这里我列举目前常见的feature learning领域的文献: [1] Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning, ICLR 2023* (Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning) Zeyuan Allen-Zhu, Yuanzhi Li [2] Feature purificatio...
其中,feature learning理论技术是一个发展迅速,潜力巨大的方向。我们给出feature learning theory一个相对宽泛的描述:在构建的数据分布基础上,一般会由task relevant和task irrelanvt成分组成,考察神经网络在具体优化算法下从数据中学习到task relevant和task irrelanvt成分的动态行为以及最终对泛化的影响。 这里有两个点...
Unsupervised feature learning and deep learning 是斯坦福大学机器学习大牛Andrew Y Ng. 近年来研究的主要领域,他在今年的一份工作Building high-level features using large scale unsupervised learning中就通过unsupervised learning解决了从only unlabeled data上建立高维feature detectors的问题。 ===第一部分:传统方法Pa...
Travel time prediction is both a challenging and interesting problem in ITS, because of the underlying traffic and events' hidden patterns. In this study, we propose a multi-step deep-learning-based algorithm for predicting travel time. Our algorithm starts with data pre-processing. Then, the ...
In general, as the time goes on, the models for representation learning become deeper and deeper, and more and more complex, while the development of neural networks is not so smooth as that of representation learning. However, in the era of deep learning, they gradually combine together for...
UFLDL(Unsupervised Feature Learning and Deep Learning)Tutorial 是由 Stanford 大学的Andrew Ng教授及其团队编写的一套教程,内容深入浅出,有很强的实用性, 学习起来,让人有种酣畅淋漓的感觉。邓侃博士于今年 2 月 20 日起,在新浪微博上召集志愿者对该教程进行翻译,并于 4 月 8 日全部完成,非常感谢所有参与者的...
In this chapter we look at a wide range of feature learning architectures and deep learning architectures, which incorporate a range of feature models and classification models. This chapter digs deeper into the background concepts of feature learning an
Use self-taught learning to obtain a(2) using w(1); Use Softmax Regression to train labled data (a(2), y) and optimize theta (the new w(2) in final network). The overall procedure is explained in topic 6.1. Notice that with fine-tuning (introduced in topic 6), we can also opt...
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space论文解读,程序员大本营,技术文章内容聚合第一站。
Recent works have begun to focus on comparing different machine learning methods in pharmaceutical research but there are still gaps in this area18. This work addresses one such gap, namely the relative performance of feature-based and deep learning model approaches. When compared to classification ...