Please log in to watch this conference skillscast.
Ensemble methods are a great way to improve the accuracy of deep learning models. However, current ensemble methods all treat the underlying base learner as a "black box".
Join Alan as he illustrates a new type of ensemble method which makes use of information about how the deep learning algorithm is structured, in order to improve training times, diversity generation, and ultimately accuracy.
Alan will also give examples of existing methods from his research at Birkbeck, and provide experimental results.
YOU MAY ALSO LIKE:
- Fast, scalable Deep Learning Ensembles (SkillsCast recorded in November 2016)
- Ceci n’est pas un canard – Machine Learning and Generative Adversarial Networks (Online Meetup on 6th August 2020)
- Web Scraping with GoLang (Online Meetup on 13th August 2020)
- Digital Discrimination: Cognitive Bias in Machine Learning (SkillsCast recorded in June 2020)
- Using A.I. to Help Humanitarian Causes (SkillsCast recorded in May 2020)
White-box Deep Learning Ensembles - Advanced
Alan Mosca is a part-time research student at Birkbeck, University of London. He also holds a full time position as Senior Data Engineer at Sendence. His research focus is on Deep Learning Ensembles and improvements to optimisation algorithms in Deep Learning. He was previously at Wadhwani Asset Management, Jane Street Capital, and several software companies.