising framework where the agent asks for advice when its epistemic uncertainty is high for a certain state. RCMP takes into account that the advice is limited and might be suboptimal. We also describe a technique to estimate the agent uncertainty by performing minor modifications in standard value-function-based RL methods. Our empirical evaluations show that RCMP performs better than Importance Advising, not receiving advice, and receiving it at random states in Gridworld and Atari Pong scenarios.
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Kawano, Yasufumi, Yoshiki Nota, Rinpei Mochizuki, and Yoshimitsu Aoki. "Non-Deep Active Learning for Deep Neural Networks." Sensors 22, no. 14 (July 13, 2022): 5244. http://dx.doi.org/10.3390/s22145244.

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One way to improve annotation efficiency is active learning. The goal of active learning is to select images from many unlabeled images, where labeling will improve the accuracy of the machine learning model the most. To select the most informative unlabeled images, conventional methods use deep neural networks with a large number of computation nodes and long computation time, but we propose a non-deep neural network method that does not require any additional training for unlabeled image selection. The proposed method trains a task model on labeled images, and then the model predicts unlabeled images. Based on this prediction, an uncertainty indicator is generated for each unlabeled image. Images with a high uncertainty index are considered to have a high information content, and are selected for annotation. Our proposed method is based on a very simple and powerful idea: select samples near the decision boundary of the model. Experimental results on multiple datasets show that the proposed method achieves higher accuracy than conventional active learning methods on multiple tasks and up to 14 times faster execution time from 1.2 × 106 s to 8.3 × 104 s. The proposed method outperforms the current SoTA method by 1% accuracy on CIFAR-10.
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Dissertations / Theses on the topic "Deep learning with uncertainty"

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Kim, Alisa. "Deep Learning for Uncertainty Measurement." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22161.

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Diese Arbeit konzentriert sich auf die Lösung des Problems der Unsicherheitsmessung und ihrer Auswirkungen auf Geschäftsentscheidungen, wobei zwei Ziele verfolgt werden: Erstens die Entwicklung und Validierung robuster Modelle zur Quantifizierung der Unsicherheit, wobei insbesondere sowohl die etablierten statistischen Modelle als auch neu entwickelte maschinelle Lernwerkzeuge zum Einsatz kommen. Das zweite Ziel dreht sich um die industrielle Anwendung der vorgeschlagenen Modelle. Die Anwendung auf reale Fälle bei der Messung der Volatilität oder bei einer riskanten Entscheidung ist mit einem direkten und erheblichen Gewinn oder Verlust verbunden. Diese These begann mit der Untersuchung der impliziten Volatilität (IV) als Proxy für die Wahrnehmung der Unsicherheit von Anlegern für eine neue Klasse von Vermögenswerten - Kryptowährungen. Das zweite Papier konzentriert sich auf Methoden zur Identifizierung risikofreudiger Händler und nutzt die DNN-Infrastruktur, um das Risikoverhalten von Marktakteuren, das auf Unsicherheit beruht und diese aufrechterhält, weiter zu untersuchen. Das dritte Papier befasste sich mit dem herausfordernden Bestreben der Betrugserkennung 3 und bot das Entscheidungshilfe-modell, das eine genauere und interpretierbarere Bewertung der zur Prüfung eingereichten Finanzberichte ermöglichte. Angesichts der Bedeutung der Risikobewertung und der Erwartungen der Agenten für die wirtschaftliche Entwicklung und des Aufbaus der bestehenden Arbeiten von Baker (2016) bot das vierte Papier eine neuartige DL-NLP-basierte Methode zur Quantifizierung der wirtschaftspolitischen Unsicherheit. Die neuen Deep-Learning-basierten Lösungen bieten eine überlegene Leistung gegenüber bestehenden Ansätzen zur Quantifizierung und Erklärung wirtschaftlicher Unsicherheiten und ermöglichen genauere Prognosen, verbesserte Planungskapazitäten und geringere Risiken. Die angebotenen Anwendungsfälle bilden eine Plattform für die weitere Forschung.
This thesis focuses on solving the problem of uncertainty measurement and its impact on business decisions while pursuing two goals: first, develop and validate accurate and robust models for uncertainty quantification, employing both the well established statistical models and newly developed machine learning tools, with particular focus on deep learning. The second goal revolves around the industrial application of proposed models, applying them to real-world cases when measuring volatility or making a risky decision entails a direct and substantial gain or loss. This thesis started with the exploration of implied volatility (IV) as a proxy for investors' perception of uncertainty for a new class of assets - crypto-currencies. The second paper focused on methods to identify risk-loving traders and employed the DNN infrastructure for it to investigate further the risk-taking behavior of market actors that both stems from and perpetuates uncertainty. The third paper addressed the challenging endeavor of fraud detection and offered the decision support model that allowed a more accurate and interpretable evaluation of financial reports submitted for audit. Following the importance of risk assessment and agents' expectations in economic development and building on the existing works of Baker (2016) and their economic policy uncertainty (EPU) index, it offered a novel DL-NLP-based method for the quantification of economic policy uncertainty. In summary, this thesis offers insights that are highly relevant to both researchers and practitioners. The new deep learning-based solutions exhibit superior performance to existing approaches to quantify and explain economic uncertainty, allowing for more accurate forecasting, enhanced planning capacities, and mitigated risks. The offered use-cases provide a road-map for further development of the DL tools in practice and constitute a platform for further research.
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Kim, Alisa [Verfasser]. "Deep Learning for Uncertainty Measurement / Alisa Kim." Berlin : Humboldt-Universität zu Berlin, 2021. http://d-nb.info/1227300824/34.

