[HKML] Hong Kong Machine Learning Meetup Season 1 Episode 12 (Season Finale)


  • Wednesday, July 17, 2019 from 7:00 PM to 9:00 PM


Amazon AWS Hong Kong hosted the meetup at their Causeway Bay office (Tower 535, pop-up loft 26/F). Many thanks to them! And especially, thanks to Sze Lok Chan.


Alex Lau on WGAN-GP

Generative Adversarial Network (GAN) is notorious for being hard to train. Wasserstein GAN with Gradient Penalty, as one of its variants, comes handy into the rescue. The presentation discussed a number of major problems one commonly faces when training a GAN: non-smooth cost function, vanishing gradients, mode collapse, lack of indicative metrics for training performance (on this issue, I learned about the Geometry Score: A Method For Comparing Generative Adversarial Networks, at ICML 2018, based on topological features; I did not have time yet to experiment with it to see if it really helps in practice). After the long and precise introduction, Alex explained how WGAN-GP can tackle some of these problems. A few applications of WGAN-GP were highlighted.

His slides are there.

Alex is looking for collaboration on fun and non-commercial projects he has in mind. Reach out to him for further information if you are interested.

Eric Greene - Forecasting Time Series with AWS

Slides will come soon.

Gautier Marti - Takeaways from ICML 2019, Long Beach, California

I presented very briefly the content of the following slides. I tried to identify some niche trends emerging in the ML community. No focus on deep (reinforcement) learning at all, which was a big part of ICML though.