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
Fall 2020
8/28/2020 – Deep Learning for Right Reasons (video) (slides) – Noah presents
08/21/2020 – Sparsity Inducing Regularization (video) – Riyasat presents
Summer 2020
08/14/2020 – Inference of Causal Relations in Dynamical Systems
(Taken Theorem video) – Jack presents
08/07/2020 – Analytic Marching (slides) (video) – William presents
07/31/2020 – Learning Graph Representations
07/24/2020 – CNNs on Graphs (video) – Alex presents
07/10/2020 – Hierarchical LSTMs (video)– William presents
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 iMAML – Alex presents
06/05/2020 – Contrastive Predictive Coding – Mahfuz presents
Spring 2020
05/29/2020 – Cutting out the Middle-Man:L Training and Evaluating Energy-Based Models without Sampling – Alex presents
05/22/2020 – Energy-Based Models – Video – Jack and Alex present
05/15/2020 – Noise Contrastive Estimation – Sergey 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
Summer 2019
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
Brad Baker
Sergey Plis