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Kendall, Alex Guy. "Geometry and uncertainty in deep learning for computer vision." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/287944.

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Deep learning and convolutional neural networks have become the dominant tool for computer vision. These techniques excel at learning complicated representations from data using supervised learning. In particular, image recognition models now out-perform human baselines under constrained settings. However, the science of computer vision aims to build machines which can see. This requires models which can extract richer information than recognition, from images and video. In general, applying these deep learning models from recognition to other problems in computer vision is significantly more challenging. This thesis presents end-to-end deep learning architectures for a number of core computer vision problems; scene understanding, camera pose estimation, stereo vision and video semantic segmentation. Our models outperform traditional approaches and advance state-of-the-art on a number of challenging computer vision benchmarks. However, these end-to-end models are often not interpretable and require enormous quantities of training data. To address this, we make two observations: (i) we do not need to learn everything from scratch, we know a lot about the physical world, and (ii) we cannot know everything from data, our models should be aware of what they do not know. This thesis explores these ideas using concepts from geometry and uncertainty. Specifically, we show how to improve end-to-end deep learning models by leveraging the underlying geometry of the problem. We explicitly model concepts such as epipolar geometry to learn with unsupervised learning, which improves performance. Secondly, we introduce ideas from probabilistic modelling and Bayesian deep learning to understand uncertainty in computer vision models. We show how to quantify different types of uncertainty, improving safety for real world applications.
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Aguilar, Eduardo. "Deep Learning and Uncertainty Modeling in Visual Food Analysis." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/670751.

