For our first paper in intelligence, we'll be covering how to detect hate speech using machine learning algorithms. This is a super interesting topic, because hate speech is often hard to define and depends a lot on the context surrounding it. This review paper will cover current state-of-the-art approaches for this problem.
Our first non-review paper of the semester will be on using Deep RNNs to perform speech recognition tasks. This approach seeks to combine the advantages of deep neural networks wtih the "flexible use of long-range context that empowers RNNs". The abstract is rather lengthy, so I'll refrain from copying it here. Our weekly meeting on this paper will go over questions from the paper, strategies for reading more complex research papers, and how to identify strengths and weaknesses of journal articles.
This paper, published from work performed at Google Brain and Google Research, proposes a new network architecture for tackling machine translation problems (among other ML transduction problems). This new approach simplifies the classic approach to translation while also achieving better performance. Accompanying the paper is a Jupyter notebook created at Harvard to add annotations to the original article while also supplying code mentioned in the work. This paper is most similar to the kinds of articles you can expect to be reading when doing original research.