End-to-End Q&A Matching; A Glance at Q-Learning


Thanks to New Yorker for co-sponsoring the event!

Talk 1: End-to-End Question-Answer Matching Network (Bhanu Pratap, 45 min)

Abstract: Question-Answer matching task is one of the most important tasks in Natural Language Processing(NLP). It has many real world applications in domains like customer support, human resources, etc. Most of the Question-Answer matching systems depend on explicit feature-engineering and are thus domain-specific. An end-to-end system with no or little feature-engineering is therefore highly desirable. Here, we present one such system implemented using techniques of Deep Learning. We show that such a system requires no featuring engineering and yet is powerful enough to attain competitive performance when compared to other state-of-the-art models.

Bio: Bhanu is working as a Senior Data Scientist @ Talla Inc., Cambridge MA. He studied Informatik at University of Bonn, where he worked on Neural Embeddings and Recursive Neural Networks for NLP. There after, he has been actively involved in implementing solutions for various real-world problems using Machine Learning and NLP. He can be contacted at: [masked].

Talk 2: Reinforcement Learning in Gaming and Energy (Adam Green, 30 min)

Bio: Adam is an energy engineer with a mission to decarbonise the supply of heat and electricity. Machine learning can help to achieve this mission. Adam has historically used linear programming to optimize energy systems and is now excited about the potential of reinforcement learning

Abstract: ‘A Glance at Q-Learning' is an introductory talk covering reinforcement learning concepts and the Q-Learning algorithm. The talk also takes a closer look at the groundbreaking 2013 DeepMind Atari paper as well as a Python project to model & control energy systems.