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Дисертації з теми "Interpretable deep learning"

Автор: Grafiati

Опубліковано: 11 березня 2023

Оновлено: 26 липня 2025

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1

FERRONE, LORENZO. "On interpretable information in deep learning: encoding and decoding of distributed structures." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2016. http://hdl.handle.net/2108/202245.

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2

Xie, Ning. "Towards Interpretable and Reliable Deep Neural Networks for Visual Intelligence." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1596208422672732.

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3

Emschwiller, Matt V. "Understanding neural network sample complexity and interpretable convergence-guaranteed deep learning with polynomial regression." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127290.

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Анотація:

Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, May, 2020<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 83-89).<br>We first study the sample complexity of one-layer neural networks, namely the number of examples that are needed in the training set for such models to be able to learn meaningful information out-of-sample. We empirically derive quantitative relationships between the sample complexity and the parameters of the network, such as its input dimension and its width. Then, we introduce

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4

Terzi, Matteo. "Learning interpretable representations for classification, anomaly detection, human gesture and action recognition." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3423183.

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The goal of this thesis is to provide algorithms and models for classification, gesture recognition and anomaly detection with a partial focus on human activity. In applications where humans are involved, it is of paramount importance to provide robust and understandable algorithms and models. A way to accomplish this requirement is to use relatively simple and robust approaches, especially when devices are resource-constrained. The second approach, when a large amount of data is present, is to adopt complex algorithms and models and make them robust and interpretable from a human-like point

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5

REPETTO, MARCO. "Black-box supervised learning and empirical assessment: new perspectives in credit risk modeling." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/402366.

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I recenti algoritmi di apprendimento automatico ad alte prestazioni sono convincenti ma opachi, quindi spesso è difficile capire come arrivano alle loro previsioni, dando origine a problemi di interpretabilità. Questi problemi sono particolarmente rilevanti nell'apprendimento supervisionato, dove questi modelli "black-box" non sono facilmente comprensibili per le parti interessate. Un numero crescente di lavori si concentra sul rendere più interpretabili i modelli di apprendimento automatico, in particolare quelli di apprendimento profondo. Gli approcci attualmente proposti si basano su un'i

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6

Thibeau-Sutre, Elina. "Reproducible and interpretable deep learning for the diagnosis, prognosis and subtyping of Alzheimer’s disease from neuroimaging data." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS495.

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L’objectif de cette thèse était la validation de l’existence ainsi que la découverte de nouveaux sous-types au sein de la maladie d’Alzheimer, première cause de démence au monde. Afin d’explorer son hétérogénéité, nous avons employé des méthodes d’apprentissage profond appliquées à une modalité de neuroimagerie, l’imagerie par résonance magnétique structurelle.Cependant, la découverte de biais méthodologiques importants dans de nombreuses études de notre domaine, ainsi que l’absence de consensus de la communauté sur la manière d’interpréter les résultats des méthodes d’apprentissage profond a

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7

Parekh, Jayneel. "A Flexible Framework for Interpretable Machine Learning : application to image and audio classification." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT032.

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Les systèmes d'apprentissage automatique, et en particulier les réseaux de neurones, ont rapidement développé leur capacité à résoudre des problèmes d'apprentissage complexes. Par conséquent, ils sont intégrés dans la société avec une influence de plus en plus grande sur tous les niveaux de l'expérience humaine. Cela a entraîné la nécessité d'acquérir des informations compréhensibles par l'homme dans leur processus de prise de décision pour s'assurer que les décisions soient prises de manière éthique et fiable. L'étude et le développement de méthodes capables de générer de telles informations

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8

Bennetot, Adrien. "A Neural-Symbolic learning framework to produce interpretable predictions for image classification." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS418.

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L'intelligence artificielle s'est développée de manière exponentielle au cours de la dernière décennie. Son évolution est principalement liée aux progrès des processeurs des cartes graphiques des ordinateurs, permettant d'accélérer le calcul des algorithmes d'apprentissage, et à l'accès à des volumes massifs de données. Ces progrès ont été principalement motivés par la recherche de modèles de prédiction de qualité, rendant ces derniers extrêmement précis mais opaques. Leur adoption à grande échelle est entravée par leur manque de transparence, ce qui provoque l'émergence de l'intelligence arti

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9

Sheikhalishahi, Seyedmostafa. "Machine learning applications in Intensive Care Unit." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/339274.

