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Learning Gaussian mixture models (GMMs) using the Expectation-Maximization (EM) algorithm and its gradient-based variants is a fundamental problem in machine learning.
Time series forecasting remains a critical challenge across numerous domains. Despite significant advancements, existing approaches struggle with complex phenomena such as regime shifts, cross-domain knowledge transfer, and multimodal data integration.
It would be useful if we had the ability to modify a model’s beliefs. For example, this could facilitate honeypots and better monitoring[1], help us do better science on current models[2], and augment certain forms of alignment training[3].
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