In the last part of this mini-series on forecasting with false nearest neighbors (FNN) loss, we replace the LSTM autoencoder from the previous post by a convolutional VAE, resulting in equivalent prediction performance but significantly lower training time. In addition, we find that FNN regularization is of great help when an underlying deterministic process is obscured by substantial noise.
Table of Contents Introduction to Autoencoders What Are Autoencoders? How Autoencoders Achieve High-Quality Reconstructions? Revisiting the Story Types of Autoencoder Vanilla Autoencoder Convolutional Autoencoder (CAE) Denoising Autoencoder Sparse Autoencoder Variational Autoencoder (VAE) Sequence-to-Sequence Autoencoder What Are the Applications of Autoencoders?…
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Machine learning advancements lead to new ways to train models, as well as deceive them. This article discusses ways to train and defend against attacks.
Use 3D to visualize matrix multiplication expressions, attention heads with real weights, and more.
This article discusses GPT-2 and BERT models, as well using knowledge distillation to create highly accurate models with fewer parameters than their teachers
Table of Contents CycleGAN: Unpaired Image-to-Image Translation (Part 3) Configuring Your Development Environment Need Help Configuring Your Development Environment? Project Structure Implementing CycleGAN Training Implementing Training Callback Implementing Data Pipeline and Model Training Perform Image-to-Image Translation Summary Citation Information CycleGAN:…
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