Learnable Modular NNet Architecture
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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.
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- 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.
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- 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.
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- See: Learnable Expressiveness, Neural Network Architecture, Compositional Models, Meta-Learning, Neural Architecture Search (NAS), Learnable Interaction Mechanism.