AMLab-Amsterdam/L0_regularization

This repository has been archived on 10/Mar/2021
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AMLab-Amsterdam/L0_regularization

AMLab-Amsterdam

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Learning Sparse Neural Networks through L0 regularization

Example implementation of the L0 regularization method described at

Learning Sparse Neural Networks through L0 regularization, Christos Louizos, Max Welling & Diederik P. Kingma, https://openreview.net/pdf?id=BkdI3hgRZ

This code is provided as is and is not maintained / updated.

AttentionDeepMIL

Implementation of Attention-based Deep Multiple Instance Learning in PyTorch

CEVAE

Causal Effect Inference with Deep Latent-Variable Models

lie_learn

Computations involving Lie groups and harmonic analysis

DIVA

Implementation of 'DIVA: Domain Invariant Variational Autoencoders'

MNF_VBNN

Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks

FNP

Implementation of the Functional Neural Process models

Jupyter Notebook

SEVDL_MGP

Example implementation of the Bayesian neural network in "Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors", Christos Louizos & Max Welling, ICML 2016

DataAugmentationInterventions

Manuals

MasterProjectsAIMaterials

A reading list for the Master AI projects June 2016

data_aug_for_interventions