Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.
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Updated
Jan 13, 2025 - Python
Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.
A minimal implementation of a VAE with BinConcrete (relaxed Bernoulli) latent distribution in TensorFlow.
Implementing a bayesian neural network in TensorFlow
Distributed Training of Bayesian Neural Networks at Scale
Tensor utilities, reinforcement learning, and more!
TensorFlow ML - an abstract implementation of commonly used machine learning algorithms using TensorFlow. Feel free to contribute! (Work in Progress)
Keras, Tensorflow eager execution implementation of Neural Processes
Keras, Tensorflow eager execution implementation of Categorical Variational Autoencoder
Code accompanying my 2021 ASA SDSS paper
Deep reinforcement learning implementations with TensorFlow and TensorFlow probability.
Statistics MSc Project (2020): Audio Source Separation
Statistics and Machine Learning in depth analysis with Tensorflow Probability
This code finds an optimal architecture, search for hyperparameters (using OPTUNA code), trains and make predictions using LYMAN alpha part of simulated 1D super massive black hole spectra and converts it to intergalactic medium gas conditions along the line of sight. However, this code easily be utilized for ANY 1D signals using supervised ML.
Implementation of Bayesian Non-negative Matrix Factorization using Variational Inference with TensorFlow Probability
Variational Factorization Machines in TensorFlow and PyTorch
Reinforce-lib is a easy to use, simple to extend reinforcement learning library for Python: https://pos.sissa.it/415/018/pdf.
Assorted useful tensorflow functions and keras layers
Task in belong laboratory (related: https://github.com/chiru1221/LabStudyTask2020)
This repository contains supporting code for the paper "Selecting a conceptual hydrological model using Bayes' factors computed with Replica Exchange Hamiltonian Monte Carlo" by Mingo et al.
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