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Several computer vision approaches have been proposed for tackling food analysis problems, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. However, high food ambiguity, interclass variability and intra-class similarity define a real challenge for the Deep learning and Computer Vision algorithms. With the advent of Convolutional Neural Networks, the complex problem of visual food analysis has experienced significant improvement. Despite this, for real applications, where thousands of foods must be analyzed and recognized it is necessary to better understand what the model learns and, from this, guide its learning on more discriminatives features to improve its accurate and robustness. In this thesis we address the problem of analyzing food images through methods based on deep learning algorithms. There are two distinguishable parts. In the first part, we focus on the food recognition task and delve into uncertainty modeling. First, we propose a new multi-task model that is able to simultaneously predict different food-related tasks. Here, we extend the homoscedastic uncertainty modeling to allow single-label and multilabel classification and propose a regularization term, which jointly weighs the tasks as well as their correlations. Second, we propose a novel prediction scheme based on a class hierarchy that considers local classifiers, in addition to a flat classifier. For this, we define criteria based on the Epistemic Uncertainty estimated from the ’children’ classifiers and the prediction from the ’parent’ classifier to decide the approach to use. And third, we propose three new data augmentation strategies that analysis class-level or sample-level epistemic uncertainty to guide the model training. In the second part we contribute to the design of new methods for food detection (food/nonfood classification), for ensemble of food classifiers and for semantic food detection. First, we proposes an overview of the last advances on food/non-food classification and an optimal model based on the GoogLeNet architecture, Principal Component Analysis, and a Support Vector Machine. Second, we propose a combination of multiple classifiers for food recognition based on two different Convolutional models that complement each other and thus, achieve an improvement in performance. And third, we address the problem of automatic food tray analysis in canteens and restaurants environment through a new approach that integrates in the same framework food localization, recognition and segmentation for semantic food detection. All the methods designed in this thesis are validated and contrasted over relevant public food datasets and the results obtained are reported in detail.
El desafiante problema que plantea el análisis de alimentos, la facilidad para recopilar imágenes de alimentos y sus numerosas aplicaciones para la salud y el ocio son algunos de los factores principales que han incentivado la generación de varios enfoques de visión por computadora para abordar este problema. Sin embargo, la ambigüedad alimentaria, variabilidad entre clases y similitud dentro de la clase definen un desafío real para los algoritmos de aprendizaje profundo y visión por computadora. Con la llegada de las redes neuronales convolucionales, el complejo problema del análisis visual de los alimentos ha experimentado una mejora significativa. A pesar de ello, para aplicaciones reales, donde se deben analizar y reconocer miles de alimentos, es necesario comprender mejor lo que aprende el modelo y, a partir de ello, orientar su aprendizaje en aspectos más discriminatorios para mejorar su precisión y robustez. En esta tesis abordamos el problema del análisis de imágenes de alimentos mediante métodos basados en algoritmos de aprendizaje profundo. Hay dos partes distinguibles. En la primera parte, nos centramos en la tarea de reconocimiento de alimentos y profundizamos en el modelado de incertidumbre. Primero, proponemos un nuevo modelo multi-tarea que es capaz de predecir simultáneamente diferentes tareas relacionadas con los alimentos. Aquí, ampliamos el modelo de incertidumbre homocedástica para permitir la clasificación tanto de etiqueta única como de etiquetas múltiples, y proponemos un término de regularización, que pondera conjuntamente las tareas y sus correlaciones. En segundo lugar, proponemos un novedoso esquema de predicción basado en una jerarquía de clases que considera clasificadores locales y un clasificador plano. Para decidir el enfoque a utilizar (plano o local), definimos criterios basados en la incertidumbre epistémica estimada a partir de los clasificadores de 'hijos' y la predicción del clasificador de 'padres'. Y tercero, proponemos tres nuevas estrategias de aumento de datos que analizan la incertidumbre epistémica a nivel de clase o de muestra para guiar el entrenamiento del modelo. En la segunda parte contribuimos al diseño de nuevos métodos para la detección de alimentos (clasificación food/non-food), para generar predicciones a partir de un conjunto de clasificadores de alimentos y para la detección semántica de alimentos. Primero, establecemos en estado del arte en cuanto a últimos avances en clasificación de food/non-food y proponemos un modelo óptimo basado en la arquitectura GoogLeNet, Análisis de Componentes Principales (PCA) y una Máquina de Vector de Soporte (SVM). En segundo lugar, proponemos medidas difusas para combinar múltiples clasificadores para el reconocimiento de alimentos basados en dos arquitecturas convolucionales diferentes que se complementan y de este modo, logran una mejora en el rendimiento. Y tercero, abordamos el problema del análisis automático de bandejas de alimentos en el entorno de comedores y restaurantes a través de un nuevo enfoque que integra en un mismo marco la localización, el reconocimiento y la segmentación de alimentos para la detección semántica de alimentos. Todos los métodos diseñados en esta tesis están validados y contrastados sobre conjuntos de datos de alimentos públicos relevantes y los resultados obtenidos se informan en detalle.
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Ekelund, Måns. "Uncertainty Estimation for Deep Learning-based LPI Radar Classification : A Comparative Study of Bayesian Neural Networks and Deep Ensembles." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301653.

