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.
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Showing 1 to 20 of 366 for “"Deep learning models"”.
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Towards Deep Learning Models of Metabolism
… second contribution is CLIPZyme, a contrastive learning method for virtual enzyme screening that frames the task of identifying enzymes catalyzing a reaction of interest as a retrieval problem. CLIPZyme outperforms the baseline approach of screening enzymes via their enzyme commission (EC) …
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Deep learning models for high-frequency financial data
… price of the financial instrument. We develop deep learning models to capture the high dimensional data distributions (on R^d) of the limit order data. These models exploit the underlying structure of this complex data. We develop a uniform data grid model for limit order book data to achieve …
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Connecting Deep Learning Models to the Human Brain
… innovative methodologies for connecting new deep learning models, particularly models that integrate vision and language with human brain processing. These models have shown remarkable advancements in tasks such as object recognition, scene classification, and language processing, achieving …
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Deep Learning Models for Context-Aware Object Detection
… context cues into a detection pipeline. Current deep learning methods for object detection exploit state-of-the-art image recognition networks for classifying the given region-of-interest (ROI) to predefined classes and regressing a bounding-box around it without using any information about the …
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Measuring Short Text Semantic Similarity with Deep Learning Models
… enunciated voice commands. We study the use of deep learning models, the state-of-the-art artificial intelligence (AI) method, for the problem of measuring short text semantic similarity in NLP area. In particular, we propose a novel deep neural network architecture to identify semantic …
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Developing Deep Learning Models for Depression Detection in Texts
… to diagnose depression. Consequently, using deep learning models to detect depressed and non-depressed individuals based on social media posts, by analyzing the words being posted, has become the focus of recent research. The lack of big-sized depression-labeled datasets for training models …
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Knowledge-Informed Weakly-Supervised Deep Learning Models for Cancer Applications
In recent decades, deep learning (DL) has emerged as a powerful tool for analyzing complex patterns in large-scale healthcare data, significantly advancing diagnosis, prognosis, and treatment planning. However, the collection of medical data faces inherent limitations, including invasiveness, high …
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Satellite Image Analysis and Sidewalk Classification using Deep Learning Models
… classification tasks using pretrained CNN models including VGG16 and ResNet50. I extended these models by adding custom layers at the top of pretrained layers, employing various techniques to improve the classification accuracy.</p> <p>The dataset comprises 4,731 images of sidewalk based on …
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Domain Playground: Extending Deep Learning Models to Open Domain Boundaries
Deep learning models have demonstrated monumental performance in classification tasks but require extensive data and training procedures to converge. Additionally, the performance is only guaranteed when there is no domain gap (e.g., the distribution of the source and target data are similar). …
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Deep Learning Models for Traffic Prediction in Urban Transport Networks.
… named EM, on linear roadways based on machine learning technology. EM is able to analyse and extract spatial and temporal features from original traffic data for the final prediction. This short-term traffic prediction could give drivers traffic situation in advance and guide them to avoid …
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Improved hyperspectral classification of vegetation through generative deep learning models.
… the intra/inter-class relationship with deep generative sample transformation. For objective one the last two decades of hyperspectral vegetation classification literature was systematically reviewed, specifically focusing on waveband/feature selection. Additionally, waveband selection …
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Deep learning models for the perception of human social interactions
… by comparing state of the art computer vision models to neuroimaging data. In this thesis, I employ a similar method in order to study social interaction perception with deep learning models and magnetoencephalography (MEG) data. Specically, I implement dierent deep learning computer vision …
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Deep learning models for defect and anomaly detection on industrial surfaces
… are advances in computer vision and machine learning for defect detection, challenges persist, such as defect variability and the computational burden. This thesis presents specialized deep learning architectures addressing defect classification, segmentation, and detection in textiles, civil …
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Improving breast cancer risk assessment with image-based deep learning models
Discriminative models for breast cancer risk prediction are needed in order to provide personalized patient care. Existing breast cancer risk models incorporate information about breast tissue using imaging biomarkers such as density scores. However, these imaging biomarkers are limited in that …
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Local approximations of deep learning models for black-box adversarial attacks
… generation: query-based attacks and substitute models. In particular, we reinterpret adversarial transferability as a strong gradient prior. Based on this unification, we develop a method for integrating model-based priors into the generation of black-box attacks. The resulting algorithms …
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Deep Learning Models of Scanner/Vision Tunnel Performance in Sortation Subsystems
We propose an end-to-end process and tool to deep-dive scanner issues at Amazon’s sorter sites, allowing us to categorize no-reads into operational issues or actual equipment issues. Our tool sends no-read scanner images to a separate Amazon Web Services (AWS) server and post-processes them through …
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Application of probabilistic deep learning models to simulate thermal power plant processes
Deep learning has gained traction in thermal engineering due to its applications to process simulations, the deeper insights it can provide and its abilities to circumvent the shortcomings of classic thermodynamic simulation approaches by capturing complex inter-dependencies. This works sets out to …
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Adversarial robustness of deep learning models : an error-correcting codes based approach
… and to perform real-time control. Modern Machine Learning (ML) systems, particularly Deep Neural Networks (DNNs), provide a scalable solution to the problem of information retrieval from sensor data. Therefore, Deep Learning systems are increasingly playing an important role in day-to-day …
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