Sökning: "Multidimensional Deep Learning"

Hittade 4 uppsatser innehållade orden Multidimensional Deep Learning.

1. 1. Towards Deep Learning Accelerated Sparse Bayesian Frequency Estimation

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Mika Persson; [2022]
   Nyckelord :Bayesian Statistics; Deep Learning; Frequency Estimation; Generative Adversarial Networks; Artificial Neural Networks; Statistical Modelling; Mathematics and Statistics;

   Sammanfattning : The Discrete Fourier Transform is the simplest way to obtain the spectrum of a discrete complex signal. This thesis concerns the case when the signal is known to contain a small (unknown) number of frequencies, not limited to the discrete Fourier frequencies, embedded in complex Gaussian noise. LÄS MER

3. 2. Imputation and Generation of Multidimensional Market Data

   Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för matematik och matematisk statistik

   Författare :Tobias Wall; Jacob Titus; [2021]
   Nyckelord :Time Series Imputation; Financial Time Series; Machine Learning; Deep Learning; Value at Risk; Expected Shortfall; Imputering av Tidsserier; Finansiella Tidsserier; Maskininlärning; Djupinlärning; Value at Risk; Expected Shortfall;

   Sammanfattning : Market risk is one of the most prevailing risks to which financial institutions are exposed. The most popular approach in quantifying market risk is through Value at Risk. Organisations and regulators often require a long historical horizon of the affecting financial variables to estimate the risk exposures. LÄS MER

4. 3. Deep Learning for Dynamic Portfolio Optimization

   Master-uppsats, KTH/Matematisk statistik

   Författare :Victor Molnö; [2021]
   Nyckelord :Dynamic portfolio optimization; No-trade-region; Deep learning; Policy iteration; Dynamisk portföljoptimering; Handelsstoppregion; Djupinlärning; Policyiterering;

   Sammanfattning : This thesis considers a deep learning approach to a dynamic portfolio optimization problem. A proposed deep learning algorithm is tested on a simplified version of the problem with promising results, which suggest continued testing of the algorithm, on a larger scale for the original problem. LÄS MER

5. 4. Multitask Convolutional Neural Network Emulators for Global Crop Models - Supervised Deep Learning in Large Hypercubes of Non-IID Data

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Amanda Nilsson; [2020]
   Nyckelord :Multitask Learning; Convolutional Neural Network CNN ; Branched Neural Network; Dynamic Global Vegetation Models DGVM ; Automated Feature Extraction; Feature Importance; Supervised Machine Learning; Emulator; Surrogate Model; Response Surface Model; Approximation Model; Metamodeling; Model Composition; Regularization; Robustness; Hyperparameter Optimization; Mathematics and Statistics;

   Sammanfattning : The aim of this thesis is to establish whether a neural network (NN) can be used for emulation of simulated global crop production - retrieved from the computationally demanding dynamic global vegetation model (DGVM) Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS). It has been devoted to elaboration with various types of neural network architectures: Branched NNs capable of processing inputs of mixed data types; Convolutional Neural Network (CNN) architectures able to perform automated temporal feature extraction of the given weather time series; simpler fully connected (FC) structures as well as Multitask NNs. LÄS MER