A Taxonomy for Neural Memory Networks.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• The proliferation of neural memory networks necessitates systematic analysis of their memory structures.
• Existing dynamic neural network models (e.g., RNN, LSTM) lack a unified framework for memory comparison.
• Understanding memory usage is crucial for optimizing neural network performance.

Purpose of the Study:

• To develop a framework for organizing and comparing memory structures in dynamic neural networks.
• To propose and validate a taxonomy for classifying neural network architectures and learning tasks.
• To provide guidance for practitioners in selecting appropriate neural network architectures based on task requirements.

Main Methods:

• A novel framework for memory organization was created.
• Four dynamic neural network models (vanilla recurrent neural network, long short-term memory, neural stack, neural RAM) were analyzed and compared.
• A unifying architecture was used to develop and prove a taxonomy classifying networks and tasks into four classes.
• Synthetic and real-world tasks (signal processing, natural language processing) were used for evaluation.

Main Results:

• A systematic framework and taxonomy for dynamic neural networks were established.
• A one-to-one mapping between network architectures and task classes was created.
• The analysis provided insights into the memory usage of different neural network models.
• The proposed methodology demonstrated effectiveness in realistic application settings.

Conclusions:

• The developed framework and taxonomy offer a systematic approach to understanding and comparing neural memory networks.
• Practitioners can leverage the taxonomy to make informed decisions when selecting neural network architectures for diverse learning tasks.
• This work contributes to demystifying the 'black box' of dynamic neural networks from a memory perspective.