Sökning: "Efficient Computation"

Visar resultat 1 - 5 av 150 uppsatser innehållade orden Efficient Computation.

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. Decoding the surface code using graph neural networks

   Master-uppsats, Göteborgs universitet / Institutionen för fysik

   Författare :Moritz Lange; [2023-10-17]
   Nyckelord :;

   Sammanfattning : Quantum error correction is essential to achieve fault-tolerant quantum computation in the presence of noisy qubits. Among the most promising approaches to quantum error correction is the surface code, thanks to a scalable two-dimensional architecture, only nearest-neighbor interactions, and a high error threshold. Decoding the surface code, i.e. LÄS MER

4. 3. Computationally Efficient Explainable AI: Bayesian Optimization for Computing Multiple Counterfactual Explanantions

   Master-uppsats, KTH/Matematik (Avd.)

   Författare :Giorgio Sacchi; [2023]
   Nyckelord :Explainable AI; Counterfactual Explanations CFEs ; Bayesian Optimization BO ; Black-Box Models; Model-Agnostic; Machine Learning ML ; Efficient Computation; High-Stake Decisions; Förklarbar AI; Kontrafaktuell Förklaring CFE ; Bayesiansk Optimering BO ; Svarta lådmodeller; Modellagnostisk; Maskininlärning; Beräkningsmässigt Effektiv; Beslut med höga insatser;

   Sammanfattning : In recent years, advanced machine learning (ML) models have revolutionized industries ranging from the healthcare sector to retail and E-commerce. However, these models have become increasingly complex, making it difficult for even domain experts to understand and retrace the model's decision-making process. LÄS MER

5. 4. Over-the-Air Federated Learning with Compressed Sensing

   Master-uppsats, Linköpings universitet/Kommunikationssystem

   Författare :Adrian Edin; [2023]
   Nyckelord :machine learning; ML; Federated Learning; FL; Over-the-air; Over-the-air computation; OtA; OtA computation; AirComp; Compressed sensing; CS; Iterative Hard thresholding; IHT;

   Sammanfattning : The rapid progress with machine learning (ML) technology has solved previously unsolved problems, but training these ML models requires ever larger datasets and increasing amounts of computational resources. One potential solution is to enable parallelization of the computations and allow local processing of training data in distributed nodes, such as Federated Learning (FL). LÄS MER

7. 5. Artificial Neural Networks for Financial Time Series Prediction

   Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

   Författare :Dana Malas; [2023]
   Nyckelord :artificial neural networks; time series analysis; deep learning; finance; long short-term memory; simple moving average;

   Sammanfattning : Financial market forecasting is a challenging and complex task due to the sensitivity of the market to various factors such as political, economic, and social factors. However, recent advances in machine learning and computation technology have led to an increased interest in using deep learning for forecasting financial data. LÄS MER