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Deep Neural Networks (DNNs) have shown promising results in classifying known Low-probability-of-intercept (LPI) radar signals in noisy environments. However, regular DNNs produce low-quality confidence and uncertainty estimates, making them unreliable, which inhibit deployment in real-world settings. Hence, the need for robust uncertainty estimation methods has grown, and two categories emerged, Bayesian approximation and ensemble learning. As autonomous LPI radar classification is deployed in safety-critical environments, this study compares Bayesian Neural Networks (BNNs) and Deep Ensembles (DEs) as uncertainty estimation methods. We synthetically generate a training and test data set, as well as a shifted data set where subtle changes are made to the signal parameters. The methods are evaluated on predictive performance, relevant confidence and uncertainty estimation metrics, and method-related metrics such as model size, training, and inference time. Our results show that our DE achieves slightly higher predictive performance than the BNN on both in-distribution and shifted data with an accuracy of 74% and 32%, respectively. Further, we show that both methods exhibit more cautiousness in their predictions compared to a regular DNN for in-distribution data, while the confidence quality significantly degrades on shifted data. Uncertainty in predictions is evaluated as predictive entropy, and we show that both methods exhibit higher uncertainty on shifted data. We also show that the signal-to-noise ratio affects uncertainty compared to a regular DNN. However, none of the methods exhibit uncertainty when making predictions on unseen signal modulation patterns, which is not a desirable behavior. Further, we conclude that the amount of available resources could influence the choice of the method since DEs are resource-heavy, requiring more memory than a regular DNN or BNN. On the other hand, the BNN requires a far longer training time.
Tidigare studier har visat att djupa neurala nätverk (DNN) kan klassificera signalmönster för en speciell typ av radar (LPI) som är skapad för att vara svår att identifiera och avlyssna. Traditionella neurala nätverk saknar dock ett naturligt sätt att skatta osäkerhet, vilket skadar deras pålitlighet och förhindrar att de används i säkerhetskritiska miljöer. Osäkerhetsskattning för djupinlärning har därför vuxit och på senare tid blivit ett stort område med två tydliga kategorier, Bayesiansk approximering och ensemblemetoder. LPI radarklassificering är av stort intresse för försvarsindustrin, och tekniken kommer med största sannolikhet att appliceras i säkerhetskritiska miljöer. I denna studie jämför vi Bayesianska neurala nätverk och djupa ensembler för LPI radarklassificering. Resultaten från studien pekar på att en djup ensemble uppnår högre träffsäkerhet än ett Bayesianskt neuralt nätverk och att båda metoderna uppvisar återhållsamhet i sina förutsägelser jämfört med ett traditionellt djupt neuralt nätverk. Vi skattar osäkerhet som entropi och visar att osäkerheten i metodernas slutledningar ökar både på höga brusnivåer och på data som är något förskjuten från den kända datadistributionen. Resultaten visar dock att metodernas osäkerhet inte ökar jämfört med ett vanligt nätverk när de får se tidigare osedda signal mönster. Vi visar också att val av metod kan influeras av tillgängliga resurser, eftersom djupa ensembler kräver mycket minne jämfört med ett traditionellt eller Bayesianskt neuralt nätverk.
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Lee, Hong Yun. "Deep Learning for Visual-Inertial Odometry: Estimation of Monocular Camera Ego-Motion and its Uncertainty." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu156331321922759.

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Cofré, Martel Sergio Manuel Ignacio. "A deep learning based framework for physical assets' health prognostics under uncertainty for big Machinery Data." Tesis, Universidad de Chile, 2018. http://repositorio.uchile.cl/handle/2250/168080.

