Essays about: "Djupa Neurala Nätverk"

Showing result 1 - 5 of 134 essays containing the words Djupa Neurala Nätverk.

  1. 1. EMONAS : Evolutionary Multi-objective Neuron Architecture Search of Deep Neural Network

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Jiayi Feng; [2023]
    Keywords : DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Embedded Systems; DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Inbyggda system;

    Abstract : Customized Deep Neural Network (DNN) accelerators have been increasingly popular in various applications, from autonomous driving and natural language processing to healthcare and finance, etc. However, deploying them directly on embedded system peripherals within real-time operating systems (RTOS) is not easy due to the paradox of the complexity of DNNs and the simplicity of embedded system devices. READ MORE

  3. 2. Transformer Offline Reinforcement Learning for Downlink Link Adaptation

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Alexander Mo; [2023]
    Keywords : Link Adaptation; Transformers; Reinforcement Learning; Sequence Modelling; Decision Transformer; Deep Neural Networks; Radio Resource Management; Telecommunication; Länkanpassning; Transformers; Reinforcement Learning; Sekvensmodellering; Beslutsstöd; Djupa neurala nätverk; Dataresurshantering; Telekommunikation;

    Abstract : Recent advancements in Transformers have unlocked a new relational analysis technique for Reinforcement Learning (RL). This thesis researches the models for DownLink Link Adaptation (DLLA). READ MORE

  4. 3. Modelling Proxy Credit Cruves Using Recurrent Neural Networks

    University essay from KTH/Matematisk statistik

    Author : Lucas Fageräng; Hugo Thoursie; [2023]
    Keywords : Deep Neural Networks; Credit Risk; Financial Modelling; LSTM; Credit Default Swaps; Credit Valuation Adjustment; Djupa Neurala Nätverk; Kreditrisk; Finansiell Modellering; LSTM; Kreditswappar; Kreditvärderingsjustering;

    Abstract : Since the global financial crisis of 2008, regulatory bodies worldwide have implementedincreasingly stringent requirements for measuring and pricing default risk in financialderivatives. Counterparty Credit Risk (CCR) serves as the measure for default risk infinancial derivatives, and Credit Valuation Adjustment (CVA) is the pricing method used toincorporate this default risk into derivatives prices. READ MORE

  5. 4. Finding the QRS Complex in a Sampled ECG Signal Using AI Methods

    University essay from KTH/Fysik

    Author : Jeanette Marie Victoria Skeppland Hole; [2023]
    Keywords : ECG; ECG-analysis; QRS detector; Artificial Intelligence; Machine Learning; Deep neural networks; Long short-term memory; Convolutional neural network; Multilayer perceptron; EKG; EKG-analys; QRS detektor; Artificiell intelligens; Maskininlärning; Djupa neurala nätverk; Long short-term memory; Convolutional neural network; Multilayer perceptron;

    Abstract : This study aimed to explore the application of artificial intelligence (AI) and machine learning (ML) techniques in implementing a QRS detector forambulatory electrocardiography (ECG) monitoring devices. Three ML models, namely long short-term memory (LSTM), convolutional neural network (CNN), and multilayer perceptron (MLP), were compared and evaluated using the MIT-BIH arrhythmia database (MITDB) and the MIT-BIH noise stress test database (NSTDB). READ MORE

  6. 5. Visual Attention Guided Adaptive Quantization for x265 using Deep Learning

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Mikaela Gärde; [2023]
    Keywords : video encoding; deep learning; visual attention; adaptive quantization; videokodning; djupinlärning; visuellt fokus; adaptiv kvantisering;

    Abstract : The video on demand streaming is raising drastically in popularity, bringing new challenges to the video coding field. There is a need for new video coding techniques that improve performance and reduce the bitrates. READ MORE