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Dissertations / Theses on the topic 'Sparse deep neural networks'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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1

Tavanaei, Amirhossein. "Spiking Neural Networks and Sparse Deep Learning." Thesis, University of Louisiana at Lafayette, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10807940.

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<p> This document proposes new methods for training multi-layer and deep spiking neural networks (SNNs), specifically, spiking convolutional neural networks (CNNs). Training a multi-layer spiking network poses difficulties because the output spikes do not have derivatives and the commonly used backpropagation method for non-spiking networks is not easily applied. Our methods use novel versions of the brain-like, local learning rule named spike-timing-dependent plasticity (STDP) that incorporates supervised and unsupervised components. Our method starts with conventional learning methods and co

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Le, Quoc Tung. "Algorithmic and theoretical aspects of sparse deep neural networks." Electronic Thesis or Diss., Lyon, École normale supérieure, 2023. http://www.theses.fr/2023ENSL0105.

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Les réseaux de neurones profonds parcimonieux offrent une opportunité pratique convaincante pour réduire le coût de l'entraînement, de l'inférence et du stockage, qui augmente de manière exponentielle dans l'état de l'art de l'apprentissage profond. Dans cette présentation, nous introduirons une approche pour étudier les réseaux de neurones profonds parcimonieux à travers le prisme d'un autre problème : la factorisation de matrices sous constraints de parcimonie, c'est-à-dire le problème d'approximation d'une matrice (dense) par le produit de facteurs (multiples) parcimonieux. En particulier,

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3

Hoori, Ammar O. "MULTI-COLUMN NEURAL NETWORKS AND SPARSE CODING NOVEL TECHNIQUES IN MACHINE LEARNING." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5743.

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Accurate and fast machine learning (ML) algorithms are highly vital in artificial intelligence (AI) applications. In complex dataset problems, traditional ML methods such as radial basis function neural network (RBFN), sparse coding (SC) using dictionary learning, and particle swarm optimization (PSO) provide trivial results, large structure, slow training, and/or slow testing. This dissertation introduces four novel ML techniques: the multi-column RBFN network (MCRN), the projected dictionary learning algorithm (PDL) and the multi-column adaptive and non-adaptive particle swarm optimization t

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4

Vekhande, Swapnil Sudhir. "Deep Learning Neural Network-based Sinogram Interpolation for Sparse-View CT Reconstruction." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/90182.

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Computed Tomography (CT) finds applications across domains like medical diagnosis, security screening, and scientific research. In medical imaging, CT allows physicians to diagnose injuries and disease more quickly and accurately than other imaging techniques. However, CT is one of the most significant contributors of radiation dose to the general population and the required radiation dose for scanning could lead to cancer. On the other hand, a shallow radiation dose could sacrifice image quality causing misdiagnosis. To reduce the radiation dose, sparse-view CT, which includes capturing a sma

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Carvalho, Micael. "Deep representation spaces." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS292.

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Ces dernières années, les techniques d’apprentissage profond ont fondamentalement transformé l'état de l'art de nombreuses applications de l'apprentissage automatique, devenant la nouvelle approche standard pour plusieurs d’entre elles. Les architectures provenant de ces techniques ont été utilisées pour l'apprentissage par transfert, ce qui a élargi la puissance des modèles profonds à des tâches qui ne disposaient pas de suffisamment de données pour les entraîner à partir de zéro. Le sujet d'étude de cette thèse couvre les espaces de représentation créés par les architectures profondes. Dans

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Gonon, Antoine. "Harnessing symmetries for modern deep learning challenges : a path-lifting perspective." Electronic Thesis or Diss., Lyon, École normale supérieure, 2024. http://www.theses.fr/2024ENSL0043.

