NanboLi/FACTS
[ICLR 2025] Implementation of "FACTS: A Factored State-Space Framework For World Modelling"
FACTS helps machine learning researchers efficiently build and experiment with state-space models, which are used for 'world modeling' in AI. It takes in structured input data, often time-series or sequential data, and outputs processed representations that capture underlying patterns and dynamics. This tool is designed for AI researchers and practitioners who are developing advanced predictive models and complex AI systems.
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Use this if you are a machine learning researcher or developer working on advanced AI models and need a flexible, high-performance PyTorch implementation of factored state-space models for tasks like time series forecasting.
Not ideal if you are looking for a plug-and-play solution for a business problem without deep knowledge of machine learning model development and PyTorch.
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Python
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MIT
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Last pushed
Jun 02, 2025
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