Sökning: "Encoder-Decoder architecture"

Visar resultat 1 - 5 av 14 uppsatser innehållade orden Encoder-Decoder architecture.

1. 1. Deep Neural Networks as SurrogateModels for Fuel Performance Codes

   Kandidat-uppsats, Uppsala universitet/Tillämpad kärnfysik

   Författare :Wenhan Zhou; [2023]
   Nyckelord :Transuranus; AI; Nuclear Fuel Rods;

   Sammanfattning : The core component of a nuclear power plant is the reactor and the fuel rods that supply it with fission fuel. Efficient and safe energy extraction is thus highly dependent on the reactor design and the conditions of the fuel rods. To anticipate high-quality operation and potential risks in advance, one must perform simulations on the fuel rods. LÄS MER

3. 2. Robust Multi-Modal Fusion for 3D Object Detection : Using multiple sensors of different types to robustly detect, classify, and position objects in three dimensions.

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

   Författare :Viktor Kårefjärd; [2023]
   Nyckelord :Computer Vision; 3D Object Detection; Multi-Modal Fusion; Deep Learning; Datorseenden; 3D-objektdetektion; Multimodal fusion; Djupinlärning;

   Sammanfattning : The computer vision task of 3D object detection is fundamentally necessary for autonomous driving perception systems. These vehicles typically feature a multitude of sensors, such as cameras, radars, and light detection and ranging sensors. LÄS MER

4. 3. Java Syntax Error Repair Using RoBERTa

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

   Författare :Ziyi Xiang; [2022]
   Nyckelord :Java program repair; RoBERTa; Neural machine translation architecture; Reparation av Java­program; RoBERTa; Neural maskinöversättningsarkitektu;

   Sammanfattning : Deep learning has achieved promising results for automatic program repair (APR).In this paper, we revisit this topic and propose an end-to-end approach Classfix tocorrect java syntax errors. Classfix uses the RoBERTa classification model to localizethe error, and uses the RoBERTa encoder-decoder model to repair the located buggyline. LÄS MER

5. 4. ML-Aided Cross-Band Channel Prediction in MIMO Systems

   Master-uppsats, Linköpings universitet/Statistik och maskininlärning

   Författare :Alejo Pérez Gómez; [2022]
   Nyckelord :MIMO; Deep Learning; Machine Learning; Probabilistic Principal Component Analysis; Variational Autoencoder;

   Sammanfattning : Wireless communications technologies have experienced an exponential development during the last decades. 5G is a prominent exponent whose one of its crucial component is the Massive MIMO technology. LÄS MER

7. 5. Medical image captioning based on Deep Architectures

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

   Författare :Georgios Moschovis; [2022]
   Nyckelord :Artificial Neural Networks; Deep Learning; Speech and language technology; Natural Language Processing NLP ; Deep networks; Generative deep networks; Convolutional neural networks CNN ; Text generation; Information retrieval; Diagnostic captioning; Image captioning; concept prediction; classification; image encoders; transformers; Encoder-Decoder architecture; abstractive summarization; Neurala nätverk; Djup inlärning; Tal-och språkteknologi; naturlig språkbehandling; djup neurala nätverk; generativa djupa nätverk; konvolutionella neurala nätverk; Textgenerering; Informationssökning; Diagnostisk textning; Bildtextning; konceptförutsägelse; klassificering; bildkodare; transformatorer; kodaravkodararkitektur; abstrakt sammanfattning;

   Sammanfattning : Diagnostic Captioning is described as “the automatic generation of a diagnostic text from a set of medical images of a patient collected during an examination” [59] and it can assist inexperienced doctors and radiologists to reduce clinical errors or help experienced professionals increase their productivity. In this context, tools that would help medical doctors produce higher quality reports in less time could be of high interest for medical imaging departments, as well as significantly impact deep learning research within the biomedical domain, which makes it particularly interesting for people involved in industry and researchers all along. LÄS MER