[ATOM] Advanced Topics on Machine Learning discussion group

ATOM is meeting on Tuesday, 27th November, 6:30pm, at Galvanize!

Our discussion this month will be led by Rachael Tatman.

The Importance of Being Recurrent for Modeling Hierarchical Structure:
http://aclweb.org/anthology/D18-1503

You may, in fact, need more than attention. This paper is a comparison of the ability of recurrent and non-recurrent (i.e. transformer) neural network structures, focusing on their ability to model hierarchical relationships in natural language. The authors found that for both subject-object agreement and logical entailment, RNN's outperformed transformers. While there is limited theoretical support for these findings, the empirical results are compelling.

About ATOM:
Advanced Topics on Machine learning ( ATOM ) is a learning and discussion group for cutting-edge machine learning techniques in the real world. We work through winning Kaggle competition entries or real-world ML projects, learning from those who have successfully applied sophisticated data science pipelines to complex problems.

As a discussion group, we strongly encourage participation, so be sure to read up about the topic of conversation beforehand !

ATOM can be found on PuPPy’s Slack under the channel #atom, and on PuPPy’s Meetup.com events.

We're kindly hosted by Galvanize (https://www.galvanize.com). Thank you !