Using phidelta diagrams to discover relevant patterns in multilayer perceptrons

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Understanding the internal workings of multilayer perceptrons (MLPs) post-training is crucial for AI research.
• MLPs are powerful feature encoders, with hidden layers potentially hosting new input features.

Purpose of the Study:

• To experimentally investigate emergent patterns within MLPs during training.
• To explore the impact of network depth on problem difficulty and generalization.
• To introduce and evaluate a novel training strategy as an alternative to backpropagation.

Main Methods:

• Utilized [Formula: see text] diagrams for visualizing emergent patterns in MLPs.
• Inspected hidden layers to analyze feature encoding capabilities.
• Implemented and tested a generic deep learning-inspired training strategy.
• Conducted comparative experiments against the classical backpropagation algorithm.

Main Results:

• Identified distinct patterns emerging during MLP training, correlating with problem complexity.
• Demonstrated that deeper neural network architectures can simplify initially difficult problems.
• The proposed generic training strategy showed comparable performance to backpropagation in comparative settings.

Conclusions:

• Emergent patterns in MLPs offer insights into problem difficulty and network behavior.
• Increasing network depth enhances generalization ability.
• The novel training strategy presents a viable alternative to traditional backpropagation for MLPs.