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

What I Read: Learning Neural Causal Models

By Andrew Fairless on December 30, 2020December 20, 2020

https://arxiv.org/abs/1910.01075Learning Neural Causal Models from Unknown InterventionsNan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio “We present a new framework for meta-learning causal modelsContinue readingWhat I Read: Learning Neural Causal Models

What I Read: Exploring Bayesian Optimization

By Andrew Fairless on December 26, 2020December 13, 2020

https://distill.pub/2020/bayesian-optimization/ Exploring Bayesian OptimizationBreaking Bayesian Optimization into small, sizeable chunks.AuthorsApoorv AgnihotriIndian Insitute of Technology GandhinagarNipun BatraIndian Insitute of Technology GandhinagarPublishedMay 5, 2020DOI10.23915/distill.00026 “Many modern machine learning algorithms have a largeContinue readingWhat I Read: Exploring Bayesian Optimization

What I Read: Artificial Intelligence Will Do What We Ask

By Andrew Fairless on December 9, 2020November 29, 2020

https://www.quantamagazine.org/artificial-intelligence-will-do-what-we-ask-thats-a-problem-20200130/ artificial intelligenceArtificial Intelligence Will Do What We Ask. That’s a Problem.Natalie WolchoverSenior Writer/EditorJanuary 30, 2020By teaching machines to understand our true desires, one scientist hopes to avoid the potentiallyContinue readingWhat I Read: Artificial Intelligence Will Do What We Ask

What I Read: Bayesian Neural Networks Need Not Concentrate

By Andrew Fairless on December 7, 2020December 13, 2020

https://jacobbuckman.com/2020-01-22-bayesian-neural-networks-need-not-concentrate/ Bayesian Neural Networks Need Not ConcentratePosted on January 22, 2020by Carles Gelada and Jacob Buckman “…it’s important that we, as a field, stop ignoring the crucial importance that theContinue readingWhat I Read: Bayesian Neural Networks Need Not Concentrate

What I Read: The Case for Bayesian Deep Learning

By Andrew Fairless on December 6, 2020December 13, 2020

https://cims.nyu.edu/~andrewgw/caseforbdl/ The Case for Bayesian Deep LearningAndrew Gordon WilsonJanuary 11, 2020 “…ignoring epistemic uncertainty is a key reason that standard neural network training is miscalibrated. By erroneously assuming that theContinue readingWhat I Read: The Case for Bayesian Deep Learning

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: Bayesian