Sökning: "Probabilistic machine learning"

Visar resultat 1 - 5 av 45 avhandlingar innehållade orden Probabilistic machine learning.

1. 1. Forecasting of Icing Related Wind Energy Production Losses : Probabilistic and Machine Learning Approaches

   Författare :Jennie Molinder; Anna Sjöblom; Heiner Körnich; Hans Bergström; Erik Nilsson; Sue Ellen Haupt; Uppsala universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Wind energy; Icing on wind turbines; Machine learning; Probabilistic forecasting; Meteorologi; Meteorology;

   Sammanfattning : Icing on wind turbine blades causes significant production losses for wind energy in cold climate. Next-day forecasts of these production losses are crucial for the power balance in the electrical grid and for the trading process, but they are uncertain due to lack of understanding of, and simplifications, in the modelling chain. LÄS MER

3. 2. Towards Accurate and Reliable Deep Regression Models

   Författare :Fredrik K. Gustafsson; Thomas B. Schön; Martin Danelljan; Søren Hauberg; Uppsala universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Machine Learning; Deep Learning; Regression; Probabilistic Models; Energy-Based Models; Uncertainty Estimation; Machine learning; Maskininlärning;

   Sammanfattning : Regression is a fundamental machine learning task with many important applications within computer vision and other domains. In general, it entails predicting continuous targets from given inputs. LÄS MER

4. 3. Confidence Predictions in Pharmaceutical Sciences

   Författare :Staffan Arvidsson McShane; Ola Spjuth; Wesley Schaal; Lars Carlsson; Ernst Ahlberg; Stefan Kramer; Uppsala universitet; []
   Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; machine learning; QSAR; Conformal prediction; Venn-ABERS; Probabilistic machine learning; Machine learning; Maskininlärning; Farmaceutisk vetenskap; Pharmaceutical Science;

   Sammanfattning : The main focus of this thesis has been on Quantitative Structure Activity Relationship (QSAR) modeling using methods producing valid measures of uncertainty. The goal of QSAR is to prospectively predict the outcome from assays, such as ADMET (Absorption, Distribution, Metabolism, Excretion), toxicity and on- and off-target interactions, for novel compounds. LÄS MER

5. 4. Deep learning applied to system identification : A probabilistic approach

   Författare :Carl Andersson; Thomas B. Schön; Uppsala universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Electrical Engineering with specialization in Signal Processing; Elektroteknik med inriktning mot signalbehandling;

   Sammanfattning : Machine learning has been applied to sequential data for a long time in the field of system identification. As deep learning grew under the late 00's machine learning was again applied to sequential data but from a new angle, not utilizing much of the knowledge from system identification. LÄS MER

7. 5. Reliable Uncertainty Quantification in Statistical Learning

   Författare :David Widmann; Fredrik Lindsten; Dave Zachariah; Erik Sjöblom; Dino Sejdinovic; Uppsala universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Reliability; Calibration; Uncertainty; Probabilistic Model; Prediction; Julia; Machine learning; Maskininlärning;

   Sammanfattning : Mathematical models are powerful yet simplified abstractions used to study, explain, and predict the behavior of systems of interest. This thesis is concerned with their latter application as predictive models. LÄS MER