Low data drug discovery with one-shot learning, ACS Central Science, 2017. H. Altae-Tran, B....
(2)H. Altae-Tran, B. Ramsundar, A. S. Pappu, and V. Pande. 2017. Low data drug discovery with one-shot learning. ACS Central Science 3, 4 (2017), 283–293. (3)P. Bachman, A. Sordoni, and A. Trischler. 2017. Learning algorithms for active learning. In International Conference ...
Few-shot/low-shot learning is an ML approach for small datasets. Explore its use cases, methods, difference from zero-shot learning & Python implementation
drug response, highlighting critical roles forRB1andSMAD4in the response to CDK inhibition andRNF8andCHD4in the response to ATM inhibition. The few-shot learning framework provides a bridge from the many samples surveyed in high-throughput screens (n-of-many) to the distinctive contexts of ...
Tuning language models as training data generators for augmentation-enhanced few-shot learning 模型(35篇) 多任务学习 When does self-supervision improve few-shot learning? 标题:自监督学习在什么情况下可以改进小样本学习? 方法介绍:虽然自监督学习的收益可能随着更大的训练数据集而增加,但我们也观察到,当用于...
shot inverse design framework by designing multi-modal representations to enrich polymer information for predictions and creating a graph grammar distillation for chemical space restriction to improve the efficiency of multi-constrained polymer generation with reinforcement learning. Exampled with HDP-mimicking...
Low data drug discovery with one-shot learning, ACS Central Science, 2017. H. Altae-Tran, B. Ramsundar, A. S. Pappu, and V. Pande. paper Prototypical networks for few-shot learning, in NeurIPS, 2017. J. Snell, K. Swersky, and R. S. Zemel. paper Attentive recurrent comparators, ...
The trend:The quest for dupes (cheaper alternatives to expensive products) may be an older trend on TikTok, but it’s a lasting one. “#Dupes” has 3.5 billion views on TikTok. The takeaway:Gen Z wants quality products at lower prices. Any way brands can communicate that they are delive...
ProteinGym is originally used for evaluating the zero-shot performance of PLMs, and we turn it into a few-shot learning benchmark as follows. For each dataset in the benchmark, we first randomly select 20 single-site mutants as an initial training ...
With pseudobulk you might have a hard time finding Differentially Expressed Genes, DEG, for GSEA as their p-adjusted value will be rather low. Perhaps you can use single cell methods to obtain the DEG list. You can do that using the function FindAllMarkers or FindMarkers in Seurat. ...