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Les réseaux de neurones connaissent un grand succès pratique, mais les outils théoriques pour les analyser sont encore souvent limités à des situations simples qui ne reflètent pas toute la complexité des cas pratiques d'intérêts. Cette thèse vise à réduire cet écart en rendant les outils théoriques plus concrets. Le premier axe de recherche concerne la généralisation : un réseau donné pourra-t-il bien se comporter sur des données jamais vues auparavant ? Ce travail améliore les garanties de généralisation basées sur la norme de chemins, les rendant applicables à des réseaux ReLU incluant du p

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7

Pawlowski, Filip igor. "High-performance dense tensor and sparse matrix kernels for machine learning." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEN081.

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Dans cette thèse, nous développons des algorithmes à haute performance pour certains calculs impliquant des tenseurs denses et des matrices éparses. Nous abordons les opérations du noyau qui sont utiles pour les tâches d'apprentissage de la machine, telles que l'inférence avec les réseaux neuronaux profonds. Nous développons des structures de données et des techniques pour réduire l'utilisation de la mémoire, pour améliorer la localisation des données et donc pour améliorer la réutilisation du cache des opérations du noyau. Nous concevons des algorithmes parallèles à mémoire séquentielle et à

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8

Thom, Markus [Verfasser]. "Sparse neural networks / Markus Thom." Ulm : Universität Ulm. Fakultät für Ingenieurwissenschaften und Informatik, 2015. http://d-nb.info/1067496319/34.

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9

Pradels, Léo. "Efficient CNN inference acceleration on FPGAs : a pattern pruning-driven approach." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS087.

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Les modèles d'apprentissage profond basés sur les CNNs offrent des performances de pointe dans les tâches de traitement d'images et de vidéos, en particulier pour l'amélioration ou la classification d'images. Cependant, ces modèles sont lourds en calcul et en empreinte mémoire, ce qui les rend inadaptés aux contraintes de temps réel sur des FPGA embarqués. Il est donc essentiel de compresser ces CNNs et de concevoir des architectures d'accélérateurs pour l'inférence qui intègrent la compression dans une approche de co-conception matérielle et logicielle. Bien que des optimisations logicielles

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10

Liu, Qian. "Deep spiking neural networks." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/deep-spiking-neural-networks(336e6a37-2a0b-41ff-9ffb-cca897220d6c).html.

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Neuromorphic Engineering (NE) has led to the development of biologically-inspired computer architectures whose long-term goal is to approach the performance of the human brain in terms of energy efficiency and cognitive capabilities. Although there are a number of neuromorphic platforms available for large-scale Spiking Neural Network (SNN) simulations, the problem of programming these brain-like machines to be competent in cognitive applications still remains unsolved. On the other hand, Deep Learning has emerged in Artificial Neural Network (ANN) research to dominate state-of-the-art solutio

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11

Zheng, Léon. "Frugalité en données et efficacité computationnelle dans l'apprentissage profond." Electronic Thesis or Diss., Lyon, École normale supérieure, 2024. http://www.theses.fr/2024ENSL0009.

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Cette thèse s’intéresse à deux enjeux de frugalité et d’efficacité dans l’apprentissage profond moderne : frugalité en données et efficacité en ressources de calcul. Premièrement, nous étudions l’apprentissage auto-supervisé, une approche prometteuse en vision par ordinateur qui ne nécessite pas d’annotations des données pour l'apprentissage de représentations. En particulier, nous proposons d’unifier plusieurs fonctions objectives auto-supervisées dans un cadre de noyaux invariants par rotation, ce qui ouvre des perspectives en termes de réduction de coût de calcul de ces fonctions objectives

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12

Squadrani, Lorenzo. "Deep neural networks and thermodynamics." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Deep learning is the most effective and used approach to artificial intelligence, and yet it is far from being properly understood. The understanding of it is the way to go to further improve its effectiveness and in the best case to gain some understanding of the "natural" intelligence. We attempt a step in this direction with the aim of physics. We describe a convolutional neural network for image classification (trained on CIFAR-10) within the descriptive framework of Thermodynamics. In particular we define and study the temperature of each component of the network. Our results provides a n

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13

Mancevo, del Castillo Ayala Diego. "Compressing Deep Convolutional Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217316.

