图 书 馆
使用基于规则的标签进行弱监督学习Using Rule‑Based Labels for Weak Supervised Learning |
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| 课程网址: | http://videolectures.net/kdd2018_goh_rule-based_learning/ |
| 主讲教师: | Garrett B. Goh |
| 开课单位: | 太平洋西北实验室 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry data is inherently small and fragmented. In this work, we develop an approach of using rule-based knowledge for training ChemNet, a transferable and generalizable deep neural network for chemical property prediction that learns in a weak-supervised manner from large unlabeled chemical databases. When coupled with transfer learning approaches to predict other smaller datasets for chemical properties that it was not originally trained on, we show that ChemNet’s accuracy outperforms contemporary DNN models that were trained using conventional supervised learning. Furthermore, we demonstrate that the ChemNet pre-training approach is equally effective on both CNN (Chemception) and RNN (SMILES2vec) models, indicating that this approach is network architecture agnostic and is effective across multiple data modalities. Our results indicate a pre-trained ChemNet that incorporates chemistry domain knowledge and enables the development of generalizable neural networks for more accurate prediction of novel chemical properties. |
| 关 键 词: | 大型数据集; 深度神经网络; 图像和语音识别任务; ChemNet预训练方法 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-01-24：cyh |
| 最后编审: | 2023-01-24：cyh |
| 阅读次数: | 29 |