Essays about: "Multidimensional Deep Learning"
Found 4 essays containing the words Multidimensional Deep Learning.
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1. Towards Deep Learning Accelerated Sparse Bayesian Frequency Estimation
University essay from Lunds universitet/Matematisk statistikAbstract : 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. READ MORE
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2. Imputation and Generation of Multidimensional Market Data
University essay from Umeå universitet/Institutionen för matematik och matematisk statistikAbstract : 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. READ MORE
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3. Deep Learning for Dynamic Portfolio Optimization
University essay from KTH/Matematisk statistikAbstract : 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. READ MORE
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4. Multitask Convolutional Neural Network Emulators for Global Crop Models - Supervised Deep Learning in Large Hypercubes of Non-IID Data
University essay from Lunds universitet/Matematisk statistikAbstract : 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. READ MORE