Search for dissertations about: "neural computing"

Showing result 1 - 5 of 52 swedish dissertations containing the words neural computing.

  2. 1. Quantum state characterization with deep neural networks

    Author : Shahnawaz Ahmed; Chalmers tekniska högskola; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Machine learning; classification; optical quantum states.; quantum state tomography; deep neural networks; bosonic states; quantum computing; reconstruction; quantum state discrimination;

    Abstract : In this licentiate thesis, I explain some of the interdisciplinary topics connecting machine learning to quantum physics. The thesis is based on the two appended papers, where deep neural networks were used for the characterization of quantum systems. READ MORE

  4. 2. Machine learning for quantum information and computing

    Author : Shahnawaz Ahmed; Chalmers tekniska högskola; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; quantum machine learning; quantum process tomography; quantum information; Machine learning; generative neural networks; variational quantum algorithms; quantum state tomography; optimization; quantum computing; Bayesian estimation;

    Abstract : This compilation thesis explores the merger of machine learning, quantum information, and computing. Inspired by the successes of neural networks and gradient-based learning, the thesis explores how such ideas can be adapted to tackle complex problems that arise during the modeling and control of quantum systems, such as quantum tomography with noisy data or optimizing quantum operations, by incorporating physics-based constraints. READ MORE

  5. 3. Efficient Document Image Binarization using Heterogeneous Computing and Interactive Machine Learning

    Author : Florian Westphal; Håkan Grahn; Niklas Lavesson; Marcus Liwicki; Blekinge Tekniska Högskola; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; image binarization; heterogeneous computing; recurrent neural networks; interactive machine learning; historical documents;

    Abstract : Large collections of historical document images have been collected by companies and government institutions for decades. More recently, these collections have been made available to a larger public via the Internet. However, to make accessing them truly useful, the contained images need to be made readable and searchable. READ MORE

  6. 4. Towards Supporting IoT System Designers in Edge Computing Deployment Decisions

    Author : Majid Ashouri; Paul Davidsson; Romina Spalazzese; Malmö universitet; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Internet of Things; Edge computing; Decision Support; Quality Attrib-utes; Metrics; Simulation;

    Abstract : The rapidly evolving Internet of Things (IoT) systems demands addressing new requirements. This particularly needs efficient deployment of IoT systems to meet the quality requirements such as latency, energy consumption, privacy, and bandwidth utilization. READ MORE

  7. 5. Event-Driven Architectures for Heterogeneous Neuromorphic Computing Systems

    Author : Mattias Nilsson; Fredrik Sandin; Foteini Liwicki; Jerker Delsing; Chiara Bartolozzi; Luleå tekniska universitet; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Neuromorphic computing; Mixed-signal; Low-power; Non-von Neumann; Spatiotemporal pattern recognition; System integration; Cyber-Physical Systems; Cyberfysiska system;

    Abstract : Mixed-signal neuromorphic processors have brain-like organization and device physics optimized for emulation of spiking neural networks (SNNs), and offer an energy-efficient alternative for implementing artificial intelligence in applications where deep learning based on conventional digital computing is unfeasible or unsustainable. However, efficient use of such hardware requires appropriate configuration of its inhomogeneous, analog neurosynaptic circuits, with methods for sparse, spike-timing-based information encoding and processing. READ MORE