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Deep Convolutional Neural Networks and "deep learning" in general stand at the cutting edge on a range of applications, from image based recognition and classification to natural language processing, speech and speaker recognition and reinforcement learning. Very deep models however are often large, complex and computationally expensive to train and evaluate. Deep learning models are thus seldom deployed natively in environments where computational resources are scarce or expensive. To address this problem we turn our attention towards a range of techniques that we collectively refer to as "mo

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14

Abbasi, Mahdieh. "Toward robust deep neural networks." Doctoral thesis, Université Laval, 2020. http://hdl.handle.net/20.500.11794/67766.

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Dans cette thèse, notre objectif est de développer des modèles d’apprentissage robustes et fiables mais précis, en particulier les Convolutional Neural Network (CNN), en présence des exemples anomalies, comme des exemples adversaires et d’échantillons hors distribution –Out-of-Distribution (OOD). Comme la première contribution, nous proposons d’estimer la confiance calibrée pour les exemples adversaires en encourageant la diversité dans un ensemble des CNNs. À cette fin, nous concevons un ensemble de spécialistes diversifiés avec un mécanisme de vote simple et efficace en termes de calcul pour

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15

Shao, Yuanlong. "Learning Sparse Recurrent Neural Networks in Language Modeling." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398942373.

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16

Lu, Yifei. "Deep neural networks and fraud detection." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-331833.

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17

Kalogiras, Vasileios. "Sentiment Classification with Deep Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217858.

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Attitydanalys är ett delfält av språkteknologi (NLP) som försöker analysera känslan av skriven text. Detta är ett komplext problem som medför många utmaningar. Av denna anledning har det studerats i stor utsträckning. Under de senaste åren har traditionella maskininlärningsalgoritmer eller handgjord metodik använts och givit utmärkta resultat. Men den senaste renässansen för djupinlärning har växlat om intresse till end to end deep learning-modeller.Å ena sidan resulterar detta i mer kraftfulla modeller men å andra sidansaknas klart matematiskt resonemang eller intuition för dessa modeller. På

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Choi, Keunwoo. "Deep neural networks for music tagging." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/46029.

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In this thesis, I present my hypothesis, experiment results, and discussion that are related to various aspects of deep neural networks for music tagging. Music tagging is a task to automatically predict the suitable semantic label when music is provided. Generally speaking, the input of music tagging systems can be any entity that constitutes music, e.g., audio content, lyrics, or metadata, but only the audio content is considered in this thesis. My hypothesis is that we can fi nd effective deep learning practices for the task of music tagging task that improves the classi fication performanc

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19

Yin, Yonghua. "Random neural networks for deep learning." Thesis, Imperial College London, 2018. http://hdl.handle.net/10044/1/64917.

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The random neural network (RNN) is a mathematical model for an 'integrate and fire' spiking network that closely resembles the stochastic behaviour of neurons in mammalian brains. Since its proposal in 1989, there have been numerous investigations into the RNN's applications and learning algorithms. Deep learning (DL) has achieved great success in machine learning, but there has been no research into the properties of the RNN for DL to combine their power. This thesis intends to bridge the gap between RNNs and DL, in order to provide powerful DL tools that are faster, and that can potentially

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Zagoruyko, Sergey. "Weight parameterizations in deep neural networks." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1129/document.

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Les réseaux de neurones multicouches ont été proposés pour la première fois il y a plus de trois décennies, et diverses architectures et paramétrages ont été explorés depuis. Récemment, les unités de traitement graphique ont permis une formation très efficace sur les réseaux neuronaux et ont permis de former des réseaux beaucoup plus grands sur des ensembles de données plus importants, ce qui a considérablement amélioré le rendement dans diverses tâches d'apprentissage supervisé. Cependant, la généralisation est encore loin du niveau humain, et il est difficile de comprendre sur quoi sont basé

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Ioannou, Yani Andrew. "Structural priors in deep neural networks." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/278976.