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Magíster en Ciencias de la Ingeniería, Mención Mecánica
El desarrollo en tecnología de mediciones ha permitido el monitoreo continuo de sistemas complejos a través de múltiples sensores, generando así grandes bases de datos. Estos datos normalmente son almacenados para ser posteriormente analizados con técnicas tradicionales de Prognostics and Health Management (PHM). Sin embargo, muchas veces, gran parte de esta información es desperdiciada, ya que los métodos tradicionales de PHM requieren de conocimiento experto sobre el sistema para su implementación. Es por esto que, para estimar parámetros relacionados a confiabilidad, los enfoques basados en análisis de datos pueden utilizarse para complementar los métodos de PHM. El objetivo de esta tesis consiste en desarrollar e implementar un marco de trabajo basado en técnicas de Aprendizaje Profundo para la estimación del estado de salud de sistemas y componentes, utilizando datos multisensoriales de monitoreo. Para esto, se definen los siguientes objetivos específicos: Desarrollar una arquitectura capaz de extraer características temporales y espaciales de los datos. Proponer un marco de trabajo para la estimación del estado de salud, y validarlo utilizando dos conjuntos de datos: C-MAPSS turbofan engine, y baterías ion-litio CS2. Finalmente, entregar una estimación de la propagación de la incertidumbre en los pronósticos del estado de salud. Se propone una estructura que integre las ventajas de relación espacial de las Convolutional Neural Networks, junto con el análisis secuencial de las Long-Short Term Memory Recurrent Neural Networks. Utilizando Dropout tanto para la regularización, como también para una aproximación bayesiana para la estimación de incertidumbre de los modelos. De acuerdo con lo anterior, la arquitectura propuesta recibe el nombre CNNBiLSTM. Para los datos de C-MAPSS se entrenan cuatro modelos diferentes, uno para cada subconjunto de datos, con el objetivo de estimar la vida remanente útil. Los modelos arrojan resultados superiores al estado del arte en la raíz del error medio cuadrado (RMSE), mostrando robustez en el proceso de entrenamiento, y baja incertidumbre en sus predicciones. Resultados similares se obtienen para el conjunto de datos CS2, donde el modelo entrenado con todas las celdas de batería logra estimar el estado de carga y el estado de salud con un bajo RMSE y una pequeña incertidumbre sobre su estimación de valores. Los resultados obtenidos por los modelos entrenados muestran que la arquitectura propuesta es adaptable a diferentes sistemas y puede obtener relaciones temporales abstractas de los datos sensoriales para la evaluación de confiabilidad. Además, los modelos muestran robustez durante el proceso de entrenamiento, así como una estimación precisa con baja incertidumbre.
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Martin, Alice. "Deep learning models and algorithms for sequential data problems : applications to language modelling and uncertainty quantification." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS007.

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Dans ce manuscrit de thèse, nous développons de nouveaux algorithmes et modèles pour résoudre les problèmes d'apprentissage profond sur de la donnée séquentielle, en partant des problématiques posées par l'apprentissage des modèles de langage basés sur des réseaux de neurones. Un premier axe de recherche développe de nouveaux modèles génératifs profonds basés sur des méthodes de Monte Carlo Séquentielles (SMC), qui permettent de mieux modéliser la diversité du langage, ou de mieux quantifier l'incertitude pour des problèmes de régression séquentiels. Un deuxième axe de recherche vise à faciliter l'utilisation de techniques de SMC dans le cadre de l'apprentissage profond, en développant un nouvel algorithme de lissage à coût computationnel largement réduit, et qui s'applique à un scope plus large de modèles à espace d'états, notamment aux modèles génératifs profonds. Finalement, un troisième axe de recherche propose le premier algorithme d'apprentissage par renforcement permettant d'apprendre des modèles de langage conditionnels "ex-nihilo" (i.e sans jeu de données supervisé), basé sur un mécanisme de troncation de l'espace d'actions par un modèle de langage pré-entrainé
In this thesis, we develop new models and algorithms to solve deep learning tasks on sequential data problems, with the perspective of tackling the pitfalls of current approaches for learning language models based on neural networks. A first research work develops a new deep generative model for sequential data based on Sequential Monte Carlo Methods, that enables to better model diversity in language modelling tasks, and better quantify uncertainty in sequential regression problems. A second research work aims to facilitate the use of SMC techniques within deep learning architectures, by developing a new online smoothing algorithm with reduced computational cost, and applicable on a wider scope of state-space models, including deep generative models. Finally, a third research work proposes the first reinforcement learning that enables to learn conditional language models from scratch (i.e without supervised datasets), based on a truncation mechanism of the natural language action space with a pretrained language model
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Wang, Peng. "STOCHASTIC MODELING AND UNCERTAINTY EVALUATION FOR PERFORMANCE PROGNOSIS IN DYNAMICAL SYSTEMS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1499788641069811.

