Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identicaldistribution assumpt...
Here we advocate an approach to learning hardware based on induction of finite state machines from temporal logic constraints. The method involves training on examples, constraint...
Marek A. Perkowski, Alan Mishchenko, Anatoli N. Ch...
We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and e...
— One of the central issues in Learning to Rank (L2R) for Information Retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures ...
Various data mining applications involve data objects of multiple types that are related to each other, which can be naturally formulated as a k-partite graph. However, the resear...
Bo Long, Xiaoyun Wu, Zhongfei (Mark) Zhang, Philip...