WebAug 20, 2024 · In recent years, machine learning has achieved great success in research and has been applied in many fields, especially after the emergence of powerful computing devices (such as GPU and distributed platform), standard and practical large data sets (such as ImageNet-1000 []) and advanced model algorithms (such as convolutional … WebFew-shot Learning for Low-Data Drug Discovery. Implementations for the following machine learning models: Random Forests; Graph Convolutional Network; Siamese Networks; …
Few-shot Learning for Low-Data Drug Discovery - GitHub
WebJan 1, 2024 · Ravi S, Larochelle H. Optimization As A Model For Few-Shot Learning. In: International Conference on Learning Representations. 2024, pp. 1–11. Google Scholar. 7. Li Fei-Fei, R Fergus, P. Perona. ... Low Data Drug Discovery with One-Shot Learning. ACS Cent Sci, 3 (2024), pp. 283-293. WebFew-shot learning part I: Meta-learning for few-shot learning ; Problem statement: Few-shot learning; Optimization-based methods (e.g., MAML) Metric-based methods (e.g., Siamese, MatchingNet, ProtoNet) Applications: Drug discovery and cellular response prediction ; Few-shot learning part II: Integrating side information matthew gajda
Few-Shot Learning for Low-Data Drug Discovery - PubMed
WebJun 12, 2024 · Abstract. Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to tackle this problem. Using prior knowledge, FSL can rapidly generalize to new tasks containing only a few samples with supervised information. WebMar 12, 2024 · However, drug discovery projects are often characterized by limited labeled data, hindering the applications of deep learning in this setting. Meanwhile advances in … WebNov 10, 2016 · In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug … matthew gailey