noise – Andrew Fairless, Ph.D.

What I Read: How diffusion models work

By Andrew Fairless on November 30, 2022October 20, 2022

https://theaisummer.com/diffusion-models/ How diffusion models work: the math from scratchSergios Karagiannakos, Nikolas Adaloglouon2022-09-29 “Diffusion models are a new class of state-of-the-art generative models that generate diverse high-resolution images.”

What I Read: Illustrated Stable Diffusion

By Andrew Fairless on November 29, 2022October 20, 2022

https://jalammar.github.io/illustrated-stable-diffusion/ The Illustrated Stable DiffusionJay AlammarVisualizing machine learning one concept at a time. “The ability to create striking visuals from text descriptions has a magical quality to it and pointsContinue readingWhat I Read: Illustrated Stable Diffusion

What I Read: Chaos Researchers Can Now Predict

By Andrew Fairless on November 10, 2022September 27, 2022

https://www.quantamagazine.org/ai-algorithm-foresees-chaotic-tipping-points-20220915/ Chaos Researchers Can Now Predict Perilous Points of No ReturnBen BrubakerSeptember 15, 2022 “A custom-built machine learning algorithm can predict when a complex system is about to switch toContinue readingWhat I Read: Chaos Researchers Can Now Predict

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: ‘Machine Scientists’ Distill the Laws of Physics From Raw Data

By Andrew Fairless on June 7, 2022May 23, 2022

https://www.quantamagazine.org/machine-scientists-distill-the-laws-of-physics-from-raw-data-20220510/ Powerful ‘Machine Scientists’ Distill the Laws of Physics From Raw DataCharlie WoodStaff WriterMay 10, 2022 “Researchers say we’re on the cusp of “GoPro physics,” where a camera can pointContinue readingWhat I Read: ‘Machine Scientists’ Distill the Laws of Physics From Raw Data

What I Read: Learning, not Enough Data Part 3

By Andrew Fairless on June 1, 2022May 23, 2022

https://lilianweng.github.io/posts/2022-04-15-data-gen/ Learning with not Enough Data Part 3: Data GenerationApril 15, 2022Lilian Weng “Here comes the Part 3 on learning with not enough data (Previous: Part 1 and Part 2).Continue readingWhat I Read: Learning, not Enough Data Part 3

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: AI Researchers Fight Noise by Turning to Biology

By Andrew Fairless on February 1, 2022December 12, 2021

https://www.quantamagazine.org/ai-researchers-fight-noise-by-turning-to-biology-20211207/ AI Researchers Fight Noise by Turning to BiologyAllison WhittenContributing WriterDecember 7, 2021 “Tiny amounts of artificial noise can fool neural networks, but not humans. Some researchers are looking toContinue readingWhat I Read: AI Researchers Fight Noise by Turning to Biology

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: Reproducing Deep Double Descent

By Andrew Fairless on February 7, 2021January 11, 2021

https://hippocampus-garden.com/double_descent/ Reproducing Deep Double DescentShion HondaJune 13, 2020 “…you’ve probably heard of bias-variance trade-off… But, do you know that there is a continuation of the U-shaped test risk curve? Surprisingly,Continue readingWhat I Read: Reproducing Deep Double Descent

Tag: noise