Sökning: "Model of Computation"

Visar resultat 1 - 5 av 346 uppsatser innehållade orden Model of Computation.

  1. 1. Measuring the Utility of Synthetic Data : An Empirical Evaluation of Population Fidelity Measures as Indicators of Synthetic Data Utility in Classification Tasks

    Master-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

    Författare :Alexander Florean; [2024]
    Nyckelord :Synthetic Data; Machine Learning; Population Fidelity Measures; Utility Metrics; Synthetic Data Quality Evaluation; Classification Algorithms; Utility Estimation; Data Privacy; Artificial Intelligence; Experiment Framework; Model Performance Assessment; Syntetisk Data; Maskininlärning; Population Fidelity Mätvärden; Användbarhetsmätvärden; Kvalitetsutvärdering av Syntetisk Data; Klassificeringsalgoritmer; Användbarhetsutvärdering; Dataintegritet; Artificiell Intelligens; AI; Experiment Ramverk; Utvärdering av Modellprestanda;

    Sammanfattning : In the era of data-driven decision-making and innovation, synthetic data serves as a promising tool that bridges the need for vast datasets in machine learning (ML) and the imperative necessity of data privacy. By simulating real-world data while preserving privacy, synthetic data generators have become more prevalent instruments in AI and ML development. LÄS MER

  3. 2. 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

  4. 3. 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

  5. 4. Development of a cable analysis theory based on integral equation derivation

    Uppsats för yrkesexamina på avancerad nivå, Luleå tekniska universitet/Institutionen för teknikvetenskap och matematik

    Författare :Sandra Larsson; [2023]
    Nyckelord :;

    Sammanfattning : Electrification of transportation is a major subject of research today, especially in air travel. The increasing number of electronics and cables also increases the interference between them. From this, interest grew to increase the accuracy of electromagnetic modeling of cables. LÄS MER

  7. 5. 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