no code implementations • 3 Jan 2024 • Ernest Perkowski, Rui Pan, Tuan Dung Nguyen, Yuan-Sen Ting, Sandor Kruk, Tong Zhang, Charlie O'Neill, Maja Jablonska, Zechang Sun, Michael J. Smith, Huiling Liu, Kevin Schawinski, Kartheik Iyer, Ioana Ciucă for UniverseTBD
We explore the potential of enhancing LLM performance in astronomy-focused question-answering through targeted, continual pre-training.
1 code implementation • 13 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.
no code implementations • 7 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.
2 code implementations • 2 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.
Ranked #1 on Galaxy emergent property recreation on SDSS Galaxies
1 code implementation • 1 Oct 2020 • Michael J. Smith, Nikhil Arora, Connor Stone, Stéphane Courteau, James E. Geach
In perspective, Pix2Prof would take under an hour to infer profiles for $10^5$ galaxies on a single NVIDIA DGX-2 system.
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