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The London Machine Learning Meetup is the largest machine learning community in Europe. Previous speakers include Juergen Schmidhuber, Yoshua Bengio and Andrej Karpathy.

Sponsors: Evolution AI—Intelligent data extraction from corporate and financial documents. (http://evolution.ai)

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Upcoming events (1)

David Ha | Collective Intelligence for Deep Learning: Recent Developments

Link visible for attendees

Virtual London Machine Learning Meetup -[masked] @ 15:00 (BST)

Agenda:

  • 14:55: Virtual doors open
  • 15:00: Talk
  • 15:40: Q&A session
  • 16:00: Close

Sponsor: Evolution AI - Intelligent data extraction from corporate and financial documents.

Title: Collective Intelligence for Deep Learning: A Survey of Recent Developments

Speaker: David Ha, Research Scientist at Google Brain

Papers:

Abstract: In the past decade, we have witnessed the rise of deep learning to dominate the field of artificial intelligence. Advances in artificial neural networks alongside corresponding advances in hardware accelerators with large memory capacity, together with the availability of large datasets enabled practitioners to train and deploy sophisticated neural network models that achieve state-of-the-art performance on tasks across several fields spanning computer vision, natural language processing, and reinforcement learning. However, as these neural networks become bigger, more complex, and more widely used, fundamental problems with current deep learning models become more apparent.

State-of-the-art deep learning models are known to suffer from issues that range from poor robustness, inability to adapt to novel task settings, to requiring rigid and inflexible configuration assumptions. Collective behavior, commonly observed in nature, tends to produce systems that are robust, adaptable, and have less rigid assumptions about the environment configuration. Collective intelligence, as a field, studies the group intelligence that emerges from the interactions of many individuals. Within this field, ideas such as self-organization, emergent behavior, swarm optimization, and cellular automata were developed to model and explain complex systems. It is therefore natural to see these ideas incorporated into newer deep learning methods.

In this review, we will provide a historical context of neural network research's involvement with complex systems, and highlight several active areas in modern deep learning research that incorporate the principles of collective intelligence to advance its current capabilities. We hope this review can serve as a bridge between the complex systems and deep learning communities.

Bio: David is a Research Scientist at Google, working in the Brain team in Japan. His research interests include complex systems, self-organization, and creative applications of machine learning. He obtained undergraduate and graduate degrees in Engineering Science and Applied Math from the University of Toronto and the University of Tokyo. You can follow him on Twitter - @hardmaru

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