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 20 of 97 for “"Few-Shot"”.
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Few Shot Learning for Rare Disease Diagnosis
… The goal of this thesis is to develop few shot learning methods that can overcome the data limitations of deep learning approaches to diagnose patients with rare genetic conditions. Motivated by the need to infuse external knowledge into models, we first develop novel graph neural …
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Compositional Models for Few Shot Sequence Learning
… learning of new constructions and tenses from as few as eight initial examples. The second is a lexical translation mechanism for neural sequence modeling. Previous work shows that many failures of systematic generalization arise from neural models' inability to disentangle lexical phenomena from …
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Few-shot text classification with distributional signatures
We explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks. However, directly applying this approach to text is challenging-lexical features highly informative for one task …
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Code generation for few-shot event structure prediction
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01
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Feature extractor stacking for cross-domain few-shot learning
Cross-domain few-shot learning (CDFSL) addresses learning problems where knowledge needs to be transferred from one or more source domains into an instance-scarce target domain with an explicitly different distribution. Recently published CDFSL methods generally construct a universal model that …
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Deep Zero- and Few-shot Learning in Computer Vision
… includes the following subtopics: i) zero-shot learning, ii) few-shot learning, and iii) domain adaptation. In this thesis, we study zero-shot and few-shot learning to demonstrate how to learn better from limited training samples. Zero-shot learning requires learning a mapping that …
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Deep Zero- and Few-shot Learning in Computer Vision
… includes the following subtopics: i) zero-shot learning, ii) few-shot learning, and iii) domain adaptation. In this thesis, we study zero-shot and few-shot learning to demonstrate how to learn better from limited training samples. Zero-shot learning requires learning a mapping that …
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Few-Shot Semi-Supervised Robust Text Classification with MAML
The need for few-shot semi-supervised text classification arises in a variety of applications, including, e.g., recommendation systems classifying textual content such as product descriptions or news articles based on limited amounts of user feedback. In such settings, existing supervised methods …
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Few-Shot and Zero-Shot Learning for Information Extraction
… attribute-value extraction in e-commerce, with few labeled (few-shot learning) or even no labeled (zero-shot learning) training data. We explore multi-source auxiliary information and novel learning techniques to integrate semantic auxiliary information with the input text to improve few-shot …
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Improving few-shot object detection by saving and hallucinating examples
… to detect an object in an image from very few training examples - few-shot object detection - is challenging, because the classifier that sees proposal boxes has very little training data. A particularly challenging training regime occurs when there are one or two training examples. In this …
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A few-shot learning method for single-object visual anomaly detection
We propose a few-shot learning method for visually inspecting single objects in an industrial setting. The proposed method is able to identify whether or not an object is defective by comparing its visual appearance with a small set of images of the “working” object, i.e., the object that passes …
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Advances in Few-Shot Learning for Image Classification and Tabular Data
… data. These constraints underscore the need for few-shot learning methods, enabling models to adapt from just a handful of examples. In addition, many of these applications require updating only a small fraction of model parameters for adaptation -- a crucial form of parameter efficiency -- to …
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ExoSpotter: Few Shot Relevance Feedback For Learning High Recall Exoplanet Search
… human input is desirable. Unfortunately, very few labeled training data are available (i.e., light curves labeled as planet candidates), which makes automatic classification di cult. Here, we propose a new approach to identify planet candidates using relevance-feedback accelerated few-shot …
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Meta-Learning and Self-Supervised Pretraining for Few-shot Image Translation
… follow this line of work and contribute a novel few-shot multi-task image to image translation problem. We then present several benchmarks for this problem using ideas from both meta-learning and contrastive-learning and improve upon baselines trained using simple supervised learning. …
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Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning
… autoencoders, and probabilistic approaches for few-shot learning. As inference is rarely tractable, we discuss variational inference methods as a secondary theme. First, we disentangle the theoretical properties and optimisation behaviour of two widely used sparse Gaussian process …
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A Patch-as-Filter Method for Same-Different Problems with Few-Shot Learning
… are still a huge undertaking, particularly in few-shot learning cases. Little is known, especially in solving the Same-Different (SD) task, which is a type of visual reasoning task that requires seeking pattern repetitions in a single image. In this thesis, we propose a patch-as-filter method …
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Learning with Limited Labeled Data: Techniques and Applications
… (3) How to boost the performance of the few-shot learning model with unlabeled data, and (4) How to utilize limited labeled data to learn new classes without the training data in the same domain. First, we study few-shot learning in text classification tasks. Meta-learning is becoming a …
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Generalizing Under Data Scarcity. Enhancing the representation capability from few samples.
… transformers for supervised and unsupervised few-shot tasks. We explore how transformers behave when trained on structured, multi-domain datasets under controlled conditions, where train/test contamination can be explicitly avoided. By reframing few-shot learning as a sequence modeling …
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