Sökning: "memory models"

Visar resultat 1 - 5 av 451 uppsatser innehållade orden memory models.

1. 1. Optical Communication using Nanowires and Molecular Memory Systems

   Master-uppsats, Lunds universitet/Fysiska institutionen; Lunds universitet/Synkrotronljusfysik

   Författare :Thomas Kjellberg Jensen; [2024]
   Nyckelord :neuromorphic computing; nanowire; molecular dye; DASA photoswitch; OBIC; Physics and Astronomy;

   Sammanfattning : Neuromorphic computational networks, inspired by biological neural networks, provide a possible way of lowering computational energy cost, while at the same time allowing for much more sophisticated devices capable of real-time inferences and learning. Since simulating artificial neural networks on conventional computers is particularly inefficient, the development of neuromorphic devices is strongly motivated as the reliance on AI-models increases. LÄS MER

3. 2. Exploring Attachment Dynamics: Implications for Overgeneralized Memory and Rumination

   Master-uppsats, Lunds universitet/Institutionen för psykologi

   Författare :Ocean Bouey; [2024]
   Nyckelord :Overgeneralized memory; attachment anxiety; attachment avoidance; rumination; narrative memory task; Social Sciences;

   Sammanfattning : Objective and Methods: This thesis investigates the relationship between insecure attachment (anxiety and avoidance), rumination, and overgeneralized episodic memory retrieval in nonclinical adults, addressing the potential moderating role of rumination in the attachment- overgeneralization link. A community sample of 41 Swedish-speaking women participated in a narrative memory task involving attachment and non-attachment narratives. LÄS MER

4. 3. 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

5. 4. Physical Exercise and Fatigue Detection using Machine Learning

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

   Författare :Filip Säterberg; Rasmus Nilsson; [2024]
   Nyckelord :Machine Learning; Fatigue Prediction; Data Collection; Supervised learning; Surface Electromyography; Accelerometers; Maskininlärning; Trötthetsförutsägelse; Datainsamling; Övervakad; Ytlig-elektromyografi Accelerometrar;

   Sammanfattning : Monitoring of physical exercise is an important task to evaluate and adapt exercise to provide better exercise results. The Inno-X™ device, developed by Innowearable, is a device that can be used for such monitoring. It collects data using an accelerometer and sEMG sensor. LÄS MER

7. 5. Audio Anomaly Detection in Cars

   Master-uppsats, Göteborgs universitet/Institutionen för matematiska vetenskaper

   Författare :Asma Hussein; [2023-09-11]
   Nyckelord :Audio Anomaly detection; Outlier detection; Machine learning; Mel Frequency; Chroma;

   Sammanfattning : Audio anomaly detection in the context of car driving is a crucial task for ensuring vehicle safety and identifying potential faults. This paper aims to investigate and compare different methods for unsupervised audio anomaly detection using a data set consisting of recorded audio data from fault injections and normal "no fault" driving. LÄS MER