Reading Club

Discussion of machine learning, its applications, and related ideas

A brown-bag (bring your own lunch) reading club at TReNDs Center held mainly for cross pollination of ideas and “out of the comfort zone” fun. We read and discuss papers, tutorials, or packs of papers and ideas at once. The topics span all areas of machine learning, with a focus on Deep Learning, but also venture into brain imaging and theories of how the brain works. The goal is to both learn and refine the knowledge of important foundational concepts and stay current with the literature.

Please sign up for the mailing list or, if you’re a part of the TReNDS Center, join the #reading_group channel on our Slack workspace.

A list of topics with associated materials can be found on our GitHub page.

Next Meeting: October 4th, 2019

This Friday’s BBL Seminar will focus on statistical techniques which may be employed when comparing ML/DL models, caveats in classification accuracy, and model utility. The primary focus will be on power calculations and the validity of models constructed within AI competitions, but the principles apply to just any ML/DL framework. A short summary of these trends and comparisons can be found on this blog post

Upcoming meetings (subject to change)

Normalizing flows
Time Varying Neural Networks
Contemporary Ethical Issues in Data Science/Neuroimaging
Information Theory for Deep Learning
Word Embeddings (Word2Vec, GloVe, etc.)

Past meetings

09/27/2019 – Wasserstein GANs and other easier to train models

09/20/2019 – Generative Adversarial Networks (Intro)

09/13/2019 – Predictive coding theory of the mind: part II

09/06/2019 – Predictive coding theory of the mind: Intro

08/30/2019 – Confidence and accuracy: On Calibration of Modern Neural Networks

08/23/2019 – Deep learning model introspection

08/16/2019 – Deep learning trends from the deep learning summer school

08/09/2019 – Variational Autoencoders: part II

08/02/2019 – Variational Autoencoders: part I

07/26/2019 – Attention in deep learning models: part II

07/19/2019 – Attention in deep learning models: part I

07/12/2019 – NLP successes of 2018: transfer learning

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