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MSA Transformer.

, , , , , , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 8844-8856. PMLR, (2021)

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Language models enable zero-shot prediction of the effects of mutations on protein function., , , , , и . NeurIPS, стр. 29287-29303. (2021)Transformer protein language models are unsupervised structure learners., , , , и . ICLR, OpenReview.net, (2021)Quality and Relevance Metrics for Selection of Multimodal Pretraining Data., , , , , и . CVPR Workshops, стр. 4109-4116. Computer Vision Foundation / IEEE, (2020)GPU accelerated t-distributed stochastic neighbor embedding., , , и . J. Parallel Distributed Comput., (2019)T-SNE-CUDA: GPU-Accelerated T-SNE and its Applications to Modern Data., , , и . SBAC-PAD, стр. 330-338. IEEE, (2018)Training, Evaluating, and Understanding Evolutionary Models for Protein Sequences. University of California, Berkeley, USA, (2021)MSA Transformer., , , , , , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 8844-8856. PMLR, (2021)ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations., , , , , и . CoRR, (2019)End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman., , , , , , , , и . Bioinform., (января 2023)Evaluating Protein Transfer Learning with TAPE., , , , , , , и . NeurIPS, стр. 9686-9698. (2019)