Deconstructing Neural Networks for Time Series Forecasting
Ablation study of neural network components for forecasting, finding gating and attention improve RNNs while recurrence …...
Neural-Networks
Auto-Encoding Variational Bayes (VAE Paper Summary)
Summary of Kingma & Welling's foundational VAE paper introducing the reparameterization trick and variational …...
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 …
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 …...
Modern PyTorch Techniques for VAEs: A Hands-On Tutorial
A comprehensive guide to implementing Variational Autoencoders (VAEs) in PyTorch. Covers the ELBO objective, …
HyperNEAT: Scaling Neuroevolution with Geometric Patterns
How HyperNEAT uses indirect encoding and geometric patterns to evolve large-scale neural networks with biological …
NEAT: Evolving Neural Network Topologies
Learn about NEAT's approach to evolving neural networks: automatic topology design, historical markings, and speciation …
Understanding Generative Adversarial Networks (GANs)
Learn about GANs with intuitive explanations and mathematical foundations. Learn how adversarial networks generate …
Word Embeddings in NLP: An Introduction
Learn about word embeddings in NLP: from basic one-hot encoding to contextual models like ELMo. Guide with examples.