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Deep learning has in recent years come to dominate the previously separate fields of research in machine learning, computer vision, natural language understanding and speech recognition. Despite breakthroughs in training deep networks, there remains a lack of understanding of both the optimization and structure of deep networks. The approach advocated by many researchers in the field has been to train monolithic networks with excess complexity, and strong regularization --- an approach that leaves much to desire in efficiency. Instead we propose that carefully designing networks in considerati

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Billman, Linnar, and Johan Hullberg. "Speech Reading with Deep Neural Networks." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-360022.

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Recent growth in computational power and available data has increased popularityand progress of machine learning techniques. Methods of machine learning areused for automatic speech recognition in order to allow humans to transferinformation to computers simply by speech. In the present work, we are interestedin doing this for general contexts as e.g. speakers talking on TV or newsreadersrecorded in a studio. Automatic speech recognition systems are often solely basedon acoustic data. By introducing visual data such as lip movements, robustness ofsuch system can be increased.This thesis instea

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23

Wang, Shenhao. "Deep neural networks for choice analysis." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129894.

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Thesis: Ph. D. in Computer and Urban Science, Massachusetts Institute of Technology, Department of Urban Studies and Planning, September, 2020<br>Cataloged from student-submitted PDF of thesis.<br>Includes bibliographical references (pages 117-128).<br>As deep neural networks (DNNs) outperform classical discrete choice models (DCMs) in many empirical studies, one pressing question is how to reconcile them in the context of choice analysis. So far researchers mainly compare their prediction accuracy, treating them as completely different modeling methods. However, DNNs and classical choice mode

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Sunnegårdh, Christina. "Scar detection using deep neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299576.

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Object detection is a computer vision method that deals with the tasks of localizing and classifying objects within an image. The number of usages for the method is constantly growing, and this thesis investigates the unexplored area of using deep neural networks for scar detection. Furthermore, the thesis investigates using the scar detector as a basis for the binary classification task of deciding whether in-the-wild images contains a scar or not. Two pre-trained object detection models, Faster R-CNN and RetinaNet, were trained on 1830 manually labeled images using different hyperparameters.

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Landeen, Trevor J. "Association Learning Via Deep Neural Networks." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7028.

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Deep learning has been making headlines in recent years and is often portrayed as an emerging technology on a meteoric rise towards fully sentient artificial intelligence. In reality, deep learning is the most recent renaissance of a 70 year old technology and is far from possessing true intelligence. The renewed interest is motivated by recent successes in challenging problems, the accessibility made possible by hardware developments, and dataset availability. The predecessor to deep learning, commonly known as the artificial neural network, is a computational network setup to mimic the biolo

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Srivastava, Sanjana. "On foveation of deep neural networks." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123134.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 61-63).<br>The human ability to recognize objects is impaired when the object is not shown in full. "Minimal images" are the smallest regions of an image that remain recognizable for humans. [26] show that a slight modificatio

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Grechka, Asya. "Image editing with deep neural networks." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS683.pdf.

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L'édition d'images a une histoire riche remontant à plus de deux siècles. Cependant, l'édition "classique" des images requiert une grande maîtrise artistique et nécessitent un temps considérable, souvent plusieurs heures, pour modifier chaque image. Ces dernières années, d'importants progrès dans la modélisation générative ont permis la synthèse d'images réalistes et de haute qualité. Toutefois, l'édition d'une image réelle est un vrai défi nécessitant de synthétiser de nouvelles caractéristiques tout en préservant fidèlement une partie de l'image d'origine. Dans cette thèse, nous explorons di

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Andersson, Viktor. "Semantic Segmentation : Using Convolutional Neural Networks and Sparse dictionaries." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139367.

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The two main bottlenecks using deep neural networks are data dependency and training time. This thesis proposes a novel method for weight initialization of the convolutional layers in a convolutional neural network. This thesis introduces the usage of sparse dictionaries. A sparse dictionary optimized on domain specific data can be seen as a set of intelligent feature extracting filters. This thesis investigates the effect of using such filters as kernels in the convolutional layers in the neural network. How do they affect the training time and final performance? The dataset used here is the

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Chen, Zhe. "Augmented Context Modelling Neural Networks." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20654.

