To be continued
Paper and Theory discussion group for domains in Generative Modeling, likelihood-based or score-based both.
We also cover some parts of Computational Optimal Transport and Differential Geometry.
Meeting links are shared in the Github Discussion link given below. Group is open to all -- no gatekeeping.
But knowledge pre-requisites: Linear Algebra, Probabilistic Machine Learning and Information Theory
Only identified lab members will be accepted.
Focused on: Trends in Machine Learning, Computer Vision, Action Recognition, Pose Estimation, Domain Adaptation, Neural Architecture Search and Augmented Reality.
Other relevant topics: Image and Video Processing, Virtual Reality, Simulations, Computer Graphics.
All things quantum, from basic principles to practical applications using quantum computing
To keep track of papers we read in the CHIL group. Open to CHIL group members only with @uwaterloo email addresses.
This covers topics related to broader AI (Artificial Intelligence), including but not limited to Deep Learning, Machine Learning, Evolutionary Computation, Search, Algorithms, Complexity and others. This is inspired from "Boston Computation Club" and "London Computation Club"
Planetary astrophishing es una reunión semanal para discutir artículos sobre ciencia de sistemas planetarios que hayan salido recientemente en la literatura, por ejemplo en astroph en la parte de Earth and Planetary Astrophysics. El objetivo principal de la reunión es estar al tanto de la investigación y resultados recientes sobre ciencia planetaria. Además tiene como consecuencia formar vínculos entre los miembros de distintas instituciones de la UNAM interesados en ciencia planetaria. El nombre Astrophishing nace porque de todos los artículos que son publicados sólo hablaríamos de unos pocos, los que nos parezcan los más interesantes.
Covering the "purer" topics in computation, including but not limited to: programming languages, type theory, category theory, formal methods, algorithms, and complexity. (A brazen rip-off of the London Computation Club, but in a time-zone more amenable to our American schedules.)
We discuss new results in the field of machine learning. Our interest is quite broad but recently our focus has been on transformer-based NLP methods.
A reading group about Graph Representation Learning (GRL) also known as geometric deep learning. We read the latest papers on GRL published at major machine learning conferences such as NeurIPS, ICML, and ICLR.
We are a global group of students and researchers (from academia and industry) interested in machine learning and its application to analysing graph-structured data. We meet via Zoom for one hour every week to discuss a research paper.
Anyone is welcome to join our group.