Sökning: "neural maskin"

Visar resultat 1 - 5 av 20 uppsatser innehållade orden neural maskin.

1. 1. Utilizing energy-saving techniques to reduce energy and memory consumption when training machine learning models : Sustainable Machine Learning

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

   Författare :Khalid El Yaacoub; [2024]
   Nyckelord :Sustainable AI; Machine learning; Quantization-Aware Training; Model Distillation; Quantized Distillation; Siamese Neural Networks; Continual Learning; Experience Replay; Data Efficient AI; Energy Consumption; Energy-Savings; Sustainable ML; Computation resources; Hållbar maskin inlärning; Hållbar AI; Maskininlärning; Quantization-Aware Training; Model Distillation; Quantized Distillation; siamesiska neurala nätverk; Continual Learning; Experience Replay; Dataeffektiv AI; Energiförbrukning; Energibesparingar; Beräkningsresurser;

   Sammanfattning : Emerging machine learning (ML) techniques are showing great potential in prediction performance. However, research and development is often conducted in an environment with extensive computational resources and blinded by prediction performance. LÄS MER

3. 2. 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

4. 3. Local Integrals of Motion from Neural Networks

   Master-uppsats, KTH/Fysik

   Författare :Hannes Karlsson; [2023]
   Nyckelord :many-body localization; integrals of motion; neural networks; machine learning; flerkroppslokalisering; rörelseintegraler; neurala nätverk; maskininlärning;

   Sammanfattning : Neural network quantum states (NNQS) is a novel machine learning method, based on restricted Boltzmann machines, previously used to represent the wave function in many-body quantum mechanics. In this thesis, we use NNQS to instead find integrals of motion, i.e., operators, commuting with the Hamiltonian, describing a system. LÄS MER

5. 4. Evaluating Incremental Machine Learning for Smart Home Adaptation with Embedded Systems

   M1-uppsats, Malmö universitet/Institutionen för datavetenskap och medieteknik (DVMT)

   Författare :Alban Islami; Nezar Sheikhi; [2023]
   Nyckelord :Machine learning; embedded systems; incremental learning; online learning; smart home;

   Sammanfattning : The combination of machine learning on embedded systems has quickly increased throughout the years. Subsets like TinyML have become an integral part of how embedded systems implement machine learning. The field has evolved quickly, and TinyOL is an emerging subset that redefines what is possible with embedded systems. LÄS MER

7. 5. Modeling of the primary sludge thickening process at a wastewater treatment plant with the use of machine learning

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

   Författare :Eric Bröndum; [2022]
   Nyckelord :Machine Learning; Stockholms Vatten och Avfall; Primary Sludge; Dewatering; Wastewater Treatment Plant; Simulation; time series prediction; Maskininlärning; Stockholms Vatten och Avfall; Primärslam; Förtjockning; Avloppsreningsverk; Simulering; Tidsserieprediktion;

   Sammanfattning : This thesis focuses on modeling the primary sludge in the thickening process at Henrikdals wastewater treatment plant in Stockholm, Sweden. The thickening process is one of the core processes at the wastewater treatment plant, where the goal is to thicken a residual product called primary sludge. LÄS MER