Tag
This paper introduces low interaction rank as a unified theoretical framework for multiplicative dual-encoder networks, covering approximation, sample complexity, normalization, and identifiability, with experiments on operator learning and CLIP models.
This paper presents ConRetroBert, a dual encoder framework for template-based single-step retrosynthesis that uses contrastive pretraining and listwise ranking to improve template prediction accuracy, achieving up to 75.4% top-1 accuracy on the USPTO-50k benchmark while maintaining interpretability.