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 8 of 8 for “"Hyper parameter tuning"”.
-
A Transfer Learning Approach for Automatic Mapping of Retrogressive Thaw Slumps (RTSs) in the Western Canadian Arctic
… and backbone trainable layers, and performed hyper-parameter tuning and determined the optimal learning rate, momentum, and decay rate for each of the model settings. Our final model successfully mapped most of the RTSs in our test sites, with F1 scores ranging from 0.61 to 0.79. Our study …
-
Scalable hierarchical evolution strategies
… algorithms are relatively slow and brittle to hyperparameter changes. This paper offers a solution to these slow and brittle HRL algorithms, by investigating a novel method combining Scalable Evolution Strategies (SES) and HRL. S-ES, named for its excellent scalability, was popularised by Open …
-
Bias and Fairness of Evasion Attacks in Image Perturbation
… stands, the Fawkes system has a fixed set of hyper parameters for amount of perturbations added per image, which essentially means that they consider all users be treated identically in terms of amount of perturbations added. However, from testing our hypothesis through running various …
-
Random Projection Optimal Trees Ensemble
… when used with the wrong choice of their hyper-parameters values and/or when there are noisy features in the data. Thus, feature selection and fine tuning hyper-parameter could improve predictive accuracy of ensemble classifiers. This thesis first investigates the effect of feature …
-
Pushing the limits of traditional unsupervised learning
… feature extraction methods along with careful parameter selection is presented. This framework is able to achieve state-of-the-art clustering performance that is better than many deep learning-based methods on large benchmark and web-based text and image datasets. This pipeline incorporates …
-
Bridging Machine Learning and Experimental Design for Enhanced Data Analysis and Optimization
… mutual information, fractional factorial design, hyper-parameter tuning, multi-modality, etc. In Chapter 2, I propose a new mutual information estimator FLO by integrating techniques from variational inference (VAE), contrastive learning, and convex optimization. I apply FLO to broad data science …
-
Fully Automated Segmentation of High Grade Serous Ovarian Cancer on Computed Tomography Images using Deep Learning
… segmentation approaches are compared, and hyper-parameter tuning is applied to maximise their performance. Finally, we demonstrate how the discussed algorithms can be deployed in clinical workflows for the purpose of reducing manual annotation time. Our key results are the following. We …
-
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization
… to propose a study of the behavior of several hyper-parameters that influence the RL algorithm via a multi-measure approach helped with statistical tests. To demonstrate its superiority against industry-preferred computational methods, we compared its performance against the most adopted legacy …