Deep Learning


Track 1

15.35 to 16.15

How to train your robot (with Deep Reinforcement Learning)

Lucas García - MathWorks

Track 3

11.45 to 12.25

The case for a common Metadata Layer for Machine Learning

Jörg Schad - ArangoDB

16.20 to 17.00

Natural Language Generation: Explaining the unexplainable AI?

Alberto Bugarín - University of Santiago de Compostela

Track 4

13.20 to 14.00

IoT predictive maintenance for airplanes, bringing deep learning to the edge

Rodrigo Cabello - Plain Concepts

Daniela Solís - Plain Concepts

Track 5

11.00 to 11.40

Solving Natural Language problems with scarce data

Álvaro Barbero Jiménez - IIC

12.35 to 13.15

Context-Based Interpretability for Visual Attention using AI

Javier Martínez Cebrián - BBVA Next Technologies

Miguel Ángel Fernández Torres - Carlos III University


Track 2

10.55 to 11.35

Using Neo4j and Machine Learning to Create a Decision Engine

Timothy Ward - CluedIn

Track 3

12.25 to 13.05

Fighting Online Harassment with State-of-the-art Artificial Intelligence

Raúl Arrabales - Serendeepia Research

Jorge Muñoz - Serendeepia Research

13.10 to 13.50

AI and Medicine: From medical text, medical imaging… to genomics

Aurelia Bustos Moreno - Medbravo

16.10 to 16.50

End-to-End ML pipelines with Beam, Flink, TensorFlow, and Hopsworks

Theofilos Kakantousis - Logical Clocks

Track 4

12.25 to 13.05

Jupyter Notebooks on GCP (Development Best Practices/Tooling)

Viacheslav Kovalevskyi - Google

Mike Cheng - Google

13.10 to 13.50

Spotting Voice Keywords and Beyond – Harnessing Audio Data in Deep Learning

Gabriele Bunkheila - MathWorks

13.55 to 14.35

Distributed Deep Learning with Keras/TensorFlow on Spark: yes you can!

Guglielmo Iozzia - MSD

Track 5

14.40 to 15.20

Bayesian Voice Emotion Detection Applied to Robotics: Adding Uncertainty

Rubén Martínez Sánchez - Datahack