Author: Andreas Antoniades (PhD student)

The 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), co-organised by the Department’s Prof Yaochu Jin as the General Co-Chair, was held in Athens, Greece, a city full of arts, philosophy and historical attractions, between the 6th and 9th of December. Bringing together more than 20 different symposiums relevant to computational intelligence, SSCI was a great opportunity cross-fertilisation and collaboration.

The conference and more specifically the symposium on Computational Intelligence for Big Data (CIBD), was a great opportunity for me to present and promote my work. Throughout the course of the conference, I had the opportunity to discover new applications in the topic of computational intelligence and attend numerous keynote presentations from pioneers in the field.

My paper titled “An improved mini-batching technique: Sample-and-Learn” revolved around Big Data and it’s effects on machine learning and data analytics. The days where data was scarce and difficult to find are long gone. Now thanks to the Internet of Things, unprecedented volumes of data are created daily. Our algorithms need to adapt in order to cater for large scale analytics. It is in this spirit that a reservoir sampling algorithm is proposed to alleviate the computational load of models trained with big data. The resulting algorithm was proven to decrease training times of neural networks while also providing equivalent or better accuracy than state-of-the-art.