A Systematic Comparison of Robustness in Bayesian Deep Learning on Diabetic Retinopathy Diagnosis Tasks
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Updated
Nov 21, 2022 - Python
A Systematic Comparison of Robustness in Bayesian Deep Learning on Diabetic Retinopathy Diagnosis Tasks
Variational continual learning of a conditional diffusion model to generate MNIST. Based on 'Conditional Diffusion MNIST'.
Code repository of the NeurIPS 2021 paper Infinite Time Horizon Safety of Bayesian Neural Networks
PyTorch implementation of the paper 'Weight Uncertainty in Neural Networks'
Adaptive Conditional Quantile Neural Processes - PyTorch
Latex code for my computer science master thesis, "A comparison of frequentist methods and Bayesian approximations in the implementation of Convolutional Neural Networks in an Active Learning setting".
Master thesis for the MSc. Artificial Intelligence at the Universiteit van Amsterdam, 2019
Practice & experiment of bayesian deep neural networks, mainly using pixyz
Code for the ICASSP'19 submission "Modelling Sample Informativeness for Deep Affective Computing".
Bayesian Neural Network
EVCL-WC: Elastic Variational Continual Learning with Weight Consolidation
Work as part of ANL summer 2020 research on uncertainity quanitification methods in graph neural networks
My excursions in the world of Artificial Neural Networks
Benchmarking Bayesian Deep Learning for Out-of-Distribution Detection
Code accompanying ICLR 2024 paper "Function-space Parameterization of Neural Networks for Sequential Learning"
Research-repository: Bayesian neural networks for predicting disruptions using EFIT and diagnostic data in KSTAR
We provide two notebooks that enable users to explore and experiment with some BDL techniques as Ensembles, MC Dropout and Laplace Approximation. In this way, they allow you to intuitively visualize the main differences among them in a Simulated Dataset and Boston Dataset.
PheSeq, A Bayesian Deep Learning Model to Enhance and Interpret the Gene Disease Association Studies
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