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 16 of 16 for “"noisy labels"”.
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On boosting and noisy labels
… learn from labeled data in order to predict the labels of unlabeled data. A central property of boosting instrumental to its popularity is its resistance to overfitting. Previous experiments provide a margin-based explanation for this resistance to overfitting. In this thesis, the main finding is …
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Understanding Deep Learning with Noisy Labels
… datasets. However, collecting high-quality labels for large-scale datasets is expensive and time-consuming or even infeasible in practice. Approaches to addressing this issue include: acquiring labels from non-expert labelers, crowdsourcing-like platforms or other unreliable resources, where …
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Classification with noisy labels : "Multiple Account" cheating detection in Open Online Courses
… of the CAMEO algorithm as a method for producing noisy predicted cheating labels. Then a solution to the more general problem of binary classification with noisy labels ( ~ P̃̃̃ Ñ learning) is a solution to CAMEO cheating detection. ~ P̃ Ñ learning is the problem of binary classification when …
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Annotation-Free Deep Learning of Large-Scale Nuclear Segmentation and Spatial Neighborhood Analysis on Multiplexed Fluorescence Images
… driving pipeline for Brain Cell Analysis using noisy Labels with minimal human input. 1) It uses a parametric method to generate noisy labels for cell nuclei and refines through an iterative training process. 2) We introduce a background recovery technique to enhance the detection and estimation …
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Towards a Reliable Deep Learning Framework for Prostate Cancer Diagnosis using Ultrasound
… (DL) models for PCa detection is hindered by noisy labels and cancer heterogeneity. The purpose of this work is to develop a clinically applicable framework for DL-based detection of PCa from ultrasound that is robust to noise and uncertainty inherent to ultrasound images and their associated …
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Noisy with a Chance of Mislabels: A Local and Training Dynamics Perspective on Detecting Label Noise in Deep Classification
Noisy labels are a pervasive challenge in modern supervised learning, especially in highstakes domains such as healthcare, where model reliability is critical. Detecting and mitigating the influence of mislabeled data is essential to improving both performance and interpretability. Building on …
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Deep image representation learning for knowledge discovery from earth observation data archives
… information extraction; iv) effective IRL under noisy training labels; and v) joint use of multiple learning tasks for describing the complex content of RS images. This thesis aims to develop advanced DL-based IRL methods to tackle these limitations, while a particular attention is devoted to …
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Machine Learning Methods for High Throughput Biological Data
… how this approach can be combined with noisy labels derived from transcriptomics to derive an effective classifier of cell-type specificity. In my second project, I consider the problem of predicting mass spectra of small molecules: previous methods suffer from a tradeoff between …
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AI-DRIVEN ATRIAL ARRHYTHMIA DETECTION: DEVELOPMENT, CROSS-COMPARISON AND UNCERTAINTY QUANTIFICATION OF ALGORITHMS FOR CLINICAL CONTINUOUS ECGS
… we investigated the impact of annotation errors (noisy labels) on model accuracy and implemented strategies to mitigate their effects. Additionally, we quantified the uncertainty in our DL model to assess prediction confidence and benchmarked 11 uncertainty quantification (UQ) methods for robust …
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Semi-Supervised Learning for Scalable and Robust Visual Search
… the graph construction process and the initial labels. We propose a new bivariate graph transduction formulation and an efficient solution via an alternating minimization procedure. Based on this bivariate framework, we also develop new methods to filter unreliable and noisy labels. Extensive …
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Learning to classify images without explicit human annotations
… first collecting examples along with candidate labels, second obtaining clean labels from workers, and third training a large, overparameterized deep neural network on the clean examples. The second, manual labeling step is often the most expensive one as it requires manually going through all …
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Learning with Constraint-Based Weak Supervision
… or weakly supervised learning involves using noisy labels (weak signals of the data) from multiple sources to train machine learning systems. A weak supervision model aggregates multiple noisy label sources called weak signals in order to produce probabilistic labels for the data. The main …
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Controlling the effect of crowd noisy annotations in NLP Tasks
… intrinsically can not handle the inaccurate and noisy annotations and the performance of the learners have a high correlation with the quality of the input data labels. Hence, the annotations have to be prepared by experts. However, collecting labels for large dataset is impractical to perform by …
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Speech recognition with probabilistic transcriptions and end-to-end systems using deep learning
… is that it is the first to train DNNs using noisy labels in the target language transcribed by non-native speakers available in online marketplaces. End-to-End Large Vocabulary Automatic Speech Recognition: Recent advances in ASR have been mostly due to the advent of deep learning models. …
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Robust Deep Learning Methodologies for Weakly Supervised Remote Sensing Image Classification
… robustness in a weak supervision scenario where labels are sparsely distributed in time. The proposed methodologies have been rigorously tested on different benchmark RS datasets. They consistently demonstrated significant improvements in classification accuracy, robustness, and generalizability. …
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Example weighting for deep representation learning
… less attention to non-informative (easy) and noisy (usually extremely hard) ones during the learning process. Therefore, example weighting is an important tool for guiding deep models to treat training samples differentially and learn meaningful patterns robustly and effectively. …