Google and Duke University’s New Machine Learning Breakthrough Unveils Advanced Optimization by Linear Transformers

The advent of transformer architectures has marked a significant milestone, particularly in their application to in-context learning. These models can make predictions based solely on the information presented within the input sequence without explicit parameter updates. This ability to adapt and learn from the input context has been pivotal in pushing the boundaries of achievable […]

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