ConvNets for understanding documents & RL for electricity systems

Thanks to SAP for hosting and sponsoring this Meetup!

Talk 1: Chargrid: Towards Understanding 2D Documents
Speaker: Dr. Christian Reisswig, SAP Deep Learning Center of Excellence
Abstract: We introduce a novel type of text representation that preserves the 2D layout of a document. It encodes document pages as a two-dimensional grid of characters. Based on this novel representation, we present a generic document understanding pipeline for structured documents. This pipeline makes use of a fully convolutional encoder-decoder network that predicts a segmentation mask and bounding boxes. We demonstrate its capabilities on an information extraction task from invoices and show that it significantly outperforms approaches based on sequential text or document images.
Bio: Christian is a theoretical astrophysicist who left academia and hit the deep learning highway three years ago. After spending some time in industry working on deep learning algorithms for autonomous driving and medical imaging, he is now a senior data scientist at SAP where he is building machine learning prototypes for problems in natural language processing and computer vision.

Talk 2: Reinforcement learning for electricity systems
Speaker: Adam Green
Abstract: This talk reviews two years of work on energy_py - a reinforcement learning (RL) for energy systems (https://github.com/ADGEfficiency/energy_py). We will look at lessons learned designing the library, experience using the library with Open AI gym and energy_py environments. Also covered is the use of synthetic data generation in energy_py environments.
Bio: Adam is an energy engineer who started his transition into data science two years ago. He now works at Tempus Energy, using supervised and reinforcement learning to control flexible electrical load that supports variable renewable energy.

Lightning talks:
-Michael Arthur Bucko on new computer vision meetup