Sökning: "Models of Computation"
Visar resultat 1 - 5 av 200 uppsatser innehållade orden Models of Computation.
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)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
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)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. Computationally Efficient Explainable AI: Bayesian Optimization for Computing Multiple Counterfactual Explanantions
Master-uppsats, KTH/Matematik (Avd.)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
4. Parameter Inference for Stochastic Models of Gene Expression in Eukaryotic Cells
Kandidat-uppsats, Uppsala universitet/Institutionen för informationsteknologiSammanfattning : Simulation models are often used to study a system or phenomenon. However, before a simulation model can be used, its parameter needs to be fit to mimic observed data. This is called the parameter inference problem. LÄS MER
5. Preserving Privacy in Cloud Services by Using an Explainable Deep-Learning Model for Anomaly Detection
Master-uppsats, Linköpings universitet/Institutionen för datavetenskapSammanfattning : As cloud services become increasingly popular, ensuring their privacy and security has become a significant concern for users. Cloud computing involves Data Service Outsourcing and Computation Outsourcing, which require additional security considerations compared to traditional computing. LÄS MER