Sökning: "transfer-learning"

Visar resultat 1 - 5 av 26 avhandlingar innehållade ordet transfer-learning.

1. 1. Data-Efficient Reinforcement and Transfer Learning in Robotics

   Författare :Xi Chen; Patric Jensfelt; Ville Kyrki; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Computer Science; Datalogi;

   Sammanfattning : In the past few years, deep reinforcement learning (RL) has shown great potential in learning action selection policies for solving different tasks.Despite its impressive success in games, several challenges remain, such as designing appropriate reward functions, collecting large amounts of interactive data, and dealing with unseen cases, which make it difficult to apply RL algorithms to real-world robotics tasks. LÄS MER

3. 2. Transfer Learning using low-dimensional Representations in Reinforcement Learning

   Författare :Isac Arnekvist; Danica Kragic; Johannes Andreas Stork; Christos Dimitrakakis; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Computer Science; Datalogi;

   Sammanfattning : Successful learning of behaviors in Reinforcement Learning (RL) are often learned tabula rasa, requiring many observations and interactions in the environment. Performing this outside of a simulator, in the real world, often becomes infeasible due to the large amount of interactions needed. LÄS MER

4. 3. Biomechanical Parameter Estimation for Wearable Exoskeleton System Design

   Författare :Longbin Zhang; Elena Gutierrez Farewik; Ruoli Wang; Christian Smith; Max Ortiz Catalan; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Hill-type muscle model; generalization; transfer learning; computationally modeling; human-machine system; Engineering Mechanics; Teknisk mekanik;

   Sammanfattning : Exoskeletons are increasingly used in rehabilitation and daily life in persons with motor disorders after neurological injuries. The overall objective of this thesis is to study how to robustly and accurately predict joint torque using inputs from sensors that would technically be feasible to equip on an assistive exoskeleton, and to develop a framework that could be used to evaluate the user-exoskeleton interface. LÄS MER

5. 4. Sharing to learn and learning to share : Fitting together metalearning and multi-task learning

   Författare :Richa Upadhyay; Marcus Liwicki; Ronald Phlypo; Rajkumar Saini; Atsuto Maki; Luleå tekniska universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Multi-task learning; Meta learning; transfer learning; knowledge sharing algorithms; Machine Learning; Maskininlärning;

   Sammanfattning : This thesis focuses on integrating learning paradigms that ‘share to learn,’ i.e., Multitask Learning (MTL), and ‘learn (how) to share,’ i.e. LÄS MER

7. 5. Advancing Clinical Decision Support Using Machine Learning & the Internet of Medical Things : Enhancing COVID-19 & Early Sepsis Detection

   Författare :Mahbub Ul Alam; Rahim Rahmani; Jaakko Hollmén; Sadok Ben Yahia; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Internet of Medical Things; Patient-Centric Healthcare; Clinical Decision Support System; Predictive Modeling in Healthcare; Health Informatics; Healthcare analytics; COVID-19; Sepsis; COVID-19 Detection; Early Sepsis Detection; Lung Segmentation Detection; Medical Data Annotation Scarcity; Medical Data Sparsity; Medical Data Heterogeneity; Medical Data Security Privacy; Practical Usability Enhancement; Low-End Device Adaptability; Medical Significance; Interpretability; Visualization; LIME; SHAP; Grad-CAM; LRP; Electronic Health Records; Thermal Image; Tabular Medical Data; Chest X-ray; Machine Learning; Deep Learning; Federated Learning; Semi-Supervised Machine Learning; Multi-Task Learning; Transfer Learning; Multi-Modality; Natural Language Processing; ClinicalBERT; GAN; data- och systemvetenskap; Computer and Systems Sciences;

   Sammanfattning : This thesis presents a critical examination of the positive impact of Machine Learning (ML) and the Internet of Medical Things (IoMT) for advancing the Clinical Decision Support System (CDSS) in the context of COVID-19 and early sepsis detection.It emphasizes the transition towards patient-centric healthcare systems, which necessitate personalized and participatory care—a transition that could be facilitated by these emerging fields. LÄS MER