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 27 for “"Multitask Learning"”.
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A New Framework for Semisupervised, Multitask Learning
To conclude, we interpret the internal representation of the model and use it to perform unsupervised scene discovery. Defining a meaningful vocabulary for scene discovery is a challenging problem that has important consequences for object recognition. We consider scenes to depict correlated …
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Regularization based multitask learning with applications in computational biology
… Zum anderen betrachten wir Methoden des Multitask Learnings, bei dem Informationen wechselseitig zwischen verschiedenen Domänen geteilt werden. Für den Fall von Domain Adaptation entwickeln wir Erweiterungen von etablierten Algorithmen zur regularisierten Risiko Minimierung, die es …
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Training Physics-Guided Neural Networks with Multiple Constraints: An Application in Lake Ecology Modeling
… and predicting lake ecology. While machine learning has shown potential in modeling such systems, sparse environmental data often limits the ability of machine learn- ing models to produce physically consistent predictions or generalize to novel conditions. As a result, many existing …
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Yield Curves and Macro Variables Interactions and Predictions
… predicted yield curves based on ANN Regression Multitask learning, and lastly, we predicted our five macro variables based on three different ANN Classifiers, in order to generalize and present results that are not specific to a country, or region, or model. The most persistence trend, amongst …
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Multilingual multitask joint neural information extraction
… knowledge across different models through multitask learning to reduce the need for data annotation. To maximize the knowledge being transferred, we design a unified and extendable architecture that integrates multiple transfer approaches. After that, we extend this framework to more IE …
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Emergent patterns of task-specific neurons in deep neural networks
… subject of curiosity within the realm of deep learning. While much research has gone into the development of neural network models that can at times outperform humans, the underlying principles behind truly understanding visual concepts remain elusive. Utilizing a multitask learning paradigm, …
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Disentangled representations in neural models
Representation learning is the foundation for the recent success of neural network models. However, the distributed representations generated by neural networks are far from ideal. Due to their highly entangled nature, they are difficult to reuse and interpret, and they do a poor job of capturing …
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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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Learning Representations for Limited and Heterogeneous Medical Data
… heterogeneity are challenges of representation learning for machine learning in medicine due to the diversity of medical data and the expense of data collection and annotation. To learn generalizable representations from such limited and heterogeneous medical data, we aim to utilize various …
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Object Detection on Unmanned Arial Vehicles Dataset Using Adaptive HydraNet
… this limitation by introducing AHydraNet, a multitask learning module based on the low-cost dynamic multitask architecture HydraNet. AHydraNet is a multilabel classification template with an adaptive threshold that enhances the precision of the detection for small and medium-sized objects. We …
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Transfer learning algorithms for image classification
… To achieve this goal we develop transfer learning algorithms that: 1) Leverage unlabeled data annotated with meta-data and 2) Exploit labeled data from related categories. In the first part of this thesis we show how to use the structure learning framework (Ando and Zhang, 2005) to learn …
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Decentralized teaching and learning in cooperative multiagent systems
… the real world. While many works have introduced learning (rather than planning) approaches for multiagent systems, few address the partially observable setting, and even fewer do so in a scalable manner deployable to real-world settings, such as multi-robot systems that face collections of tasks …
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A geospatial deep learning framework for scalable hydrographic mapping
… and limited scalability. Recent advances in deep learning and geospatial artificial intelligence (AI) present new opportunities to automate hydrography extraction by learning spatial and physical patterns directly from data. However, challenges remain in achieving spatial transferability, …
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Learning to prevent healthcare-associated infections : leveraging data across time and space to improve local predictions
… records holds out the promise of using machine learning and data mining to build models that will help healthcare providers improve patient outcomes. However, building useful models from these datasets presents many technical problems. Among the challenges are the large number of factors (both …
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Nonparametric High-dimensional Models: Sparsity, Efficiency, Interpretability
This thesis explores ensemble methods in machine learning, a technique that builds a predictive model by jointly training simpler base models. It examines three types of ensemble methods: additive models, tree ensembles, and mixtures of experts. Each ensemble method is characterized by a specific …
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DOMAIN KNOWLEDGE-GUIDED LEARNING FOR ROBUST MYOCARDIAL INFARCTION DETECTION FROM 12-LEAD ELECTROCARDIOGRAMS
… of automated MI diagnosis. In this context, deep learning (DL) has emerged as a promising approach for identifying MI from 12 lead ECG. Challenges: Despite the encouraging results reported for MI diagnosis, DL models are hindered by several key limitations. First, existing models often overlook …
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Spatiotemporal Event Forecasting and Analysis with Ubiquitous Urban Sensors
… neural network for crime prediction, a multitask learning system for traffic incident prediction with spatiotemporal feature learning, social media-based transportation event detection, and a graph convolutional network-based cyberbullying detection algorithm are the four methods …
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Deep image representation learning for knowledge discovery from earth observation data archives
… from these archives on a large-scale, deep learning (DL) based RS image representation learning (IRL) has attracted great attention. However, existing methods have limitations on: i) accurate characterization of high-level semantic content and spectral information present in RS images; ii) …
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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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Organ Viability Assessment in Transplantation based on Data-driven Modeling
… to increase the evaluation accuracy. 2) A multitask learning logistic regression model is applied to assess liver viability by using principal component analysis to extract infrared image features to quantify the correlation between liver viability and spatial infrared imaging data. This …
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