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Found 2 papers, 2 papers with code
Transformers are efficient hierarchical chemical graph learners
We demonstrate SubFormer on benchmarks for predicting molecular properties from chemical structures and show that it is competitive with state-of-the-art graph transformers at a fraction of the computational cost, with training times on the order of minutes on a consumer-grade graphics card.
Bilingual alignment transfers to multilingual alignment for unsupervised parallel text mining
To study this theory, we design unsupervised models trained on unpaired sentences and single-pair supervised models trained on bitexts, both based on the unsupervised language model XLM-R with its parameters frozen.
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