Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 2649 for “"Deep-Learning"”.
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Evolutionary deep learning
… of algorithms in various active areas of machine learning research. Deep neural networks are exhibiting an explosion in the number of parameters that need to be trained, as well as the number of permutations of possible network architectures and hyper-parameters. There is little guidance on how to …
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Structure-aware Deep Learning
… systems designed to process this data, in their learning algorithms and the very nature of the tasks they solve. At the same time, machine learning methods are extremely data-hungry, requiring petabytes of data for training. Due to their complexity, graphs remain an under-utilized resource in …
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PATIENT CLASSIFICATION USING DEEP LEARNING
… deemed not possible a few years ago. Moreover, deep learning, one specific branch of artificial intelligence, has been used to produce useful results. It has been used in many new technologies such as self-driving cars, natural language processing, and many other automated systems. This research …
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On deep learning in physics
Machine learning, and most notably deep neural networks, have seen unprecedented success in recent years due to their ability to learn complex nonlinear mappings by ingesting large amounts of data through the process of training. This learning-by-example approach has slowly made its way into the …
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Deep learning of proteomics data
… highly complex data with conventional machine learning algorithms can be troublesome as these techniques require a considerable amount of feature engineering. Fortunately, a subfield of machine learning known as deep learning has recently, shown evidence towards overcoming these issues. Such …
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TOWARDS DATA-EFFICIENT DEEP LEARNING
This thesis advances data-efficient machine learning by tackling the limitations of current dataset distillation (DD) methods, which aim to compress large datasets into compact synthetic ones for faster training and enhanced privacy. First, it introduces Dataset Factorization, a novel framework …
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Efficient and Scalable Deep Learning
<p>Deep Neural Networks (DNNs) can achieve accuracy superior to traditional machine learning models, because of their large learning capacity and the availability of large amounts of labeled data. In general, larger DNNs can obtain higher accuracy. However, there are two obstacles which hinder us …
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Quantitative pathology using deep learning
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms
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Practical considerations for deep learning
The student, Thomas Paine, submitted this Dissertation for approval on 2017-04-17 at 10:31.
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Deep learning for grouped data
… explores the problems inherent in applying deep learning algorithms to groups of data. My claim is that groups should be represented as random variables whose values should be inferred from data. This approach has the potential to unlock solutions in many important domains of machine …
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Deep learning for supernovae detection
… the potential of using state-of-the-art machine learning algorithms to handle this burden more accurately and quickly than trained astronomers. To this end Deep Learning methods are applied to classify transients using real-world data from the Sloan Digital Sky Survey. Using cutting-edge training …
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Deep Learning on Geometry Representations
While deep learning has been successfully applied to many tasks in computer graphics and vision, standard learning architectures often operate on shape representations that are dense and regular, like pixel or voxel grids. On the other hand, decades of computer graphics and geometry processing …
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Computational imaging through deep learning
… objects being imaged. In recent years, machine learning architectures, and deep learning (DL) in particular, have attracted increasing attentions from CI researchers. Unlike traditional inverse algorithms in CI, DL approach learns both the forward operator and the objects' prior implicitly from …
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Scaling Laws for Deep Learning
… then get a car ... The renaissance of machine learning (ML) and deep learning (DL) over the last decade is accompanied by an unscalable computational cost, limiting its advancement and weighing on the field in practice. In this thesis we take a systematic approach to address the algorithmic and …
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Sculpting representations for deep learning
In machine learning, the choice of space in which to represent our data is of vital importance to their effective and efficient analysis. In this thesis, we develop approaches to address a number of problems in representation learning. We employ deep learning as means of sculpting our …
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Deep learning and structured data
In the recent years deep learning has witnessed successful applications in many different domains such as visual object recognition, detection and segmentation, automatic speech recognition, natural language processing, and reinforcement learning. In this thesis, we will investigate deep learning …
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Geometric Deep Learning for Biomolecules
Recent advancements in machine learning offer a promising pathway to deeper insights into biological phenomena. This manuscript explores the integration of geometric deep learning techniques to model biological structures. By embedding inductive biases based on geometry and physical laws, we aim to …
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Deep Learning for Taxonomy Prediction
The last decade has seen great advances in Next-Generation Sequencing technologies, and, as a result, there has been a rise in the number of genomes sequenced each year. In 2017, there were as many as 10,000 new organisms sequenced and added into the RefSeq Database. Taxonomy prediction is a …
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Vehicle Detection in Deep Learning
… of vehicle detection given the use of deep learning techniques, there are still concerns about the performance of state-of-the-art vehicle detection techniques. For example, state-of-the-art vehicle detectors are restricted by the large variation of scales. People working on vehicle …
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Deep Learning for Biological Problems
… input-output relationships, but they also seek a deep understanding of these models. In the last few years, deep models have achieved better performance in computational prediction tasks compared to other approaches. Deep models have been extensively used in processing natural data, such as …
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