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Contexts provide beneficial information for machine-based image understanding tasks. However, existing context modelling methods still cannot fully exploit contexts, especially for object recognition and detection. In this thesis, we develop augmented context modelling neural networks to better utilize contexts for different object recognition and detection tasks. Our contributions are two-fold: 1) we introduce neural networks to better model instance-level visual relationships; 2) we introduce neural network-based algorithms to better utilize contexts from 3D information and synthesized data

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Habibi, Aghdam Hamed. "Understanding Road Scenes using Deep Neural Networks." Doctoral thesis, Universitat Rovira i Virgili, 2018. http://hdl.handle.net/10803/461607.

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La comprensió de les escenes de la carretera és fonamental per als automòbils autònoms. Això requereix segmentar escenes de carreteres en regions semànticament significatives i reconèixer objectes en una escena. Tot i que objectes com ara cotxes i vianants han de segmentar-se amb precisió, és possible que no sigui necessari detectar i localitzar aquests objectes en una escena. Tanmateix, detectar i classificar objectes com ara els senyals de trànsit és fonamental per ajustar-se a les regles del camí. En aquesta tesi, primer proposem un mètode per classificar senyals de trànsit amb atributs vis

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Antoniades, Andreas. "Interpreting biomedical data via deep neural networks." Thesis, University of Surrey, 2018. http://epubs.surrey.ac.uk/845765/.

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Machine learning technology has taken quantum leaps in the past few years. From the rise of voice recognition as an interface to interact with our computers, to self-organising photo albums and self-driving cars. Neural networks and deep learning contributed significantly to drive this revolution. Yet, biomedicine is one of the research areas that has yet to fully embrace the possibilities of deep learning. Engaged in a cross-disciplinary subject, researchers, and clinical experts are focused on machine learning and statistical signal processing techniques. The ability to learn hierarchical fe

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Avramova, Vanya. "Curriculum Learning with Deep Convolutional Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-178453.

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Curriculum learning is a machine learning technique inspired by the way humans acquire knowledge and skills: by mastering simple concepts first, and progressing through information with increasing difficulty to grasp more complex topics. Curriculum Learning, and its derivatives Self Paced Learning (SPL) and Self Paced Learning with Diversity (SPLD), have been previously applied within various machine learning contexts: Support Vector Machines (SVMs), perceptrons, and multi-layer neural networks, where they have been shown to improve both training speed and model accuracy. This project ventured

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Karlsson, Daniel. "Classifying sport videos with deep neural networks." Thesis, Umeå universitet, Institutionen för datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-130654.

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This project aims to apply deep neural networks to classify video clips in applications used to streamline advertisements on the web. The system focuses on sport clips but can be expanded into other advertisement fields with lower accuracy and longer training times as a consequence. The main task was to find the neural network model best suited for classifying videos. To achieve this the field was researched and three network models were introduced to see how they could handle the videos. It was proposed that applying a recurrent LSTM structure at the end of an image classification network cou

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Peng, Zeng. "Pedestrian Tracking by using Deep Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302107.

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This project aims at using deep learning to solve the pedestrian tracking problem for Autonomous driving usage. The research area is in the domain of computer vision and deep learning. Multi-Object Tracking (MOT) aims at tracking multiple targets simultaneously in a video data. The main application scenarios of MOT are security monitoring and autonomous driving. In these scenarios, we often need to track many targets at the same time which is not possible with only object detection or single object tracking algorithms for their lack of stability and usability. Therefore we need to explore the

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Milner, Rosanna Margaret. "Using deep neural networks for speaker diarisation." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/16567/.

