Search Results for author: Mike Walmsley

Found 10 papers, 8 papers with code

Rare Galaxy Classes Identified In Foundation Model Representations

no code implementations5 Dec 2023 Mike Walmsley, Anna M. M. Scaife

We identify rare and visually distinctive galaxy populations by searching for structure within the learned representations of pretrained models.

Clustering

Deep Learning Segmentation of Spiral Arms and Bars

1 code implementation5 Dec 2023 Mike Walmsley, Ashley Spindler

We present the first deep learning model for segmenting galactic spiral arms and bars.

Towards Galaxy Foundation Models with Hybrid Contrastive Learning

1 code implementation23 Jun 2022 Mike Walmsley, Inigo Val Slijepcevic, Micah Bowles, Anna M. M. Scaife

New astronomical tasks are often related to earlier tasks for which labels have already been collected.

Contrastive Learning

Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift

1 code implementation19 Apr 2022 Inigo V. Slijepcevic, Anna M. M. Scaife, Mike Walmsley, Micah Bowles, Ivy Wong, Stanislav S. Shabala, Hongming Tang

In this work we examine the classification accuracy and robustness of a state-of-the-art semi-supervised learning (SSL) algorithm applied to the morphological classification of radio galaxies.

Benchmarking Classification

Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification

1 code implementation4 Jan 2022 Devina Mohan, Anna M. M. Scaife, Fiona Porter, Mike Walmsley, Micah Bowles

In this work we use variational inference to quantify the degree of uncertainty in deep learning model predictions of radio galaxy classification.

Classification Data Augmentation +1

Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies

1 code implementation16 Feb 2021 Mike Walmsley, Chris Lintott, Tobias Geron, Sandor Kruk, Coleman Krawczyk, Kyle W. Willett, Steven Bamford, Lee S. Kelvin, Lucy Fortson, Yarin Gal, William Keel, Karen L. Masters, Vihang Mehta, Brooke D. Simmons, Rebecca Smethurst, Lewis Smith, Elisabeth M. Baeten, Christine Macmillan

All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314, 000 galaxies.

Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning

1 code implementation17 May 2019 Mike Walmsley, Lewis Smith, Chris Lintott, Yarin Gal, Steven Bamford, Hugh Dickinson, Lucy Fortson, Sandor Kruk, Karen Masters, Claudia Scarlata, Brooke Simmons, Rebecca Smethurst, Darryl Wright

We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies.

Active Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.