Optimizing Autoencoder Architectures

Explore how to optimize various autoencoder architectures tailored for rare event prediction. Understand design principles such as unit-norm constraints, activation choices, and specialized variants like sparse, denoising, LSTM, and convolutional autoencoders to enhance anomaly detection and feature extraction capabilities.

We'll cover the following...

Autoencoder constructions
Sparse autoencoders
LSTM autoencoders
Convolutional autoencoders

As we’ve seen throughout this lesson, a variety of architectures serve distinct purposes, from dimensionality reduction to denoising. Here’s a concise introduction to some key rules of thumb for constructing effective autoencoders.

Autoencoder constructions

Add unit-norm constraint on the weights. This prevents ill-conditioning of the model.
Add a linearly activated dense layer at the end of the encoder and decoder for calibration in most autoencoders.
The activation on the decoder output layer should be based on the range of the input. For example, linear if the input $x$ is in $(−∞, ∞)$ ...

1.Getting Started

2.Rare Event Prediction

3.Multi-Layer Perceptrons (MLPs)

4.Long Short-Term Memory (LSTM) Networks

5.Convolutional Neural Networks (CNNs)

6. Autoencoders

7.Conclusion

Optimizing Autoencoder Architectures

Autoencoder constructions