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Speaker diarisation answers the question “who spoke when?” in an audio recording. The input may vary, but a system is required to output speaker labelled segments in time. Typical stages are Speech Activity Detection (SAD), speaker segmentation and speaker clustering. Early research focussed on Conversational Telephone Speech (CTS) and Broadcast News (BN) domains before the direction shifted to meetings and, more recently, broadcast media. The British Broadcasting Corporation (BBC) supplied data through the Multi-Genre Broadcast (MGB) Challenge in 2015 which showed the difficulties speaker dia

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Karlsson, Jonas. "Auditory Classification of Carsby Deep Neural Networks." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-355673.

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This thesis explores the challenge of using deep neural networks to classify traits incars through sound recognition. These traits could include type of engine, model, or manufacturer of the car. The problem was approached by creating three different neural networks and evaluating their performance in classifying sounds of three different cars. The top scoring neural network achieved an accuracy of 61 percent, which is far from reaching the standard accuracy of modern speech recognition systems. The results do, however, show that there are some tendencies to the data that neural networks can l

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Wang, Yuxuan. "Supervised Speech Separation Using Deep Neural Networks." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1426366690.

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Wu, Chunyang. "Structured deep neural networks for speech recognition." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/276084.

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Deep neural networks (DNNs) and deep learning approaches yield state-of-the-art performance in a range of machine learning tasks, including automatic speech recognition. The multi-layer transformations and activation functions in DNNs, or related network variations, allow complex and difficult data to be well modelled. However, the highly distributed representations associated with these models make it hard to interpret the parameters. The whole neural network is commonly treated a ``black box''. The behaviours of activation functions and the meanings of network parameters are rarely controlle

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Zhang, Jeffrey M. Eng Massachusetts Institute of Technology. "Enhancing adversarial robustness of deep neural networks." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122994.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 57-58).<br>Logit-based regularization and pretrain-then-tune are two approaches that have recently been shown to enhance adversarial robustness of machine learning models. In the realm of regularization, Zhang et al. (2019) pr

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Miglani, Vivek N. "Comparing learned representations of deep neural networks." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123048.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 63-64).<br>In recent years, a variety of deep neural network architectures have obtained substantial accuracy improvements in tasks such as image classification, speech recognition, and machine translation, yet little is known

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Bayer, Ali Orkan. "Semantic Language models with deep neural Networks." Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/367784.

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Spoken language systems (SLS) communicate with users in natural language through speech. There are two main problems related to processing the spoken input in SLS. The first one is automatic speech recognition (ASR) which recognizes what the user says. The second one is spoken language understanding (SLU) which understands what the user means. We focus on the language model (LM) component of SLS. LMs constrain the search space that is used in the search for the best hypothesis. Therefore, they play a crucial role in the performance of SLS. It has long been discussed that an improvement in the

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Bayer, Ali Orkan. "Semantic Language models with deep neural Networks." Doctoral thesis, University of Trento, 2015. http://eprints-phd.biblio.unitn.it/1578/1/bayer_thesis.pdf.

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Spoken language systems (SLS) communicate with users in natural language through speech. There are two main problems related to processing the spoken input in SLS. The first one is automatic speech recognition (ASR) which recognizes what the user says. The second one is spoken language understanding (SLU) which understands what the user means. We focus on the language model (LM) component of SLS. LMs constrain the search space that is used in the search for the best hypothesis. Therefore, they play a crucial role in the performance of SLS. It has long been discussed that an improvement in the

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Elezi, Ismail <1991&gt. "Exploiting contextual information with deep neural networks." Doctoral thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/18453.

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Context matters! Nevertheless, there has not been much research in exploiting contextual information in deep neural networks. For the most part, the entire usage of contextual information has been limited to recurrent neural networks. Attention models and capsule networks are two recent ways of introducing contextual information in non-recurrent models, however both of these algorithms have been developed after this work has started. In this thesis, we show that contextual information can be exploited in $2$ fundamentally different ways: implicitly and explicitly. In DeepScores project, where

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RAGONESI, RUGGERO. "Addressing Dataset Bias in Deep Neural Networks." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1069001.

