Probabilistic artificial intelligence exercises
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Updated
Nov 8, 2022 - Python
Probabilistic artificial intelligence exercises
Use Deep Learning Methods to analyze gene based microarray data to make classifcations on diseases, especially cancers, where the model is going to identify cancer stages for different cancers.
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For an assignment at LSE, I deployed and evaluated a number of Bayesian machine-learning techniques based on their capabilities to correctly distinguish benign from malignant tumours.
Team programming exercises for the course unit "Probabilistic Artificial Intelligence", ETH Zurich
Project on using control variates for bayesian neural networks
Code and experiments for the preprint: "Bayesian Neural Network Versus Ex-Post Calibration For Capturing Prediction Uncertainty".
Full Log-Likelihood Heteroskedastic Regression with Deep Neural Networks and Tensorflow
AIS algorithm for BNN inference
Reimplementation of Sparse Variational Dropout in Keras-Core/Keras 3.0
Code repository of the NeurIPS 2021 paper Infinite Time Horizon Safety of Bayesian Neural Networks
Code for the ICASSP'19 submission "Modelling Sample Informativeness for Deep Affective Computing".
Variational Bayesian neural network for filtering and smoothing.
We compare the handwriting images of different writers from the AND dataset using Bayesian Networks using the features provided and compare the various handwritings.
Reproduction for the paper : Weight uncertainty in neural networks
This repository contains our work for the validation project of the course Bayesian Machine Learning, 2023/2024, ENS Paris-Saclay, Master MVA. It consists in a thorough study of the paper "Challenges in Markov chain Monte Carlo for Bayesian neural networks" by Theodore Papamarkou et al.
Bayesian Neural Network
Energy production forecasting ⚡ with PoC of Bayesian Neural Network 🎲
A comparison between Bayesian Neural Network and Classical Neural Network
Master thesis for the MSc. Artificial Intelligence at the Universiteit van Amsterdam, 2019
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