Machine Learning Theory

Channel: #ml-theory

Co-leads: 

• Anier  - @Anier Velasco Sotomayor:) on Discord, @aniervs on Twitter

• Martina - @Martina Vilas on Discord, @martinagvilas on Twitter

Goal

• In my opinion, ML Theory is all about making Machine Learning a more rigorous science, and less of a set of fancy techniques that turned out to work nicely. To give a few examples, ML Theory tries to answer questions like: why is Adam the de facto optimizer in Neural Networks? what's the underlying theory behind all modern architectures? can we predict the behaviour of a Neural Network once it's trained? can we do it without even training it?

• The learning group aims to provide a domain/task/modality independent approach to studying Machine Learning theory.

• The topic aims to cover different levels (introductory to advanced) of ML theory, in order to make topics in the field more accessible and also encourage a deeper exploration.

• Create a space for discussions with an aim also to bridge our gap of understanding related to empirical phenomenon and theoretical predictions.

Logistics:

• Bi-weekly sessions, ideally a topic per meeting,  but sometimes a topic takes a bunch of meetings. The topics are decided by votes in the discord chat.

• Anyone can present!

• Pre-requisites in terms of reading material will be provided at least a week in advance, basic introduction will also be provided by the user.

• Around 20-25 minutes talk, 5-10 minutes for questions (2x per meeting).

• Discussion can take place offline on Discord after!
  
  
  

C4AI ML Theory Resources | Paper List 

Occurrences:  Bi-weekly, Thursdays 9a PT

Recent Presentations

Materials from all past sessions