latent – Andrew Fairless, Ph.D.

What I Read: Inducing hierarchy for multi-class classification

By Andrew Fairless on February 22, 2024January 7, 2024

https://arxiv.org/abs/2102.10263 Inducing a hierarchy for multi-class classification problemsHayden S. Helm, Weiwei Yang, Sujeeth Bharadwaj, Kate Lytvynets, Oriana Riva, Christopher White, Ali Geisa, Carey E. Priebe “In applications where categorical labelsContinue readingWhat I Read: Inducing hierarchy for multi-class classification

What I Read: Perspectives on diffusion

By Andrew Fairless on August 30, 2023July 30, 2023

https://sander.ai/2023/07/20/perspectives.html Perspectives on diffusionSander DielemanJuly 20, 2023 “Diffusion models appear to come in many shapes and forms…. these various perspectives each reveal new connections and are a breeding ground forContinue readingWhat I Read: Perspectives on diffusion

What I Read: Relative representations

By Andrew Fairless on April 18, 2023March 30, 2023

https://arxiv.org/abs/2209.15430 Relative representations enable zero-shot latent space communicationLuca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, Emanuele Rodolà “…we propose to adopt pairwise similarities as an alternative data representation,Continue readingWhat I Read: Relative representations

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: 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: 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: Flows for simultaneous manifold learning and density estimation

By Andrew Fairless on December 31, 2020December 20, 2020

https://arxiv.org/abs/2003.13913 Flows for simultaneous manifold learning and density estimationJohann Brehmer, Kyle Cranmer “We introduce manifold-learning flows (M-flows), a new class of generative models that simultaneously learn the data manifold asContinue readingWhat I Read: Flows for simultaneous manifold learning and density estimation

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: latent