Sökning: "Variational Recurrent Neural Network"

Hittade 4 uppsatser innehållade orden Variational Recurrent Neural Network.

  1. 1. Towards Latent Space Disentanglement of Variational AutoEncoders for Language

    Master-uppsats, Uppsala universitet/Institutionen för lingvistik och filologi

    Författare :Paloma García de Herreros García; [2022]
    Nyckelord :Variational Autoencoders; Latent Space; disentanglement;

    Sammanfattning : Variational autoencoders (VAEs) are a neural network architecture broadly used in image generation (Doersch 2016). VAEs are neural network models that encode data from some domain and project it into a latent space (Doersch 2016). LÄS MER

  3. 2. Modelling approach and avoidance behaviour : A deep learning approach to understand the human olfactory system

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Frans Nordén; [2021]
    Nyckelord :Probabilistic Machine Learning; Variational Recurrent Neural Network; EEG; Approach and Avoidance behaviour; Frontal alpha asymmetry; Probabilistisk Maskininlärning; Variational Recurrent Neural Network; EEG; Approach and Avoidance behaviour; Frontal alpha asymmetri;

    Sammanfattning : In this thesis we examine the question whether it is possible to model approach and avoidance behaviour with probabilistic machine learning. The results from this project will primarily aid in our collective understanding of human existence. LÄS MER

  4. 3. MahlerNet : Unbounded Orchestral Music with Neural Networks

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Elias Lousseief; [2019]
    Nyckelord :music; composition; algorithmic composition; neural networks; recurrent neural networks; RNN; variational autoencoder; VAE; LSTM; BALSTM; MusicVAE; PerformanceRNN; BachProp; musik; komposition; algoritmisk komposition; neurala nätverk; recurrent neural networks; RNN; variational autoencoder; VAE; LSTM; BALSTM; MusicVAE; PerformanceRNN; BachProp;

    Sammanfattning : Modelling music with mathematical and statistical methods in general, and with neural networks in particular, has a long history and has been well explored in the last decades. Exactly when the first attempt at strictly systematic music took place is hard to say; some would say in the days of Mozart, others would say even earlier, but it is safe to say that the field of algorithmic composition has a long history. LÄS MER

  5. 4. Multivariate analysis of the parameters in a handwritten digit recognition LSTM system

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Georgios Zervakis; [2019]
    Nyckelord :Deep Learning; Interpretability; Handwritten Digit Recognition; MNIST; Recurrent Neural Networks; PCA; SVD; Variational Autoencoders;

    Sammanfattning : Throughout this project, we perform a multivariate analysis of the parameters of a long short-term memory (LSTM) system for handwritten digit recognition in order to understand the model’s behaviour. In particular, we are interested in explaining how this behaviour precipitate from its parameters, and what in the network is responsible for the model arriving at a certain decision. LÄS MER