The relative rankings of continual learning methods are not preserved across different fine-tuning regimes defined by trainable parameter depth.
Critical learning periods in deep neural networks
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DBLP is a training-phase-aware bounded-loss transport protocol that reduces end-to-end distributed ML training time by 24.4% on average (up to 33.9%) and achieves up to 5.88x communication speedup during microbursts while maintaining comparable test accuracy.
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Fine-Tuning Regimes Define Distinct Continual Learning Problems
The relative rankings of continual learning methods are not preserved across different fine-tuning regimes defined by trainable parameter depth.
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DBLP: Phase-Aware Bounded-Loss Transport for Burst-Resilient Distributed ML Training
DBLP is a training-phase-aware bounded-loss transport protocol that reduces end-to-end distributed ML training time by 24.4% on average (up to 33.9%) and achieves up to 5.88x communication speedup during microbursts while maintaining comparable test accuracy.