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Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer
The Mixture of Experts (MoE) has emerged as a highly successful technique in deep learning, based on the principle of divide-and-conquer to maximize model capacity without significant additional computational cost.
BLISS: Robust Sequence-to-Sequence Learning via Self-Supervised Input Representation
Data augmentations (DA) are the cores to achieving robust sequence-to-sequence learning on various natural language processing (NLP) tasks.
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