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Full-Text Articles in Other Statistics and Probability
Decoding Neural Networks: An Information-Theoretic Guide To Interpretability, Error Analysis And Efficiency, Mackenzie J. Meni
Decoding Neural Networks: An Information-Theoretic Guide To Interpretability, Error Analysis And Efficiency, Mackenzie J. Meni
Theses and Dissertations
This dissertation addresses critical challenges in neural network design by leveraging entropy-based techniques to improve model efficiency, interpretability, and bias reduction. Focusing on the unique demands of computer vision applications, particularly object detection and classification for real-time systems, this work introduces a series of innovative methods centered on information theory. At the core of these methods is the Probabilistic Explanations of Entropic Knowledge (PEEK) framework, a tool developed to analyze and visualize entropy distributions across feature maps. PEEK offers insights into information flow within neural networks, making it possible to pinpoint layers that contribute meaningfully to decision-making or identify those …
Bagging Improves The Performance Of Deep Learning-Based Semantic Segmentation With Limited Labeled Images: A Case Study Of Crop Segmentation For High-Throughput Plant Phenotyping, Yinglun Zhan, Yuzhen Zhou, Geng Bai, Yufeng Ge
Bagging Improves The Performance Of Deep Learning-Based Semantic Segmentation With Limited Labeled Images: A Case Study Of Crop Segmentation For High-Throughput Plant Phenotyping, Yinglun Zhan, Yuzhen Zhou, Geng Bai, Yufeng Ge
Department of Statistics: Faculty Publications
Advancements in imaging, computer vision, and automation have revolutionized various fields, including field-based high-throughput plant phenotyping (FHTPP). This integration allows for the rapid and accurate measurement of plant traits. Deep Convolutional Neural Networks (DCNNs) have emerged as a powerful tool in FHTPP, particularly in crop segmentation—identifying crops from the background—crucial for trait analysis. However, the effectiveness of DCNNs often hinges on the availability of large, labeled datasets, which poses a challenge due to the high cost of labeling. In this study, a deep learning with bagging approach is introduced to enhance crop segmentation using high-resolution RGB images, tested on the …
Perceived Neighborhood: Preferences Versus Actualities, Saeed Moradi, Ali Nejat, Da Hu, Souparno Ghosh
Perceived Neighborhood: Preferences Versus Actualities, Saeed Moradi, Ali Nejat, Da Hu, Souparno Ghosh
Department of Statistics: Faculty Publications
Housing recovery plays a key role in the overall restoration of a community. A multitude of factors affect housing recovery, many of which are associated with interactions of residents with their perceived neighborhoods. Targeting perceived neighborhoods rather than administratively defined measures of land helps with devising recovery plans that could better address social preferences of the residents. However, such measures are commonly subject to collection of information via expensive and time-consuming surveys. The current research aims to contribute to the domain by exploring the relationship between perception of households of their neighborhood anchors (perceived anchors) and the anchors that exist …
Existing And Potential Statistical And Computational Approaches For The Analysis Of 3d Ct Images Of Plant Roots, Zheng Xu, Camilo Valdes, Jennifer Clarke
Existing And Potential Statistical And Computational Approaches For The Analysis Of 3d Ct Images Of Plant Roots, Zheng Xu, Camilo Valdes, Jennifer Clarke
Department of Statistics: Faculty Publications
Scanning technologies based on X-ray Computed Tomography (CT) have been widely used in many scientific fields including medicine, nanosciences and materials research. Considerable progress in recent years has been made in agronomic and plant science research thanks to X-ray CT technology. X-ray CT image-based phenotyping methods enable high-throughput and non-destructive measuring and inference of root systems, which makes downstream studies of complex mechanisms of plants during growth feasible. An impressive amount of plant CT scanning data has been collected, but how to analyze these data efficiently and accurately remains a challenge. We review statistical and computational approaches that have been …