DanceNet -💃💃Dance generator using Autoencoder, LSTM and Mixture Density Network. (Keras)
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Updated
Sep 15, 2019 - Python
DanceNet -💃💃Dance generator using Autoencoder, LSTM and Mixture Density Network. (Keras)
A generic Mixture Density Networks (MDN) implementation for distribution and uncertainty estimation by using Keras (TensorFlow)
Mixture density network implemented in PyTorch.
Handwriting generation and handwriting synthesis as described in Alex Graves's paper https://arxiv.org/abs/1308.0850. Pytorch implementation.
Mixture Density Networks (MDN) implemented in PyTorch
Conditional density estimation with neural networks
IMPSy - the Interactive Musical Prediction SYstem
Gaussian Mixture Convolutional AutoEncoder applied to CT lung scans from the Kaggle Data Science Bowl 2017
Playground for implementing custom layers and other components compatible with keras, with the purpose to learn the framework better and perhaps in future offer some utils for others.
Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks (EMNLP2017)
Mixture Density Network Demo in Pytorch
(WIP) Implementation of a network for Handwriting Synthesis based on the work of Generating Sequences With Recurrent Neural Networks by Alex Graves (https://arxiv.org/abs/1308.0850)
Implementation of a Mixture Density Network in the deep probabilistic programming language Pyro.
A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design
Rapid characterization of exoplanet interiors with Mixture Density Networks
A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design
Trained models for the paper "Machine-learning inference of interior structures of low-mass exoplanets" (Baumeister et al. 2020)
An Embodied Musical Predictive Interface using Mixture Density Networks
Modeling of Time-varying Wireless Communication Channel with Fading and Shadowing
To reproduce the results of the paper: Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilities
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