Skip to content

ICML 2015


ICML Lille


The International Conference on Machine Learning is one of the most important annual event in the world of machine learning. The place is where the most renowned researchers in the field gather to present and share their -often diverging – vision and directions for the future. As such, the event is sponsored by most of the biggest companies in IT such as Google, Baidu and Facebook. It also attracts numerous smaller companies with particular interest in big data in its wake.

Deep Learning, everywhere

It is no secret that the star this year is deep learning. The field broke through a few time ago, when computers began beating humans not only in recognizing images, but also generating them. With daily sessions on deep learning, most of the cutting edge research on deep learning were presented.

Deep learning did not however stole all the show, and other tracks such as time series, natural language processing, and optimization were also of great focus.

EURA NOVA presented a poster at the “Greed is great” workshop on distributing the Frank-Wolfe algorithm using Stale Synchronous Parallel iterations.

Poster : "Distributed Frank-Wolfe under Pipelined Stale Synchronous Parallelism"
Poster : “Distributed Frank-Wolfe under Pipelined Stale Synchronous Parallelism”

Final thought

What particularly struck me is the gap between research in machine learning and its application in real-life use cases. Even though the field is buzzingly active, its application on industrial problems are nowhere as advanced. This is the case in medical imaging for instance, where visual recognition still does not benefit from the latest advances.

More than ever, there is a need for industrial partners who are able to bridge the gap between active research and the industry. These partners should be able to keep track of the advances in research while advising companies on the directions for innovations.

Releated Posts

The Building Blocks of a Responsible AI Practice: An Outlook on the Current Landscape

Responsible AI comes with the challenge of implementation. This survey aims to bridge the gap between principles and practice through a study of different approaches taken in the literature and the proposition of a foundational framework.
Read More

TS-Relax : Interprétation des représentations apprises pour les séries temporelles

Les modèles d’apprentissage de représentations sont de plus en plus utilisés, mais des modèles d’IA explicables et de confiance sont nécessaires. Ce travail présente l’adaptation aux séries temporelles d’une méthode d’interprétation de représentation initialement conçue pour les images.
Read More