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 7 of 7 for “"Test-Time Adaptation"”.
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Resilient Object Perception for Robotics
… of learningbased perception modules during test-time. In this thesis, we address these challenges by proposing (1) certifiably optimal solvers and a graph-theoretic framework that together help achieve state-of-the-art pose estimation performance even under high outlier rates, (2) …
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Heterogeneous machine learning with decentralized data
… we ensure robustness during both training and adaptation, preventing performance degradation caused by random failures or malicious attacks? To address (P1), we develop client clustering algorithms that enable knowledge transfer among clients with similar data distributions, allowing those with …
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Empowering vision machine perception for robust telehealth applications
… techniques. In the second focus, although test-time adaptation (TTA) techniques offer promise in handling domain-shift challenges during ML model deployment, they are susceptible to error accumulation and even adversarial attack. We extensively investigate this issue, resulting in the …
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Robust Machine Learning Methods in Solving Inverse Problems
… superior reconstruction accuracy and faster runtime, they tend to lack interpretability and physical grounding. Model-based architectures such as loop unrolling (LU) draw inspiration from optimization by unrolling the iterative updates for an optimization-based solver and then learning a …
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Inference-Time Learning Algorithms of Language Models
… fundamental questions remain about when this adaptation works, what algorithms underlie it, and how to improve it. This thesis studies the mechanisms and limitations of ICL and develops better methods for test time adaptation of LMs on diverse benchmarks of language modeling and reasoning. I …
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Label-Efficient Visual Understanding with Consistency Constraints
… labeled dataset is available during the training time. However, the progress of these visual tasks is limited by the number of manual annotations. On the other hand, it is usually time-consuming and error-prone to annotate visual data, rendering the challenge of scaling up human labeling for many …
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MITIGATING DATA SCARCITY CHALLENGES IN MEDICAL IMAGING ANALYSIS:ADVANCED LEARNING APPROACHES WITH EMPHASIS ON HEMOPHILIC ULTRASOUND IMAGES
… of total samples, class imbalance, and the adaptation of trained models to different domains (such as knee to elbow transfer). To address the problem of the limited number of total samples, this research investigates the adoption of transfer learning and proposes a new multi-task model to …