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Found 2 papers, 1 papers with code
Data Distributional Properties Drive Emergent In-Context Learning in Transformers
In further experiments, we found that naturalistic data distributions were only able to elicit in-context learning in transformers, and not in recurrent models.
Continual Learning using the SHDL Framework with Skewed Replay Distributions
Continuous learning has been a long-standing challenge for neural networks as the repeated acquisition of information from non-uniform data distributions generally lead to catastrophic forgetting or interference.
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