Sökning: "sparse learning"

Visar resultat 1 - 5 av 67 avhandlingar innehållade orden sparse learning.

1. 1. Cooperative Compressive Sampling

   Författare :Ahmed Zaki; Lars Kildehøj; Saikat Chatterjee; Jan Østergaard; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Compressive sampling; greedy algorithms; convex optimisation; RIP analysis; fusion strategy; distributed learning; sparse learning; stochastic matrix; consensus; sparse estimation; Telecommunication; Telekommunikation;

   Sammanfattning : Compressed Sampling (CS) is a promising technique capable of acquiring and processing data of large sizes efficiently. The CS technique exploits the inherent sparsity present in most real-world signals to achieve this feat. Most real-world signals, for example, sound, image, physical phenomenon etc., are compressible or sparse in nature. LÄS MER

3. 2. Sparse Modeling of Harmonic Signals

   Författare :Filip Elvander; Statistical Signal Processing Group; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; multi-pitch estimation; sparse modeling; convex optimisation; inharmonicity; sparse recursive least squares; adaptive signal processing; optimal transport distance;

   Sammanfattning : This thesis considers sparse modeling and estimation of multi-pitch signals, i.e., signals whose frequency content can be described by superpositions of harmonic, or close-to-harmonic, structures, characterized by a set of fundamental frequencies. LÄS MER

4. 3. Sensorimotor Robot Policy Training using Reinforcement Learning

   Författare :Ali Ghadirzadeh; Mårten Björkman; Danica Kragic; Atsuto Maki; Ville Kyrki; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Reinforcement Learning; Artificial Intelligence; Robot Learning; Sensorimotor; Policy Training; Computer Science; Datalogi;

   Sammanfattning : Robots are becoming more ubiquitous in our society and taking over many tasks that were previously considered as human hallmarks. Many of these tasks, e.g. LÄS MER

5. 4. Seismic Exploration Solutions for Deep-Targeting Metallic Mineral Deposits : From high-fold 2D to sparse 3D, and deep-learning workflows

   Författare :Magdalena Markovic; Alireza Malehmir; Gilles Bellefleur; Uppsala universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Exploration; Seismic; Mineral Deposits; Diffraction; Deep learning;

   Sammanfattning : Mineral exploration has in recent years moved its focus to greater depths than ever before, particularly in brown fields. Exploring new deposits at depth, if economical, would not only expand the life of mine but also provide minimal environmental impacts. It allows the existing mining infrastructures to be used for a longer period. LÄS MER

7. 5. Adapting Deep Learning for Microscopy: Interaction, Application, and Validation

   Författare :Ankit Gupta; Carolina Wählby; Ida-Maria Sintorn; Ola Spjuth; Andreas Hellander; Philip Kollmannsberger; Uppsala universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Deep Learning; Microscopy; Human-in-the-Loop; Semi-Supervised Learning; Application-Specific Analysis; Image Classification; Image-to-Image Translation; Template Matching; Computerized Image Processing; Datoriserad bildbehandling;

   Sammanfattning : Microscopy is an integral technique in biology to study the fundamental components of life visually. Digital microscopy and automation have enabled biologists to conduct faster and larger-scale experiments with a sharp increase in the data generated. LÄS MER