Sökning: "arkitektur modeller"

Visar resultat 1 - 5 av 106 uppsatser innehållade orden arkitektur modeller.

1. 1. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition

   Master-uppsats, KTH/Mekatronik och inbyggda styrsystem

   Författare :Helgi Hrafn Björnsson; Jón Kaldal; [2023]
   Nyckelord :Recurrent Neural Networks; Long Short-Term Memory Networks; Embedded Systems; Human Activity Recognition; Edge AI; TensorFlow Lite Micro; Recurrent Neural Networks; Long Short-Term Memory Networks; Innbyggda systyem; Mänsklig aktivitetsigenkänning; Edge AI; TensorFlow Lite Micro;

   Sammanfattning : Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. LÄS MER

3. 2. 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

4. 3. Generation of a metrical grid informed by Deep Learning-based beat estimation in jazz-ensemble recordings

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

   Författare :Andres Alonso Toledo Carrera; [2023]
   Nyckelord :Beat tracking; Metrical grid; Jazz; Music Information Retrieval; Deep Learning; Temporal Convolutional Network; Beatuppskattning; Metriskt rutnät; Jazz; Music Informationshämting; Deep Learning; Temporal Convolutional Network;

   Sammanfattning : This work uses a Deep Learning architecture, specifically a state-of-the-art Temporal Convolutional Network, to track the beat and downbeat positions in jazz-ensemble recordings to derive their metrical grid. This network architecture has been used successfully for general beat tracking purposes. LÄS MER

5. 4. Regularizing Vision-Transformers Using Gumbel-Softmax Distributions on Echocardiography Data

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

   Författare :Alfred Nilsson; [2023]
   Nyckelord :Deep Learning; Vision-Transformers; Echocardiography; Feature Selection; Gumbel-Softmax; Concrete Autoencoders; Regression; Djupinlärning; Vision-Transformers; Ekokardiografi; Feature Selection; GumbelSoftmax; Concrete Autoencoders; Regression;

   Sammanfattning : This thesis introduces an novel approach to model regularization in Vision Transformers (ViTs), a category of deep learning models. It employs stochastic embedded feature selection within the context of echocardiography video analysis, specifically focusing on the EchoNet-Dynamic dataset. LÄS MER

7. 5. A Transformer-Based Scoring Approach for Startup Success Prediction : Utilizing Deep Learning Architectures and Multivariate Time Series Classification to Predict Successful Companies

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

   Författare :Gustaf Halvardsson; [2023]
   Nyckelord :Machine learning; Time Series Classification; Transformers; Gated Recurrent Unit; Venture Capital; Maskininlärning; tidsseriesklassifiering; Transformer; Gated Recurrent Unit; riskkapital;

   Sammanfattning : The Transformer, an attention-based deep learning architecture, has shown promising capabilities in both Natural Language Processing and Computer Vision. Recently, it has also been applied to time series classification, which has traditionally used statistical methods or the Gated Recurrent Unit (GRU). LÄS MER