i-machine-think/awesome-compositionality
A list of resources dedicated to compositionality
This resource is a curated list of academic papers and datasets focused on how AI systems, particularly neural networks, build and understand complex concepts from simpler parts. It gathers research on how these models handle tasks like language modeling or image recognition when they need to generalize to new combinations of familiar elements. This is for researchers and practitioners working on the foundational principles of artificial intelligence, particularly those interested in areas like cognitive science, psycholinguistics, and machine learning theory.
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Use this if you are an AI researcher or cognitive scientist looking for a structured collection of papers and datasets related to 'compositionality' in machine learning models.
Not ideal if you are looking for code libraries, practical applications, or tutorials on how to implement compositional AI models.
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Feb 21, 2019
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