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Asgrimsson, David Steinar. "Quantifying uncertainty in structural condition with Bayesian deep learning : A study on the Z-24 bridge benchmark." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-251451.

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A machine learning approach to damage detection is presented for a bridge structural health monitoring system, validated on the renowned Z-24 bridge benchmark dataset where a sensor instrumented, threespan bridge was realistically damaged in stages. A Bayesian autoencoder neural network is trained to reconstruct raw sensor data sequences, with uncertainty bounds in prediction. The reconstruction error is then compared with a healthy-state error distribution and the sequence determined to come from a healthy state or not. Several realistic damage stages were successfully detected, making this a viable approach in a data-based monitoring system of an operational bridge. This is a fully operational, machine learning based bridge damage detection system, that is learned directly from raw sensor data.
En maskininlärningsmetod för strukturell skadedetektering av broar presenteras. Metoden valideras på det kända referensdataset Z-24, där en sensor-instrumenterad trespannsbro stegvist skadats. Ett Bayesianskt neuralt nätverk med autoenkoders tränas till att rekonstruera råa sensordatasekvenser, med osäkerhetsgränser i förutsägningen. Rekonstrueringsavvikelsen jämförs med avvikelsesfördelningen i oskadat tillstånd och sekvensen bedöms att komma från ett skadad eller icke skadat tillstånd. Flera realistiska stegvisa skadetillstånd upptäcktes, vilket gör metoden användbar i ett databaserat skadedetektionssystem för en bro i full storlek. Detta är ett lovande steg mot ett helt operativt databaserat skadedetektionssystem.
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Books on the topic "Deep learning with uncertainty"

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Marchau, Vincent A. W. J., Warren E. Walker, Pieter J. T. M. Bloemen, and Steven W. Popper, eds. Decision Making under Deep Uncertainty. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05252-2.

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Saefken, Benjamin, Alexander Silbersdorff, and Christoph Weisser, eds. Learning deep. Göttingen: Göttingen University Press, 2020. http://dx.doi.org/10.17875/gup2020-1338.

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Bishop, Christopher M., and Hugh Bishop. Deep Learning. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-45468-4.

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Kruse, René-Marcel, Benjamin Säfken, Alexander Silbersdorff, and Christoph Weisser, eds. Learning Deep Textwork. Göttingen: Göttingen University Press, 2021. http://dx.doi.org/10.17875/gup2021-1608.

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Rodriguez, Andres. Deep Learning Systems. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-01769-8.

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Fergus, Paul, and Carl Chalmers. Applied Deep Learning. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04420-5.

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Calin, Ovidiu. Deep Learning Architectures. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36721-3.

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El-Amir, Hisham, and Mahmoud Hamdy. Deep Learning Pipeline. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5349-6.

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Matsushita, Kayo, ed. Deep Active Learning. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5660-4.

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Michelucci, Umberto. Applied Deep Learning. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3790-8.

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Book chapters on the topic "Deep learning with uncertainty"

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Şen, Zekâi. "Uncertainty and Modeling Principles." In Shallow and Deep Learning Principles, 141–243. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29555-3_4.

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Wüthrich, Mario V., and Michael Merz. "Deep Learning." In Springer Actuarial, 267–379. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12409-9_7.

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AbstractThe core of this book are deep learning methods and neural networks. This chapter considers deep feed-forward neural (FN) networks. We introduce the generic architecture of deep FN networks, and we discuss universality theorems of FN networks. We present network fitting, back-propagation, embedding layers for categorical variables and insurance-specific issues such as the balance property in network fitting, as well as network ensembling to reduce model uncertainty. This chapter is complemented by many examples on non-life insurance pricing, but also on mortality modeling, as well as tools that help to explain deep FN network regression results.
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Plasencia Salgueiro, Armando, Lynnette González Rodríguez, and Ileana Suárez Blanco. "Managing Deep Learning Uncertainty for Unmanned Systems." In Deep Learning for Unmanned Systems, 175–223. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77939-9_6.

