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0111 · Dec 201919922001200920172026
3 results for seq-to-seq

This paper examines data transfer methods to improve off-the-shelf Transformer models for retrosynthesis.

problem Improving prediction models for retrosynthesis using small datasets.
method Systematic examination of data transfer approaches on end-to-end generative models.
result Pre-training plus fine-tuning boosts the accuracy of a Transformer baseline, achieving state-of-the-art results.

Although deep neural networks generally have fixed network structures, the concept of dynamic mechanism has drawn more and more attention in recent years. Attention mechanisms compute input-dependent dynamic attention weights for aggregating a sequence of hidden states. Dynamic network configuration in convolutional ne…

2019-12-01abs ↗pdf ↗