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.

Upcoming Meetings

??/??/???? – “What you wanted to know about ICA, but were afraid to ask”

??/??/???? – Normalizing Flows

??/??/???? – Time-Varying Neural Networks

??/??/???? – Information Theory for Deep Learning

??/??/???? – Word Embeddings

Past Meetings

Spring 2020

06/26/2020 – ANIL (slides) (video)Alex presents

06/19/2020 – “Multitask Learning over graphs” (slides) (video) – Riyasat and Brad Present

06/12/2020 – MAML and iMAMLAlex presents

06/05/2020 – Contrastive Predictive CodingMahfuz presents

05/29/2020 – Cutting out the Middle-Man:L Training and Evaluating Energy-Based Models without SamplingAlex presents

05/22/2020 – Energy-Based Models VideoJack and Alex present

05/15/2020 – Noise Contrastive EstimationSergey presents

05/08/2020 – “Representation Learning”Alex presents

05/01/2020 – ” Shortcut Learning in Deep Neural Networks”Alex presents

04/24/2020 – Fixed Point Layers (pt 2) – presented by Sergey

04/17/2020 – Fixed Point Layers – presented by Sergey

04/10/2020 – Multitask Learning – presented by Alex

03/13/2020 – A tutorial on Automatic Differentiation – presented by Sergey

03/06/2020 – Sanity Checks for Saliency Maps

02/28/2020 – Complexity control by gradient descent in deep networks

02/21/2020 – A mathematical theory of semantic development in deep neural networks

02/14/2020 – N/A

02/07/2020 – Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

01/31/2020 – Data-driven discovery of coordinates andgoverning equations

01/24/2020 – Neural Networks in System Identification

01/17/2020 – A tutorial on hidden Markov models and selected applications in speech recognition

Fall 2019

12/06/2019 – Meta-learning for neuroimaging

11/22/2019 – Nonlinear Dimensionality Reduction methods and a generalization

11/08/2019 – Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead

11/01/2019 – Synthesizing Programs for Images using Reinforced Adversarial Learning

10/25/2019 – Introduction to Deep Q-Network

10/18/2019 – Hidden stratification

10/11/2019 – Neural Ordinary Differential Equations

10/04/2019 – Model Utility

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

maintained by
Brad Baker
organized by
Sergey Plis