This paper laid out the position of Machine Teaching, a universally accessible, easy to manage, and fast discipline proposed by Microsoft Research, and articulated the fundamental machine teaching principles.
In applying the adversarial training, this paper adopts distributed word representation, or word embedding, as the input, rather than the traditional one-hot representation. The reason lies in the fact that the higher dimensionality the input has, the more likely it is to be disturbed by noise.
In this paper, the author aims to design a smart way to handle rare/unknown words in the bilingual training data during machine translation.
This paper is the implementation of ‘encoder-decoder reconstructor framework’ for neural machine translation for the English-Japanese translation task.
Recently NASA announced that their Frontier Development Laboratory (FDL) would cooperate with Intel to apply deep learning for space exploration.
In this paper, we present a survey on language models, which mainly consists of count-based and continuous-space language models.
Recently, CNNs have been successfully used in estimating optical flow. Compared with traditional methods, these methods achieved a large improvement in quality. Here, we will give a brief review on the following papers.
InfiniteBoosting overcomes the disadvantages of both bagging and boosting, thus slightly improves the performance. Its most important aspect is its adaptability.
RACECAR is a Powerful Platform for Robotics Research and Teaching. It is one of the greatest university courses in this field.
The goal of this report is to inspire our readers in the field of distributed machine learning and invite more to join our discussion to exchange knowledge and contribute to our intellectual community.
