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 81 for “"Multi-task learning"”.
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Efficient Continuous Pareto Exploration in Multi-Task Learning
Tasks in multi-task learning often correlate, conflict, or even compete with each other. As a result, a single solution that is optimal for all tasks rarely exists. Recent papers introduced the concept of Pareto optimality to this field and directly cast multi-task learning as multiobjective …
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DEEP MULTI-TASK LEARNING FOR FACE AND HUMAN ANALYSIS
In this thesis, we use multi-task learning methods to solve face and human analysis tasks. We design multi-task learning models to learn multiple face and human analysis tasks. We demonstrate that deep multi-task learning can be used to perform the face attribute classification task and up to 40 …
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Multi-triage: A Multi-Task Learning Approach to Bug Triaging
… the effectiveness of linkages between two triage tasks. An automated approach to assisting the issue allocation process to relevant category and developer benefits bug triages. A large body of previous work aims to address the allocation problem by conjecturing the extensive list of approaches …
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Multi task learning and incorporating common sense knowledge for question answering
… has been made in the recent years for this task, since the advent of deep learning and use of sequence to sequence models for NLP. This thesis deals with two complex tasks in Question Answering with their own inherent challenges: Multi Task Learning for Narrative Question Answering, which …
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Deep Structured Multi-Task Learning for Computer Vision in Autonomous Driving
… computer vision is currently dominated by deep learning advances. Convolutional Neural Networks (CNNs) have become the predominant tool for solving almost any computer vision task, so state-of-the-art systems have been built by using the predictive capabilities of Convolutional Neural Networks …
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Predicting Individual Components of the SOFA Score using Multi-Task Learning
… individual components of the SOFA score. We use multi-task learning frameworks to predict future values for the SOFA score components, with the goal of sharing information across the different tasks to improve overall predictive performance. We use approximately 53,000 days of time-series …
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Video Scene Understanding: Semantic-based representation, Temporal Variation Modeling, Multi-Task Learning
… descriptors in videos, and (iii) proposing a multitask learning framework to leverage the huge amount of unlabeled videos. The first category covers a method for enriching visual words that contain local motion information but they lack information about the cause of the motion. Our proposed …
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Predicting Performance Run-time Metrics in Fog Manufacturing using Multi-task Learning
… services based on the optimal computation task offloading, scheduling, and hardware autoscaling strategies to finish the computation tasks on time without compromising on the quality of the computation service. A prerequisite for adapting such optimal strategies is to accurately predict the …
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Recent Advances on Statistical Network Analysis and Multi-task Learning for Complex Data
… effect estimation. To support this, I develop a multi-task learning approach that leverages historical trials of the treatment being studied to identify prognostic variables, which can guide the design and analysis of new studies. The performance is validated through simulations and demonstrated …
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A deeper look into multi-task learning ability of unified text-to-text transformer
Structure prediction (SP) tasks are important in natural language understanding in the sense that they provide complex and structured knowledge of the text. Recently, some unified text-to-text transformer models like T5 and TANL have produced competitive results on SP tasks. These models convert SP …
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Design and evaluation of a hybrid multi-task learning model for optimizing deep reinforcement learning agents
… within the artificial intelligence domain, deep learning has emerged as a promising representation learning technique. This in turn has given rise to the evolution of deep reinforcement learning that combines deep learning with reinforcement learning methods. Subsequently, performance …
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Multiple Tasks are Better than One: Multi-task Learning and Feature Selection for Head Pose Estimation, Action Recognition and Event Detection
… some specific object, feature, or activity. This task can normally be solved robustly and without effort by a human, but is still not satisfactorily solved in computer vision for the general case - arbitrary objects in arbitrary situations. The existing methods for dealing with this problem can at …
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ENHANCING POLYMER INFORMATICS: A STUDY ON THE IMPACT OF FINGERPRINTING METHODS AND MACHINE LEARNING MODELS
… prediction. This study will evaluate machine learning model performance prediction accuracy using single-task and multi-task learning methods. We examined eight polymer properties and added polymer genome (PG) fingerprints to Morgan fingerprinting for more accurate predictions. This work aims …
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Multi-Task Reinforcement Learning: From Single-Agent to Multi-Agent Systems
… of the technology. The ability to develop these multi-task, multi-agent drone systems is limited by the lack of available training environments, as well as deficiencies of multi-task learning due to a phenomenon known as catastrophic forgetting. In this thesis, we present a set of simulation …
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Learning matrix and functional models in high-dimensions
Statistical machine learning methods provide us with a principled framework for extracting meaningful information from noisy high-dimensional data sets. A significant feature of such procedures is that the inferences made are statistically significant, computationally efficient and scientifically …
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Statistical models with diverging dimensionality
… dimensionality and iii) statistical analysis for multi-task learning.
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Privileged Machine Learning for Prediction
Machine learning for prediction suffers from asymmetric distribution, such as posterior information, future information and hidden information. With some additional information only available in training, how to learn a machine learning model with them remains a key challenge. Despite recent …
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Novel Algorithms for Understanding Online Reviews
… and has found applications in many downstream tasks, such as recommendation, information retrieval and review summarization. In this dissertation, we aim to develop machine learning and natural language processing tools to understand and learn structured knowledge from unstructured reviews, …
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Spatio-temporal Event Detection and Forecasting in Social Media
… a generative framework for event forecasting, multi-task learning for spatiotemporal event forecasting, multi-source spatiotemporal event forecasting, and deep learning based epidemic modeling for forecasting influenza outbreaks. For the first of these methods, existing solutions for …
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