图 书 馆开课单位--德克萨斯农工大学
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1Towards Explanation of DNN‑based Prediction with Guided Feature Inversion[用引导特征反演解释基于DNN的预测]
Mengnan Du(德克萨斯农工大学) While deep neural networks (DNN) have become an effective computational tool, the prediction results are often criticized by the lack of interpretabil...
热度：18
Mengnan Du(德克萨斯农工大学) While deep neural networks (DNN) have become an effective computational tool, the prediction results are often criticized by the lack of interpretabil...
热度：18
2Adversarial Detection with Model Interpretation[基于模型解释的对抗检测]
Ninghao Liu(德克萨斯农工大学) Machine learning (ML) systems have been increasingly applied in web security applications such as spammer detection, malware detection and fraud detec...
热度：23
Ninghao Liu(德克萨斯农工大学) Machine learning (ML) systems have been increasingly applied in web security applications such as spammer detection, malware detection and fraud detec...
热度：23
3A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation[一个双故事模型：改进长尾项目推荐的双迁移学习框架]
Yin Zhang(德克萨斯农工大学) A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation
热度：40
Yin Zhang(德克萨斯农工大学) A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation
热度：40
4Sampling for Big Data[大数据采样]
Nick Duffield;Graham Cormode(德克萨斯农工大学) One response to the proliferation of large datasets has been to develop ingenious ways to throw resources at the problem, using massive fault tolerant...
热度：36
Nick Duffield;Graham Cormode(德克萨斯农工大学) One response to the proliferation of large datasets has been to develop ingenious ways to throw resources at the problem, using massive fault tolerant...
热度：36
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