Countering Mainstream Bias via End-to-End Adaptive Local Learning: Adaptive Weight | HackerNoon

To overcome the mainstream bias caused by learning difficulties that vary per user, an adaptive weight approach is essential for synchronizing user learning paces.

By connecting a user's learning status to their loss function, we can control the learning pace with weights based on current performance - addressing unsynchronized learning.

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