Optimization

Summary of Kingma & Welling's foundational VAE paper introducing the reparameterization trick and variational …...

Summary of Burda, Grosse & Salakhutdinov's ICLR 2016 paper introducing Importance Weighted Autoencoders for tighter …...

Learn the crucial difference between multi-sample VAEs and Importance Weighted Autoencoders (IWAEs). Explore how …

The Müller-Brown potential: a classic two-dimensional analytical benchmark for testing optimization algorithms, reaction …...

Guide to implementing the Müller-Brown potential in PyTorch, comparing analytical vs automatic differentiation with …

Langevin dynamics simulation showing particle motion in the deep reactant minimum (Basin MA) of the Müller-Brown …

Langevin dynamics simulation showing particle motion in the product minimum (Basin MB) of the Müller-Brown potential …

PyTorch implementation of the Müller-Brown potential with performance optimizations, MD simulations, and analytical vs …...

Extended Langevin dynamics simulation showing particle transitions between different basins of the Müller-Brown …

A comprehensive guide to implementing Variational Autoencoders (VAEs) in PyTorch. Covers the ELBO objective, …

Analytical model of Word2Vec and GloVe statistics. First analytical solution to Word2Vec's softmax skip-gram with bias …...

Investigation into EigenNoise, a data-free initialization scheme for word vectors that approaches pre-trained model …...