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 733 for “"Machine learning models"”.
-
Efficient machine learning: models and accelerations
… enablers 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 …
-
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 …
-
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 …
-
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 …
-
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
-
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 …
-
On evaluating counterfactual explanations for Machine Learning Models
… αποτελεσμάτων 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 …
-
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 …
-
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 …
-
Data Standardization and Machine Learning Models for Histopathology
Machine learning can provide insight and support for a variety of decisions. In some areas of medicine, decision-support models are capable of assisting healthcare practitioners in making accurate diagnoses. In this work we explored the application of these techniques to distinguish between two …
-
Machine Learning Models in Fullerene/Metallofullerene Chromatography Studies
Machine learning methods are now extensively applied in various scientific research areas to make models. Unlike regular models, machine learning based models use a data-driven approach. Machine learning algorithms can learn knowledge that are hard to be recognized, from available data. The …
-
ADVANCED MACHINE LEARNING MODELS IN PREDICTION OF MEDICAL CONDITIONS
The primary goal of Machine learning (ML) models in the prediction of medical conditions is to accurately predict (classify) the occurrence of a disease, or therapy. Many ML models, traditional and deep, have been utilized for the prediction of disease diagnosis, or prediction of the most optimal …
-
Robust machine learning models for high dimensional data interpretation
L'abstract è presente nell'allegato / the abstract is in the attachment
-
Statistical and Machine Learning Models to Predict Programming Performance
… programming success and on the development of machine learning models to predict incoming student performance. Although numerous studies have developed models to predict programming success, the models struggled to achieve high accuracy in predicting the likely performance of incoming students. …
-
Efficient and robust algorithms for training machine learning models
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms
-
Machine learning models on geographic spatial-temporal data predictions
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
-
Statistical and machine learning models for critical infrastructure resilience
… network. Using this formulation, we then develop machine learning approaches to predict delays in the rail network. Through experiments on real-world rail data, we find that the selected architecture provides more accurate predictions than other models due to its ability to capture both spatial …
-
The Trainability and Expressivity of Quantum Machine Learning Models
… than what is achievable using conventional models of computation. This culminated in recent years with experimental demonstrations on quantum devices of computational tasks on the verge of classical intractability. These current generation quantum devices are, however, too noisy and small to …
Page 1 of 37