Revisiting the problem of learning long-term dependencies in recurrent neural networks

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Recurrent Neural Networks (RNNs) are crucial for sequential data.
• The vanishing and exploding gradient (VEG) problem is widely believed to hinder RNNs' ability to learn long-term dependencies.
• This belief has driven significant research in RNN advancements.

Purpose of the Study:

• To challenge the established belief that VEG prevents RNNs from learning long-term dependencies.
• To investigate the actual learning dynamics of RNNs beyond the VEG and hyperbolic attractor explanations.
• To identify key factors influencing RNNs' capacity for long-term dependency learning.

Main Methods:

• Trained over 40,000 RNNs using a large factorial experiment.
• Re-examined the concept of latching behavior in RNNs.
• Analyzed the impact of hyper-parameters, specifically learning rate, on model performance.

Main Results:

• Provided evidence contradicting the long-held belief that VEG inherently prevents long-term dependency learning in RNNs.
• Demonstrated that RNNs can learn dynamics not fully explained by hyperbolic attractors.
• Identified learning rate as a critical hyper-parameter influencing the success of long-term dependency learning.

Conclusions:

• The VEG problem does not necessarily preclude RNNs from learning long-term dependencies.
• RNNs exhibit diverse learned dynamics beyond hyperbolic attractors.
• Optimizing hyper-parameters, particularly learning rate, is essential for enabling RNNs to effectively learn long-term dependencies.