Search for dissertations about: "computational learning optimal"

Showing result 1 - 5 of 51 swedish dissertations containing the words computational learning optimal.

  2. 1. Multidimensional inverse problems in imaging and identification using low-complexity models, optimal mass transport, and machine learning

    Author : Axel Ringh; Johan Karlsson; Rodolphe Sepulchre; KTH; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Inverse problems; convex optimization; variational regularization; trigonometric moment problems; optimal mass transport; computed tomography; machine learning; analytic interpolation; delay systems; Inversa problem; konvex optimering; variationell regularisering; trigonometriska momentproblem; optimal masstransport; datortomografi; maskininlärning; analytisk interpolation; system med tidsfördröjning; Mathematics; Matematik;

    Abstract : This thesis, which mainly consists of six appended papers, primarily considers a number of inverse problems in imaging and system identification.In particular, the first two papers generalize results for the rational covariance extension problem from one to higher dimensions. READ MORE

  4. 2. How can data science contribute to a greener world? : an exploration featuring machine learning and data mining for environmental facilities and energy end users

    Author : Dong Wang; Mats Tysklind; Johan Trygg; Lili Jiang; Venkat Venkatasubramanian; Umeå universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Wastewater treatment; Process analytics; Big data; Machine learning; Interpretable AI; Power plants; Failure analysis; Data mining; Buildings; Energy consumption; Anomaly detection;

    Abstract : Human society has taken many measures to address environmental issues. For example, deploying wastewater treatment plants (WWTPs) to alleviate water pollution and the shortage of usable water; using waste-to-energy (WtE) plants to recover energy from the waste and reduce its environmental impact. READ MORE

  5. 3. Data-driven Methods in Inverse Problems

    Author : Jonas Adler; Ozan Öktem; Thomas Pock; KTH; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Inverse Problems; Machine Learning; Tomography;

    Abstract : In this thesis on data-driven methods in inverse problems we introduce several new methods to solve inverse problems using recent advancements in machine learning and specifically deep learning. The main goal has been to develop practically applicable methods, scalable to medical applications and with the ability to handle all the complexities associated with them. READ MORE

  6. 4. Synergies between Chemometrics and Machine Learning

    Author : Rickard Sjögren; Johan Trygg; Olivier Cloarec; Ola Spjuth; Umeå universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; computational science; machine learning; chemometrics; multivariate data analysis; design of experiments; data science; beräkningsvetenskap; maskininlärning; kemometri; multivariat dataanalys; experimentdesign;

    Abstract : Thanks to digitization and automation, data in all shapes and forms are generated in ever-growing quantities throughout society, industry and science. Data-driven methods, such as machine learning algorithms, are already widely used to benefit from all these data in all kinds of applications, ranging from text suggestion in smartphones to process monitoring in industry. READ MORE

  7. 5. Decentralized Constrained Optimization: a Novel Convergence Analysis

    Author : Firooz Shahriari Mehr; Chalmers tekniska högskola; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Constrained optimization; Decentralized optimal transport; Distributed optimization; Multi-agent systems; Convergence analysis; Convex optimization;

    Abstract : One reason for the spectacular success of machine learning models is the appearance of large datasets. These datasets are often generated by different computational units or agents and cannot be processed on a single machine due to memory and computing limitations. READ MORE