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Wüthrich, Mario V., and Michael Merz. "Selected Topics in Deep Learning." In Springer Actuarial, 453–535. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12409-9_11.

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AbstractThis chapter presents a selection of different topics. We discuss forecasting under model uncertainty, deep quantile regression, deep composite regression and the LocalGLMnet which is an interpretable FN network architecture. Moreover, we provide a bootstrap example to assess prediction uncertainty, we discuss mixture density networks, and we give an outlook to studying variational inference.
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González-Rodríguez, Lynnette, and Armando Plasencia-Salgueiro. "Uncertainty-Aware Autonomous Mobile Robot Navigation with Deep Reinforcement Learning." In Deep Learning for Unmanned Systems, 225–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77939-9_7.

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Ståhl, Niclas, Göran Falkman, Alexander Karlsson, and Gunnar Mathiason. "Evaluation of Uncertainty Quantification in Deep Learning." In Information Processing and Management of Uncertainty in Knowledge-Based Systems, 556–68. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50146-4_41.

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Grigorescu, Irina, Alena Uus, Daan Christiaens, Lucilio Cordero-Grande, Jana Hutter, Dafnis Batalle, A. David Edwards, Joseph V. Hajnal, Marc Modat, and Maria Deprez. "Uncertainty-Aware Deep Learning Based Deformable Registration." In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis, 54–63. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87735-4_6.

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Linsner, Florian, Linara Adilova, Sina Däubener, Michael Kamp, and Asja Fischer. "Approaches to Uncertainty Quantification in Federated Deep Learning." In Communications in Computer and Information Science, 128–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93736-2_12.

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Imam, Raza, and Mohammed Talha Alam. "Optimizing Brain Tumor Classification: A Comprehensive Study on Transfer Learning and Imbalance Handling in Deep Learning Models." In Epistemic Uncertainty in Artificial Intelligence, 74–88. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57963-9_6.

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Ghoshal, Biraja, Bhargab Ghoshal, and Allan Tucker. "Leveraging Uncertainty in Deep Learning for Pancreatic Adenocarcinoma Grading." In Medical Image Understanding and Analysis, 565–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12053-4_42.

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Conference papers on the topic "Deep learning with uncertainty"

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Kong, Lingkai, Harshavardhan Kamarthi, Peng Chen, B. Aditya Prakash, and Chao Zhang. "Uncertainty Quantification in Deep Learning." In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3580305.3599577.

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Darling, Michael, Justin Doak, Richard Field, and Mark Smith. "Optimizing Machine Learning Decisions with Prediction Uncertainty." In Proposed for presentation at the Machine Learning Deep Learning (MLDL) in ,. US DOE, 2021. http://dx.doi.org/10.2172/1888406.

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Kail, Roman, Kirill Fedyanin, Nikita Muravev, Alexey Zaytsev, and Maxim Panov. "ScaleFace: Uncertainty-aware Deep Metric Learning." In 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2023. http://dx.doi.org/10.1109/dsaa60987.2023.10302546.

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Sanchez, Téo, Baptiste Caramiaux, Pierre Thiel, and Wendy E. Mackay. "Deep Learning Uncertainty in Machine Teaching." In IUI '22: 27th International Conference on Intelligent User Interfaces. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3490099.3511117.

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Ahuja, Rishit Mohan, Maxime Alos, Alex McQuilkin, and Anudeep Venapally. "Quantifying Uncertainty using Bayesian Deep Learning and Deep Ensembles." In 2023 IEEE 3rd International Conference on Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET). IEEE, 2023. http://dx.doi.org/10.1109/temsmet56707.2023.10150105.

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ZHANG, YANG, YOU-WU WANG, and YI-QING NI. "HYBRID PROBABILISTIC DEEP LEARNING FOR DAMAGE IDENTIFICATION." In Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/37014.

