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Full-Text Articles in Physical Sciences and Mathematics
Probabilistic Forecasting Of Winter Mixed Precipitation Types In New York State Utilizing A Random Forest, Brian Chandler Filipiak
Probabilistic Forecasting Of Winter Mixed Precipitation Types In New York State Utilizing A Random Forest, Brian Chandler Filipiak
Legacy Theses & Dissertations (2009 - 2024)
Operational forecasters face a plethora of challenges when making a forecast; they must consider multiple data sources ranging from radar and satellites to surface and upper air observations, to numerical weather prediction output. Forecasts must be done in a limited window of time, which adds an additional layer of difficulty to the task. These challenges are exacerbated by winter mixed precipitation events where slight differences in thermodynamic profiles or changes in terrain create different precipitation types across small areas. In addition to being difficult to forecast, mixed precipitation events can have large-scale impacts on our society.
Deep Active Genetic Learning With Evidential Uncertainty For Agriculture Crops And Lake Water Quality Assessment, Oguz M. Aranay
Deep Active Genetic Learning With Evidential Uncertainty For Agriculture Crops And Lake Water Quality Assessment, Oguz M. Aranay
Legacy Theses & Dissertations (2009 - 2024)
Despite significant advancements in the field of machine learning, there are two issues that still require further exploration. First, how to learn from a small dataset; and second, how to select appropriate features from the data. Although there exist many techniques to address these issues, choosing a combination of the techniques from these two groups is challenging, and worth investigating. To address these concerns, this thesis presents a learning framework that is based on a deep learning model utilizing active learning (with evidential uncertainty as a basis for acquisition function) for the first issue and a genetic algorithm for the …
Stability And Differential Privacy Of Stochastic Gradient Methods, Zhenhuan Yang
Stability And Differential Privacy Of Stochastic Gradient Methods, Zhenhuan Yang
Legacy Theses & Dissertations (2009 - 2024)
Recently there are a considerable amount of work devoted to the study of the algorithmic stability as well as differential privacy (DP) for stochastic gradient methods (SGM). However, most of the existing work focus on the empirical risk minimization (ERM) and the population risk minimization problems. In this paper, we study two types of optimization problems that enjoy wide applications in modern machine learning, namely the minimax problem and the pairwise learning problem.
Exposing Gan-Generated Faces Using Deep Neural Network, Hui Guo
Exposing Gan-Generated Faces Using Deep Neural Network, Hui Guo
Legacy Theses & Dissertations (2009 - 2024)
Generative adversarial network (GAN) generated high-realistic human faces are visually challenging to discern from real ones. They have been used as profile images for fake social media accounts, which leads to high negative social impacts.In this work, we explore a universal physiological cue of the eye, namely the pupil shape consistency, to identify GAN-generated faces reliably. We show that GAN-generated faces can be exposed via irregular pupil shapes. This phenomenon is caused by the lack of physiological constraints in the GAN models. We demonstrate that such artifacts exist widely in high-quality GAN-generated faces. We design an automatic method to segment …