ML Theory.

I am one of the organizers for the ML Theory Learning group at Cohere For AI and we will be having our session’s bi-weekly from 2 - 3 pm MST! If you aren’t a part of this wonderful community feel free to apply through this form to join us.

This community is amazing and we have tons of other reading groups and I personally am enjoying the Reinforcement Learning Reading group events, Fireside talks and other events that the community holds. Its a really supportive environment, intellectually motivating and you definitely are going to find atleast one person who shares the same research interest as you.

Third Session

Last saturday, we had our third ML theory session and we had two wonderful speakers Sree Harsha Nelaturu and Max Marion who talked about Meta Learning and Neural Information Retrieval respectively. I enjoyed the talks and it was very informative.

Some of the resources are listed below

Neural Information Retrieval - Lecture Notes on Neural Information Retrieval -

Meta Learning

  1. Meta Genetic Programming -
  2. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks -
  3. Prototypical Networks for Few-shot Learning -
  4. Learning to learn by gradient descent by gradient descent -
  5. Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples -
  6. Meta Policy Gradients -
  7. Differentiable Plasticity: training plastic networks with Backprop -
  8. Meta RL -
  9. Synthetic Petri Dish -
  10. POET -

Second Session

Yesterday, we had our second session, which went really well! We had 4 exciting proposals from Sree Harsha Nelaturu, Ashish Gaurav, Max Marion and Harry Saini on the topics Meta Learning, Quantum probability, Neural Information Retrieval and Representation learning, respectively.

The voting also happened, and for the next meeting on 19/11/2022, we will have Harsha and Max talk on Meta-Learning and Neural Information Retrieval. In the subsequent meeting on 3/12/2022, we will have Harry and Ashish talk on Representation Learning and Quantum Probability.

All the speakers will post some pre-requisite material to read up on or familiarize ourselves with before the talk (at least 1 week in advance), so we all prepare and learn as much as we can from the talk! Each presenter will talk for 25 minutes, followed by a 5 - 10 min Q/A! If you have more questions for the speakers, we will also create forums after each presentation where the conversation can be carried on. Thanks to everyone who came the other day; see you all next meeting:)

First Session

We had the ML Theory Learning group’s first session, which was a great success. We discussed what we all hope to gain out of this reading group, our mission statement (which is subject to change over time) as well as potential topics we could cover. I am super excited to see where this group goes and I hope to see you all there!


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