News and group updates

• Michele Floris (CERN)
• Steven Randolph Schramm (Universite de Geneve (CH))
• Lorenzo Moneta (CERN)
• Paul Seyfert (Universita & INFN, Milano-Bicocca (IT))
• Sergei Gleyzer (University of Florida (US))

Room: 40/S2-C01 - Salle Curie

Internally-Parallelized Boosted Decision Trees

• Andrew Mathew Carnes (University of Florida (US))

Room: 40/S2-C01 - Salle Curie

Rapid development platforms for machine learning

• Andrew Lowe (Hungarian Academy of Sciences (HU))

Room: 40/S2-C01 - Salle Curie

Distributed Deep Learning using Apache Spark and Keras (see materials)

• Joeri Hermans (Maastricht University (NL))

Room: 40/S2-C01 - Salle Curie Data parallelism is an inherently different methodology of optimizing parameters. The general idea is to reduce the training time by having n workers optimizing a central model by processing n different shards (partitions) of the dataset in parallel. In this setting we distribute n model replicas over n processing nodes, i.e., every node (or process) holds one model replica. Then, the workers train their local replica using the assigned data shard. However, it is possible to coordinate the workers in such a way that, together, they will optimize a single objective during training and as a result, reduce the wall clock training time. There are several approaches to achieve this, and these will be discussed in greater detail in the materials below.

Parallelization in Machine Learning with Multiple Processes

• Omar Andres Zapata Mesa (University of Antioquia & Metropolitan Institute of Technology)
• Gerardo gutierrez (ITM)

Room: 40/S2-C01 - Salle Curie

Minutes Room: 40/S2-C01 - Salle Curie