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Deep Learning has achieved tremendous success in recent years in several areas such as image classification, text translation, autonomous agents, to name a few. Deep Neural Networks are able to learn non-linear features in a data-driven fashion from complex, large scale datasets to solve tasks. However, some fundamental issues remain to be fixed: the kind of data that is provided to the neural network directly influences its capability to generalize. This is especially true when training and test data come from different distributions (the so called domain gap or domain shift problem): in thi

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Jeanneret, Sanmiguel Guillaume. "Towards explainable and interpretable deep neural networks." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMC229.

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Les architectures neuronales profondes ont démontré des résultats remarquables dans diverses tâches de vision par ordinateur. Cependant, leur performance extraordinaire se fait au détriment de l'interprétabilité. En conséquence, le domaine de l'IA explicable a émergé pour comprendre réellement ce que ces modèles apprennent et pour découvrir leurs sources d'erreur. Cette thèse explore les algorithmes explicables afin de révéler les biais et les variables utilisés par ces modèles de boîte noire dans le contexte de la classification d'images. Par conséquent, nous divisons cette thèse en quatre pa

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Zheng, Xuebin. "Wavelet-based Graph Neural Networks." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/27989.

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This thesis focuses on spectral-based graph neural networks (GNNs). In Chapter 2, we use multiresolution Haar-like wavelets to design a framework of GNNs which equips with graph convolution and pooling strategies. The resulting model is called MathNet whose wavelet transform matrix is constructed with a coarse-grained chain. So our proposed MathNet not only enjoys the multiresolution analysis from the Haar-like wavelets but also leverages the clustering information of the graph data. Furthermore, we develop a novel multiscale representation system for graph data, called decimated framelets, w

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Bonfiglioli, Luca. "Identificazione efficiente di reti neurali sparse basata sulla Lottery Ticket Hypothesis." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Frankle e Carbin 2018, data una rete densa inizializzata casualmente, mostrano che esistono sottoreti sparse di tale rete che possono ottenere accuratezze superiori alla rete densa e richiedono meno iterazioni di addestramento per raggiungere l’early stop. Tali sottoreti sono indicate con il nome di winning ticket. L’identificazione di questi ultimi richiede tuttavia almeno un addestramento completo del modello denso, il che ne limita l’impiego pratico, se non come tecnica di compressione. In questa tesi, si mira a trovare una variante più efficiente dei metodi di magnitude based pruning propo

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Pons, Puig Jordi. "Deep neural networks for music and audio tagging." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/668036.

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Automatic music and audio tagging can help increase the retrieval and re-use possibilities of many audio databases that remain poorly labeled. In this dissertation, we tackle the task of music and audio tagging from the deep learning perspective and, within that context, we address the following research questions: (i) Which deep learning architectures are most appropriate for (music) audio signals? (ii) In which scenarios is waveform-based end-to-end learning feasible? (iii) How much data is required for carrying out competitive deep learning research? In pursuit of answering research q

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Purmonen, Sami. "Predicting Game Level Difficulty Using Deep Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217140.

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We explored the usage of Monte Carlo tree search (MCTS) and deep learning in order to predict game level difficulty in Candy Crush Saga (Candy) measured as number of attempts per success. A deep neural network (DNN) was trained to predict moves from game states from large amounts of game play data. The DNN played a diverse set of levels in Candy and a regression model was fitted to predict human difficulty from bot difficulty. We compared our results to an MCTS bot. Our results show that the DNN can make estimations of game level difficulty comparable to MCTS in substantially shorter time.<br>

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Winsnes, Casper. "Automatic Subcellular Protein Localization Using Deep Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189991.

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Protein localization is an important part in understanding the functionality of a protein. The current method of localizing proteins is to manually annotate microscopy images. This thesis investigates the feasibility of using deep artificial neural networks to automatically classify subcellular protein locations based on immunoflourescent images. We investigate the applicability in both single-label and multi-label classification, as well as cross cell line classification. We show that deep single-label neural networks can be used for protein localization with up to 73% accuracy. We also show

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