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In structural health monitoring, various types of sensors collect a large amount of data for structural defect detection. These data provide critical support for the application of machine learning for structural damage identification. However, machine learning relies heavily on training data, whose quality and distribution can affect the effectiveness of detection models in real-world damage identification. In addition, machine learning contains a large number of parameters that are highly uncertain, which results in the output of machine learning models is not always as reliable. These deterministic deep networks usually make overconfident decisions in some data. The ability of deep learning to provide safe and reliable decisions is very important when applied in the field of engineering. In order to ensure the decision security of machine learning models, this paper proposes a hybrid probabilistic deep network for structural damage identification. The proposed method converts deterministic weights into a Gaussian distribution, which in turn quantifies the uncertainty in machine learning. Among them, variational inference is used for uncertainty modeling of probabilistic deep networks. These uncertainty metrics can be used to determine whether the output of the machine learning model is reliable. Nevertheless, the introduction of uncertainty weakens the learning ability of deep networks. Meanwhile, the number of parameters in the probabilistic layer is twice that of the deterministic layer for the same architecture. Therefore, probabilistic deep learning is more difficult to train compared to deterministic deep learning. To address these issues, deep learning with hybrid probabilistic and non-probabilistic layers needs to be investigated. This paper analyzed and discussed the effects of different numbers of probability layers on the effectiveness of structural damage identification. Finally, a series of experimental results showed that the proposed method is able to accurately identify structural damage while quantifying the decision uncertainty.
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Hu, Qian, and Huzefa Rangwala. "Reliable Deep Grade Prediction with Uncertainty Estimation." In LAK19: The 9th International Learning Analytics & Knowledge Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3303772.3303802.

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GLAUNER, PATRICK O. "DEEP LEARNING FOR SMILE RECOGNITION." In Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making (FLINS 2016). WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789813146976_0053.

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Rajput, Kishansingh, Malachi Schram, and Karthik Somayaji. "Uncertainty Aware Deep Learning for Particle Accelerators." In 36th Conference on Neural Information Processing, Hybrid/New Orleans, November 29, 2022. US DOE, 2022. http://dx.doi.org/10.2172/1998542.

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Pantoja, Maria, Drazen Fabris, and Robert Klienhenz. "Uncertainty in Deep Learning for Image Processing." In International Conference on Industrial Application Engineering 2023. The Institute of Industrial Applications Engineers, 2023. http://dx.doi.org/10.12792/iciae2023.013.

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Reports on the topic "Deep learning with uncertainty"

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Caldeira, Joao. Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1623354.

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Catanach, Thomas, and Jed Duersch. Efficient Generalizable Deep Learning. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1760400.

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Stracuzzi, David, Maximillian Chen, Michael Darling, Matthew Peterson, and Charlie Vollmer. Uncertainty Quantification for Machine Learning. Office of Scientific and Technical Information (OSTI), June 2017. http://dx.doi.org/10.2172/1733262.

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Thompson, A., K. Jagan, A. Sundar, R. Khatry, J. Donlevy, S. Thomas, and P. Harris. Uncertainty evaluation for machine learning. National Physical Laboratory, January 2022. http://dx.doi.org/10.47120/npl.ms34.

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Groh, Micah. NOvA Reconstruction using Deep Learning. Office of Scientific and Technical Information (OSTI), June 2018. http://dx.doi.org/10.2172/1462092.

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Geiss, Andrew, Joseph Hardin, Sam Silva, William Jr., Adam Varble, and Jiwen Fan. Deep Learning for Ensemble Forecasting. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769692.

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Harris, James, Shannon Kinkead, Dylan Fox, and Yang Ho. Continual Learning for Pattern Recognizers using Neurogenesis Deep Learning. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1855019.

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Draelos, Timothy John, Nadine E. Miner, Christopher C. Lamb, Craig Michael Vineyard, Kristofor David Carlson, Conrad D. James, and James Bradley Aimone. Neurogenesis Deep Learning: Extending deep networks to accommodate new classes. Office of Scientific and Technical Information (OSTI), December 2016. http://dx.doi.org/10.2172/1505351.

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Fan, Yiming. Nonlocal Operator Learning with Uncertainty Quantification. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1813660.

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Balaji, Praveen. Detecting Stellar Streams through Deep Learning. Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1637622.

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