gnofai is temporarily on hiatus due to my scheduling difficulties.
I am open to passing the torch, so drop me a line if you are interested…
gnofai is temporarily on hiatus due to my scheduling difficulties.
I am open to passing the torch, so drop me a line if you are interested…
Zrinka has provided a well written and detailed writeup of her talk on sparse models for tracking time varying signals. You can read it here.
Ali began a discussion on learning the structure of sparse graphical models. The notes for the first lecture are attached. We will continue this discussion in a couple of weeks. The main reference he used was “High dimensional Ising model selection using ℓ1-regularized logistic regression.” by Ravikumar and Wainwright.
Ayan discussed “Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds” by Chen. et. al. available here. This paper develops a Bayesian model for mixtures of factor analyzers in the framework of compressed sensing i.e. with sparsity constraints on the factors. This idea is used to model manifolds and related structures. The meeting notes are attached.
Sanmi discussed “Bayesian Compressive Sensing via Belief Propagation” by Baron. et. al. available here, and discussed some other connections between probabilistic models and compressed sensing. The meeting notes are attached.
Ali Jalali gave a great introduction to compressive sensing; presenting the general intuition and the main results. I have attached the meeting notes.
Jeremy Stober put together a great set of notes on Gaussian processes for reinforcement learning. The notes are very clear and easy to follow. You can find them here.
Meghana began covering Gaussian processes for classification. You can find the notes here.
Priyank is still working the latex notes. In the meantime, here are the scanned notes from those meetings.