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Found 4 papers, 1 papers with code
KroneckerBERT: Significant Compression of Pre-trained Language Models Through Kronecker Decomposition and Knowledge Distillation
We push the limits of state-of-the-art Transformer-based pre-trained language model compression using Kronecker decomposition.
KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation
We present our KroneckerBERT, a compressed version of the BERT_BASE model obtained using this framework.
Block Pruning For Faster Transformers
Pre-training has improved model accuracy for both classification and generation tasks at the cost of introducing much larger and slower models.
Fully Quantized Transformer for Machine Translation
State-of-the-art neural machine translation methods employ massive amounts of parameters.
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