Gaussian – Page 2 – Andrew Fairless, Ph.D.

What I Read: Neural Tangent Kernel

By Andrew Fairless on November 2, 2022September 27, 2022

https://lilianweng.github.io/posts/2022-09-08-ntk/ Some Math behind Neural Tangent KernelLilian WengSeptember 8, 2022 “Neural tangent kernel… leads to great insights into why neural networks with enough width can consistently converge to a globalContinue readingWhat I Read: Neural Tangent Kernel

What I Read: Annotated Diffusion Model

By Andrew Fairless on August 1, 2022June 27, 2022

https://huggingface.co/blog/annotated-diffusion The Annotated Diffusion ModelPublished June 7th, 2022Niels RoggeKashif Rasul “In this blog post, we’ll take a deeper look into Denoising Diffusion Probabilistic Models… as researchers have been able toContinue readingWhat I Read: Annotated Diffusion Model

What I Read: Weak Supervision

By Andrew Fairless on July 11, 2022June 27, 2022

https://www.kdnuggets.com/2022/05/weak-supervision-modeling-explained.html Weak Supervision Modeling, ExplainedBy Frederic Sala, Computer Sciences Department at the University of Winsconsin-MadisonMay 27, 2022 “Weak supervision is a way to obtain labels for training data points forContinue readingWhat I Read: Weak Supervision

What I Read: Generative Modeling by Estimating Gradients

By Andrew Fairless on March 7, 2022January 20, 2022

https://yang-song.github.io/blog/2021/score/ Generative Modeling by Estimating Gradients of the Data DistributionAuthor Yang SongDate May 5, 2021 “This blog post gives a detailed introduction to score-based generative models. We demonstrate that thisContinue readingWhat I Read: Generative Modeling by Estimating Gradients

What I Read: How Kalman filter works

By Andrew Fairless on January 25, 2022December 11, 2021

https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/ How a Kalman filter works, in pictures “I have to tell you about the Kalman filter… Surprisingly few software engineers and scientists seem to know about it… it isContinue readingWhat I Read: How Kalman filter works

What I Read: machine learning with differential privacy

By Andrew Fairless on November 29, 2021November 16, 2021

https://differentialprivacy.org/how-to-deploy-ml-with-dp/ How to deploy machine learning with differential privacy?Posted by Nicolas Papernot and Abhradeep Thakurta on October 25, 2021. “In many applications of machine learning, such as machine learning forContinue readingWhat I Read: machine learning with differential privacy

What I Read: Diffusion Models

By Andrew Fairless on August 18, 2021July 22, 2021

https://lilianweng.github.io/lil-log/2021/07/11/diffusion-models.html#nice What are Diffusion Models?Jul 11, 2021Lilian Weng“Diffusion models are inspired by non-equilibrium thermodynamics…. Unlike VAE or flow models, diffusion models are learned with a fixed procedure and the latentContinue readingWhat I Read: Diffusion Models

What I Read: Element-wise Active Information Acquisition

By Andrew Fairless on December 4, 2020November 28, 2020

https://arxiv.org/abs/1908.04537 Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian ModelWenbo Gong, Sebastian Tschiatschek, Richard Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang “…we introduce the ice-start problem, i.e.,Continue readingWhat I Read: Element-wise Active Information Acquisition

Tag: Gaussian