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The rapid digitalization of the healthcare domain in recent years highlighted the need for advanced predictive methods particularly based upon deep learning methods. Deep learning methods which are capable of dealing with time- series data have recently emerged in various fields such as natural language processing, machine translation, and the Intensive Care Unit (ICU). The recent applications of deep learning in ICU have increasingly received attention, and it has shown promising results for different clinical tasks; however, there is still a need for the benchmark models as far as a handful

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10

Loiseau, Romain. "Real-World 3D Data Analysis : Toward Efficiency and Interpretability." Electronic Thesis or Diss., Marne-la-vallée, ENPC, 2023. http://www.theses.fr/2023ENPC0028.

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Cette thèse explore de nouvelles approches d'apprentissage profond pour l'analyse des données 3D du monde réel. Le traitement des données 3D est utile pour de nombreuses applications telles que la conduite autonome, la gestion du territoire, la surveillance des installations industrielles, l'inventaire forestier et la mesure de biomasse. Cependant, l'annotation et l'analyse des données 3D peuvent être exigeantes. En particulier, il est souvent difficile de respecter des contraintes liées à l'utilisation des ressources de calcul ou à l'efficacité de l'annotation. La difficulté d'interpréter et

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11

jui, mao wen, and 毛文瑞. "Towards Interpretable Deep Extreme Multi-label Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/t7hq7r.

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碩士<br>國立中山大學<br>資訊管理學系研究所<br>107<br>Extreme multi-label learning is to seek most relevant subset of labels from an extreme large labels space. The problem of scalability and sparsity makes extreme multi-label hard to learn. In this paper, we propose a framework to deal with these problems. Our approach allows to deal with enormous dataset efficiently. Moreover, most algorithms nowadays are criticized for “black box” problem, which model cannot provide how it decides to make predictions. Through special non-negative constraint, our proposed approach is able to provide interpretable explanation.

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12

Kuo, Bo-Wen, and 郭博文. "Interpretable representation learning based on Deep Rule Forests." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/7wqrk4.

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碩士<br>國立中山大學<br>資訊管理學系研究所<br>106<br>The spirit of tree-based methods is to learn rules. A large number of machine learning techniques are tree-based. More complicated tree learners may result in higher predictive models, but may sacrifice for model interpretability. On the other hand, the spirit of representation learning is to extract abstractive concepts from manifestations of the data. For instance, Deep Neural networks (DNNs) is the most popular method in representation learning. However, unaccountable feature representation is the shortcoming of DNNs. In this paper, we proposed an approac

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13

Würfel, Max. "Online advertising revenue forecasting: an interpretable deep learning approach." Master's thesis, 2021. http://hdl.handle.net/10362/122676.

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This paper investigates whether publishers’ Google AdSense online advertising revenues can be predicted from peekd’s proprietary database using deep learning methodologies. Peekd is a Berlin (Germany) based data science company, which primarily provides e Retailers with sales and shopper intelligence. I find that using a single deep learning model, AdSense revenues can be predicted across publishers. Additionally, using unsupervised clustering, publishers were grouped and related time series were fed as covariates when making predictions. No performance improvement was found in relation with

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14

Huang, Sheng-Tai, and 黃升泰. "Interpretable Logic Representation Learning based on Deep Rule Forest." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/hybs2q.

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碩士<br>國立中山大學<br>資訊管理學系研究所<br>107<br>Compared to traditional machine learning algorithms, most contemporary algorithms have prominent promotion in terms of accuracy, but this also complicate the model architecture, which disables human from understanding how the predictions are generated. This makes the latent discrimination in data difficult for human to discover, and thus there are legislations enforce that models should have interpretability. However, recent interpretable models (e.g. decision tree, linear model) are too simple to produce enough accurate predictions in case of dealing large

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15

Agrawal, Purvi. "Neural Representation Learning for Speech and Audio Signals." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4824.

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Representation learning is the branch of machine learning consisting of techniques that are capable of automatically discovering meaningful representations from raw data for efficient information extraction. In recent years, following the trends in other streams of machine learning, representation learning using neural networks has attracted significant interest. For example, deep representation learning in the text domain using word embeddings has shown interesting semantic properties that make them widely useful for many natural language processing applications. In the speech processing fiel

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