Semi-Supervised Learning by Augmented Distribution Alignment

In this work, we propose a simple yet effective semi-supervised learning approach called Augmented Distribution Alignment. We reveal that an essential sampling bias exists in semi-supervised learning due to the limited number of labeled samples, which often leads to a considerable empirical distribution mismatch between labeled data and unlabeled data. To this end, we propose to align the empirical distributions of labeled and unlabeled data to alleviate the bias. On one hand, we adopt an adversarial training strategy to minimize the distribution distance between labeled and unlabeled data as inspired by domain adaptation works. On the other hand, to deal with the small sample size issue of labeled data, we also propose a simple interpolation strategy to generate pseudo training samples. Those two strategies can be easily implemented into existing deep neural networks. We demonstrate the effectiveness of our proposed approach on the benchmark SVHN and CIFAR10 datasets. Our code is available at \url{https://github.com/qinenergy/adanet}.Comment: To appear in ICCV 201

Knowledge Modeling for Developing Program Planning Agents

This paper describes a method of domain knowledge modeling for program planning and scheduling in intelligent e-Learning advising systems, focusing on the modeling and representation of precedence relations among course learning objects encoded in model curricular and the representation of domain experts’ knowledge using Petri nets formalism and a XML-based markup language. We developed a Web-based program model editor