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 82 for “"Meta learning"”.
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Meta-learning in Medicine
… same time, the advances in the field of machine learning, specifically deep learning has accommodated the opportunity for knowledge discovery and data mining algorithms to gain insight from this digital health data. Predictive modeling of clinical risks from EHRs, such as in-hospital mortality …
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Meta-learning computational intelligence architectures
… In this dissertation the notion of memes and meta-learning is extended from a computational viewpoint and the purpose, definitions, design guidelines and architecture for effective meta-learning are explored. The background and structure of meta-learning architectures is discussed, …
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Meta-learning for adaptive filtering
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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Meta-learning representations with relational structure
Representation learning has emerged as a versatile tool that is able to take advantage of the vast datasets acquired using digital technologies. The broad applicability of this method stems from its flexibility in use as a subsystem and malleability in incorporating priors in model architectures. …
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Data and Computation Efficient Meta-Learning
… with high accuracy, conventional deep learning systems require large training datasets consisting of thousands or millions of examples and long training times measured in hours or days, consuming high levels of electricity with a negative impact on our environment. It is desirable to …
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Meta-Learning Exploration Strategies with Decision Transformers
… exploration approaches drawn from reinforcement learning and active hypothesis testing typically rely on heuristic strategies that require explicit prior assumptions about such structural information. However, when this information is unknown, heuristic methods often lead to redundant …
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Implementing Control-oriented Meta-learning on Hardware
… also known as drones. The control-oriented meta-learning algorithm aims to solve this problem by learning a controller that can adapt to dynamic environments. This algorithm has already been derived and simulated for a two-dimensional model. This project explores the implementation of the …
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Meta-Learning and the Full Model Selection Problem
… outlier detection, feature selection, learning algorithm and evaluation techniques, for a given data project. This indeed was an enjoyable job at the beginning, because to me finding patterns and valuable information from data is always fun. Things become tricky when several projects …
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Sparsity-aware personalized recommender system via meta-learning
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
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Advances in Probabilistic Meta-Learning and the Neural Process Family
A natural progression in machine learning research is to automate and learn from data increasingly many components of our learning agents.Meta-learning is a paradigm that fully embraces this perspective, and can be intuitively described as embodying the idea of learning to learn. A goal of …
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Meta-learning and Enforcing Useful Conservation Laws in Sequential Prediction Problems
In recent years, deep learning techniques have enjoyed storied success in a wide array of problem domains, including computer vision, natural language processing, and robotics. While much of this success can be attributed to increasing availability of both data and computing resources, the …
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Meta-Learning and Self-Supervised Pretraining for Few-shot Image Translation
Recent advances in machine learning (ML) and deep learning in particular, enabled by hardware advances and big data, have provided impressive results across a wide range of computational problems such as computer vision, natural language, or reinforcement learning. Many of these improvements are …
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A study of meta-learning methods on the problem of video matting
… in video data. In this work, we studied two Meta Learning approaches—Boosting with Adapters (BwA) and Boosting using Ensemble (BuE)—to tackle the task of video matting using pre-trained image matting models. BwA refines (image matting) alpha mattes by fine tuning pre-trained segmentation …
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Advances in Meta-Learning, Robustness, and Second-Order Optimisation in Deep Learning
In machine learning, we are concerned with developing algorithms that are able to learn, that is, to accumulate knowledge about how to do a task without having been programmed specifically for that purpose. In this thesis, we are concerned with learning from two different perspectives: domains to …
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Learning to teach and meta-learning for sample-efficient multiagent reinforcement learning
Learning optimal policies in the presence of non-stationary policies of other simultaneously learning agents is a major challenge in multiagent reinforcement learning (MARL). The difficulty is further complicated by other challenges, including the multiagent credit assignment, the high …
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Game Theory and Meta Learning for Optimization of Integrated Satellite-Drone-Terrestrial-Communication Systems
… dynamics is investigated using machine learning solutions with meta training capabilities. First, the use of satellites for on-demand coverage to unforeseeable radio access needs is investigated using game theory. The optimal data routing strategies are learned by the satellite system, …
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Better Generalization with Less Human Annotation Using Meta-Learning and Self-Supervised Learning for Image Analysis
… the data efficiency issue in training the deep learning model, we proposed the solutions in two directions: meta-learning and self-supervised learning. Meta-learning tries to generate a robust model that can learn to quickly adapt to new tasks with minimal labeled samples. It is also called …
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META LEARNING IN PROCESS MINING: TOWARD A SYSTEMATIC APPROACH TO DESIGN DATA ANALYTICS PIPELINES WITH EVENT LOGS
… are better suited. For that, we rely on a meta-learning approach that maps the relationships between event data and suitable solutions. The application of the proposed framework generates two main contributions. First, given a business process (event log) and a task (e.g., process …
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