My excursions in the world of Artificial Neural Networks
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Updated
Oct 28, 2017 - Jupyter Notebook
My excursions in the world of Artificial Neural Networks
Building a Bayesian deep learning classifier
In which I try to demystify the fundamental concepts behind Bayesian deep learning.
Implementations of the ICML 2017 paper (with Yarin Gal)
Deep Learning Models using Tensorflow & Keras
An implementation of natural parameter networks and its extension to GRUs in PyTorch
Code for 'Relational Deep Learning: A Deep Latent Variable Model for Link Prediction' - AAAI
pytorch implementation of Structured Bayesian Pruning
PyTorch implementation of "Weight Uncertainties in Neural Networks" (Bayes-by-Backprop)
Bayesian Gradient Descent Algorithm Model for TensorFlow
My notes on different machine learning papers and related topics
A pytorch implementation of MCDO(Monte-Carlo Dropout methods)
Overview of Bayesian Deep Learning
Code for the ICASSP'19 submission "Modelling Sample Informativeness for Deep Affective Computing".
Master Thesis on Bayesian Convolutional Neural Network using Variational Inference
Active Learning with approximations of Bayesian Convolutional Neural Networks.
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".
Implementing Bayes by Backprop with PyTorch. Applied on time-series prediction.
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