Auto-Encoding Variational Bayes (VAE Paper Summary)
Summary of Kingma & Welling's foundational VAE paper introducing the reparameterization trick and variational …...
Generative-Models
Importance Weighted Autoencoders (IWAE Paper Summary)
Summary of Burda, Grosse & Salakhutdinov's ICLR 2016 paper introducing Importance Weighted Autoencoders for tighter …...
Importance Weighted Autoencoders: Beyond the Standard VAE
Learn the crucial difference between multi-sample VAEs and Importance Weighted Autoencoders (IWAEs). Explore how …
SELFIES: The Original Paper (Krenn et al. 2020)
A summary of the foundational 2020 paper that introduced SELFIES - the 100% robust molecular string representation …
Converting SELFIES Strings to 2D Molecular Images
Learn how to visualize SELFIES molecular representations and explore their unique advantages through random sampling, …
DenoiseVAE: Learning Molecule-Adaptive Noise Distributions for Denoising-based 3D Molecular Pre-training
Liu et al.'s ICLR 2025 paper introducing DenoiseVAE, which learns adaptive, atom-specific noise for better molecular …...
A Contrastive Learning Approach for Training Variational Autoencoder Priors
Dai et al.'s NeurIPS 2021 paper introducing Noise Contrastive Priors (NCPs) to address VAE's 'prior hole' problem with …...
Invalid SMILES are Beneficial Rather than Detrimental to Chemical Language Models
Skinnider's 2024 Nature Machine Intelligence paper demonstrates that the ability to generate invalid SMILES is actually …...
Modern PyTorch Techniques for VAEs: A Hands-On Tutorial
A comprehensive guide to implementing Variational Autoencoders (VAEs) in PyTorch. Covers the ELBO objective, …
GAN Objective Functions: A Comprehensive Guide
Complete guide to GAN objective functions including WGAN, LSGAN, Fisher GAN, and more. Understand which loss function to …
Understanding Generative Adversarial Networks (GANs)
Learn about GANs with intuitive explanations and mathematical foundations. Learn how adversarial networks generate …