Paper

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 LlaVA-Gemma: Accelerating Multimodal Foundation Models With a Compact Language Model by Musashi Hinck, Matthew L. Olson, David Cobbley, Shao-Yen Tseng, and Vasudev Lal. Hinck et. al....

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 comment on the paper Mixed-Privacy Forgetting in Deep Networks by Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, and Stefano Soatto. Golatkar et. al. introduce a novel method for forgetting in a mixed-privacy setting, where a core subset of the training samples will not be forgotten....