Search Results for author: Michael J. Smith

Found 6 papers, 4 papers with code

EarthPT: a time series foundation model for Earth Observation

1 code implementation13 Sep 2023 Michael J. Smith, Luke Fleming, James E. Geach

EarthPT is a 700 million parameter decoding transformer foundation model trained in an autoregressive self-supervised manner and developed specifically with EO use-cases in mind.

Earth Observation Time Series

Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy

no code implementations7 Nov 2022 Michael J. Smith, James E. Geach

In this review, we explore the historical development and future prospects of artificial intelligence (AI) and deep learning in astronomy.

Astronomy

Realistic galaxy image simulation via score-based generative models

2 code implementations2 Nov 2021 Michael J. Smith, James E. Geach, Ryan A. Jackson, Nikhil Arora, Connor Stone, Stéphane Courteau

We show that a Denoising Diffusion Probabalistic Model (DDPM), a class of score-based generative model, can be used to produce realistic mock images that mimic observations of galaxies.

Denoising Galaxy emergent property recreation +1

Generative deep fields: arbitrarily sized, random synthetic astronomical images through deep learning

1 code implementation Submitted to MNRAS 2019 Michael J. Smith, James E. Geach

Generative Adversarial Networks (GANs) are a class of artificial neural network that can produce realistic, but artificial, images that resemble those in a training set.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies

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