Sebastian Ruder is a 3rd year PhD Student in Natural Language Processing and Deep Learning at the Insight Research Centre for Data Analytics and a research scientist at Dublin-based text analytics startup AYLIEN. He is interested in transfer and multi-task learning for NLP and democratising machine learning and AI. He has studied Computational Linguistics at the University of Heidelberg, Germany and at Trinity College, Dublin. During his studies, he has worked with Microsoft, IBM's Extreme Blue, Google Summer of Code, and SAP, among others.
-
Sebastian Ruder, Barbara Plank (2017). Learning to select data for transfer learning with Bayesian Optimization. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 372–382, Copenhagen, Denmark. Code, poster
-
Sebastian Ruder, Parsa Ghaffari, John G. Breslin (2016). A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 999–1005, Austin, Texas, US. Poster
-
Sebastian Ruder (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
The link of his blog is here. He has blogged about Machine Learning, Deep Learning, and NLP.
He has written the newsletter for NLP lately. You can subscribe from this link. The newsletter will show the newest slides, resources, blog posts, paper, codes, datasets, conference, and even industry insights about NLP. I've subscribed it and it's really helpful for learning new things about NLP.