In a recent post, we showed how an LSTM autoencoder, regularized by false nearest neighbors (FNN) loss, can be used to reconstruct the attractor of a nonlinear, chaotic dynamical system. Here, we explore how that same technique assists in prediction. Matched up with a comparable, capacity-wise, “vanilla LSTM”, FNN-LSTM improves performance on a set of very different, real-world datasets, especially for the initial steps in a multi-step forecast.
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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Use 3D to visualize matrix multiplication expressions, attention heads with real weights, and more.
With the rapid advance in NLP models we have outpaced out ability to measure just how good they are at human level language tasks. We need better NLP datasets now more than ever to both evaluate how good these models are and to be able to tweak them for out own business domains.
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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