Learnable Modular NNet Architecture

A Learnable Modular NNet Architecture is a NNet architecture that consists of multiple learnable modules or components that can be dynamically combined and adapted based on the task and data.

• Context:
  • It can allow the network to decompose complex tasks into simpler subtasks that can be handled by specialized modules.
  • It can enable the network to reuse and combine learned modules in different ways to solve new tasks or adapt to new domains.
  • It can potentially lead to more interpretable and compositional models that can be analyzed and manipulated at a higher level of abstraction.
  • It can be seen as a way to achieve Learnable Expressiveness by allowing the network to dynamically adjust its architecture and computation based on the input and the task.
  • ...
• Example(s):
  • Modular Neural Networks, where different modules are trained to handle different subtasks or data modalities and are dynamically combined based on the task requirements.
  • Neural Module Networks, where a set of neural modules are composed based on a structured query or program to answer questions or perform reasoning tasks.
  • Slot Attention models, where a set of learnable slots are used to represent and manipulate different objects or entities in a scene.
  • ...
• Counter-Example(s):
  • Monolithic Neural Networks, where all the layers and parameters are trained jointly without any explicit modular structure.
  • Fixed Computational Graphs, where the flow of computation and the connections between different parts of the network are predefined and not learnable.
  • ...
• See: Learnable Expressiveness, Neural Network Architecture, Compositional Models, Meta-Learning, Neural Architecture Search (NAS), Learnable Interaction Mechanism.

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