generative – Page 5 – Andrew Fairless, Ph.D.

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: Introduction AutoEncoder

By Andrew Fairless on November 15, 2021October 24, 2021

https://www.theaidream.com/post/an-introduction-to-autoencoder-and-variational-autoencoder-vae Introduction to AutoEncoder and Variational AutoEncoder(VAE)Nagesh Singh ChauhanJul 28“In recent years, deep learning-based generative models have gained more and more interest…. we will dive deep into these generative networksContinue readingWhat I Read: Introduction AutoEncoder

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: Adversarial Neural Cryptography

By Andrew Fairless on May 18, 2021May 1, 2021

https://pub.towardsai.net/what-is-adversarial-neural-cryptography-70b461c7db88?gi=3bf97cbeefd What is Adversarial Neural Cryptography?The novel approach combines GANs and cryptography in a single, powerful security method.Jesus RodriguezApr 19 “Somewhere between anonymization methods and homomorphic encryption, we find aContinue readingWhat I Read: Adversarial Neural Cryptography

What I Read: Adversarial generation of extreme samples

By Andrew Fairless on March 19, 2021March 7, 2021

https://aihub.org/2020/10/01/adversarial-generation-of-extreme-samples/ Adversarial generation of extreme samplesby Lucy SmithOctober 1, 2020 “…existing GAN-based methods tend to generate typical samples.. the authors seek to design deep learning-based models that can generate samplesContinue readingWhat I Read: Adversarial generation of extreme samples

What I Read: Causal Reasoning in Probability Trees

By Andrew Fairless on February 11, 2021January 13, 2021

https://arxiv.org/abs/2010.12237 Algorithms for Causal Reasoning in Probability TreesTim Genewein, Tom McGrath, Grégoire Déletang, Vladimir Mikulik, Miljan Martic, Shane Legg, Pedro A. Ortega “Probability trees are one of the simplest modelsContinue readingWhat I Read: Causal Reasoning in Probability Trees

What I Read: Continual Learning… and the Bridge to Active and Open World Learning

By Andrew Fairless on January 22, 2021December 30, 2020

https://arxiv.org/abs/2009.01797 A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World LearningMartin Mundt, Yong Won Hong, Iuliia Pliushch, Visvanathan RameshContinue readingWhat I Read: Continual Learning… and the Bridge to Active and Open World Learning

What I Read: Deep Generative Models

By Andrew Fairless on December 25, 2020December 13, 2020

https://medium.com/@jrodthoughts/microsoft-research-unveils-three-efforts-to-advance-deep-generative-models-b1d2fe3395e8 Microsoft Research Unveils Three Efforts to Advance Deep Generative ModelsOptimus, FQ-GAN and Prevalent bring new ideas to apply generative models at large scale.Jesus RodriguezApr 27 “With the emergence ofContinue readingWhat I Read: Deep Generative Models

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