Paper Reading: Machine Unlearning with Minimal Gradient Dependence

About this event

Join us for a paper discussion on latest advancements in machine unlearning with : "Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios" presented by Logan. This papers cover the latest advancements in LLMs and unlearning or intentionally removing information from LLMs and deep learning models after training. Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios (https://dl.acm.org/doi/pdf/10.1145/3793867) https://arxiv.org/pdf/2406.16986 Review Surveys for Machine Unlearning: https://www.mdpi.com/2079-9292/15/16/3643 https://www.researchgate.net/publication/389580822_A_Comprehensive_Survey_of_Machine_Unlearning_Techniques_for_Large_Language_Models Silicon Valley Generative AI has two meeting formats. 1\. Paper Reading \- Every second week we meet to discuss machine learning papers\. This is a collaboration between Silicon Valley Generative AI and Boulder Data Science\. 2\. Talks \- Once a month we meet to have someone present on a topic related to generative AI\. Speakers can range from industry leaders\, researchers\, startup founders\, subject matter experts and those with an interest in a topic and would like to share\. Topics vary from technical to business focused\. They can be on how the latest in generative models work and how they can be used\, applications and adoption of generative AI\, demos of projects and startup pitches or legal and ethical topics\. The talks are meant to be inclusive and for a more general audience compared to the paper readings\. If you would like to be a speaker please contact: Matt White