Awesome resources on normalizing flows.
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Updated
Apr 12, 2024 - Python
Awesome resources on normalizing flows.
PyAF is an Open Source Python library for Automatic Time Series Forecasting built on top of popular pydata modules.
Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"
Open-AI's DALL-E for large scale training in mesh-tensorflow.
End-2-end speech synthesis with recurrent neural networks
🍊 📈 Orange add-on for analyzing, visualizing, manipulating, and forecasting time series data.
Generative model for sequential recommendation based on Convolution Neural Networks (CNN))
PyTorch implementation of "Conditional Image Generation with PixelCNN Decoders" by van den Oord et al. 2016
PyTorch Implementation of Google's Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions. This implementation supports both single-, multi-speaker TTS and several techniques to enforce the robustness and efficiency of the model.
A framework based on Tensorflow for running variational Monte-Carlo simulations of quantum many-body systems.
Forecasting Monthly Sales of French Champagne - Perrin Freres
[CVPR 2022] Look Outside the Room: Synthesizing A Consistent Long-Term 3D Scene Video from A Single Image
Pytorch implementations of autoregressive pixel models - PixelCNN, PixelCNN++, PixelSNAIL
[ICML 2024] This repository includes the official implementation of our paper "Rejuvenating image-GPT as Strong Visual Representation Learners"
Julia package containing utilities intended for Time Series analysis.
Sequence-to-Sequence Generative Model for Sequential Recommender System
A Repo of Time-series analysis techniques. Holt-Winter methods, ACF/PACF, MA, AR, ARMA, ARIMA, SARIMA, SARIMAX, VAR, VARMA, RNN Keras, Facebook- Prophet etc.
🥝 Autoregressive Models in PyTorch.
Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs
List of papers and code for relevant Generative Models
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