Sökning: "transfer-learning."

Visar resultat 11 - 15 av 276 uppsatser innehållade ordet transfer-learning..

1. 11. Adaptive Model-Based Temperature Monitoring for Electric Powertrains : Investigation and Comparative Analysis of Transfer Learning Approaches

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Chenzhou Huang; [2023]
   Nyckelord :Transfer Learning; Condition Monitoring; Domain Adaptation; Neural Network; Powerstrain.; Siirto-oppiminen; kunnonvalvonta; verkkotunnuksen mukauttaminen; neuroverkko; voimansiirto.; Överföring lärande; tillståndsövervakning; domänanpassning; neuralt nätverk; Powerstrain;

   Sammanfattning : In recent years, deep learning has been widely used in industry to solve many complex problems such as condition monitoring and fault diagnosis. Powertrain condition monitoring is one of the most vital and complicated problems in the automation industry since the condition of the drive affects its health, performance, and reliability. LÄS MER

3. 12. Head-to-head Transfer Learning Comparisons made Possible : A Comparative Study of Transfer Learning Methods for Neural Machine Translation of the Baltic Languages

   Master-uppsats, Uppsala universitet/Institutionen för lingvistik och filologi

   Författare :Mathias Stenlund; [2023]
   Nyckelord :machine translation; transfer learning; Latvian; Lithuanian; low-resource languages; transformers; parent language; child language; comparative study;

   Sammanfattning : The struggle of training adequate MT models using data-hungry NMT frameworks for low-resource language pairs has created a need to alleviate the scarcity of sufficiently large parallel corpora. Different transfer learning methods have been introduced as possible solutions to this problem, where a new model for a target task is initialized using parameters learned from some other high-resource task. LÄS MER

4. 13. Aggregating predictions of a yeast semantic segmentation model : Reducing a pixel classifier into a binary image classifier

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Ali Muquri; [2023]
   Nyckelord :;

   Sammanfattning : The introduction of machine learning in clinical microbiology is important for aiding clinical laboratories with highly repetitive tasks that are fatiguing, error-prone, and require long employee training time due to the complex nature of the task. A challenging task that belongs to the subareas that need assistance is yeast detection in fluorescence microscopy where various yeast morphologies exist. LÄS MER

5. 14. Automatic quality assessment of formed fiber products via Computer Vision and Artificial Intelligence

   Uppsats för yrkesexamina på grundnivå, Högskolan i Halmstad/Akademin för informationsteknologi

   Författare :Jesper Sköld; [2023]
   Nyckelord :Artificial intelligence; Deep learning; CNN; YOLO; Computer Vision; Fiber products; Machine learning; Neural network;

   Sammanfattning : Defects on fiber products have varied appearances and are common in production lines. A reliable system that can classify and identify defects without subjectivity and fatigue can improve a company's quality management. Computer vision systems are crucial for any autonomous system, but accuracy is essential for real-life applications. LÄS MER

7. 15. Efficient Sentiment Analysis and Topic Modeling in NLP using Knowledge Distillation and Transfer Learning

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :George Malki; [2023]
   Nyckelord :Large Language Model; RoBERTa; Knowledge distillation; Transfer learning; Sentiment analysis; Topic modeling; Stor språkmodell; RoBERTa; Kunskapsdestillation; överföringsinlärning; Sentimentanalys; Ämnesmodellering;

   Sammanfattning : This abstract presents a study in which knowledge distillation techniques were applied to a Large Language Model (LLM) to create smaller, more efficient models without sacrificing performance. Three configurations of the RoBERTa model were selected as ”student” models to gain knowledge from a pre-trained ”teacher” model. LÄS MER