Deep Learning

Disclaimer: This is part of my notes on AI research papers. I do this to learn and communicate what I understand. Feel free to comment if you have any suggestion, that would be very much appreciated. Here you can find my notes from the lecture on Nonequilibrium Statistical Mechanics by Chris Jarzynski from University of Maryland. His lecture is available on YouTube: Nonequilibrium Statistical Mechanics - Part 1 Nonequilibrium Statistical Mechanics - Part 2...

Disclaimer: This is part of my notes on AI research papers. I do this to learn and communicate what I understand. Feel free to comment if you have any suggestion, that would be very much appreciated. The following post is a comment on the paper SLIDE: In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems by Beidi Chen, Tharun Medini, James Farwell, Sameh Gobriel, Charlie Tai and Anshumali Shrivastava, from Rice University and Intel Corporation....

Disclaimer: This is part of my notes on AI research papers. I do this to learn and communicate what I understand. Feel free to comment if you have any suggestion, that would be very much appreciated. The following post is a comment on the paper Auto Encoding Variational Bayes by Diederik P. Kingma and Max Welling. What introduces their contributions is the following question: How can we perform efficient approximate inference and learning with directed probabilistic models whose continuous latent variables and/or parameters have intractable posterior distributions?...

Disclaimer: This is part of my notes on AI research papers. I do this to learn and communicate what I understand. Feel free to comment if you have any suggestion, that would be very much appreciated. The following post is a comment on the paper SSSE: Efficiently Erasing Samples From Trained Machine Learning Models by Alexandra Peste, Dan Alistarh, and Christoph H. Lampert. Peste et. al. propose Single-Step Sample Erasure (SSSE), a method to efficiently and effectively erase samples from trained machine learning models....

Disclaimer: This is part of my notes on AI research papers. I do this to learn and communicate what I understand. Feel free to comment if you have any suggestion, that would be very much appreciated. The following post is a summary of the paper Denoising Diffusion Probabilistic Models by Jonathan Ho, Ajay Jain and Pieter Abbeel, from University of California, Berkeley. The paper was published in 2020 and is a follow up on Diffusion Probabilistic Models (DPM)....