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 1230 for “"learning models"”.
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Efficient machine learning: models and accelerations
… of the recent unprecedented success of machine learning is the adoption of very large models. Modern machine learning models typically consist of multiple cascaded layers such as deep neural networks, and at least millions to hundreds of millions of parameters (i.e., weights) for the entire …
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Learning Models for Cyber-Physical Systems
Contains fulltext : 213663.pdf (Publisher’s version ) (Open Access)
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Automated Interpretation of Machine Learning Models
As machine learning (ML) models are increasingly deployed in production, there’s a pressing need to ensure their reliability through auditing, debugging, and testing. Interpretability, the subfield that studies how ML models make decisions, aspires to meet this need but traditionally relies on …
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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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Computational Face Recognition Using Machine Learning Models
… the various computational face recognition models are investigated to overcome the challenges posed by ageing and occlusions/partial faces. For partial face-based face recognition, a pre-trained VGGF model is employed for feature extraction and then followed by popular classifiers such as …
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Computational Face Recognition Using Machine Learning Models
… the various computational face recognition models are investigated to overcome the challenges posed by ageing and occlusions/partial faces. For partial face-based face recognition, a pre-trained VGGF model is employed for feature extraction and then followed by popular classifiers such as …
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Learning Models for Multi-Viewpoint Object Detection
… relations and algorithms for efficiently learning the model parameters. The first approach uses a generative model that models the joint probability distribution over the locations and visibility of all the object parts. The second approach employs a discriminative Conditional Random Field …
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Enhancing the robustness of machine learning models
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-08-01
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Learning models and the double monotone model
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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Evaluating Machine Learning Models of Sensory Systems
… of our field is to build stimulus-computable models of sensory systems that reproduce brain responses and behavior. The past decade has given rise to models that capture complex behaviors such as image classification, word recognition, and texture perception. Yet, there are known discrepancies …
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On evaluating counterfactual explanations for Machine Learning Models
… των μοντέλων Μηχανικής Μάθησης (Machine Learning – ML) μέσω αντιπαραθετικών εξηγήσεων (counterfactual explanations – CF), οι οποίες περιλαμβάνουν την πραγματοποίηση μικρών μεταβολών στα δεδομένα εισόδου προκειμένου να διερευνηθούν εναλλακτικά αποτελέσματα. Αναγνωρίζοντας τη διαρκώς …
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Machine learning models for reliable airline ancillary pricing
Machine learning is becoming increasingly prevalent for decision-making across key application areas such as healthcare, finance, law systems, and pricing. However, evaluating the predictive power of models on historical data is not enough. When deploying ML models in the real-world, system …
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Deep learning models for high-frequency financial data
… 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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Learning models of environments with manifest causal structure
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
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Learning models of world dynamics using Bayesian networks
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
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Towards Effective Tools for Debugging Machine Learning Models
… of detecting and fixing the errors of a machine learning (ML) model—model debugging. Current ML models, especially overparametrized deep neural networks (DNNs) trained on crowd-sourced data, easily latch onto spurious signals, underperform for small subgroups, and can be derailed by errors in …
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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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