| Name | Abs no. | Title |
|---|
| Annalisa Pillepich | 2178 | ERGO-ML: Extracting Reality
from Galaxy Observables with Machine Learning |
| David Parkinson | 555 | Detecting complex sources in large
surveys using an apparent complexity measure |
| Dennis Crake | 2021 | In Search of the Peculiar: An
Unsupervised Approach to Anomaly Detection in the Transient
Universe. |
| Didier Fraix-Burnet | 1003 | Unsupervised classification:
a necessary step for Deep Learning? |
| Gordian Edenhofer | 1049 | Iterative Grid Refinement:
Approximate Gaussian Processes for Billions of Parameters |
| Jeroen Audenaert | 1830 | Unraveling the physical
mechanisms of pulsating stars through a multimodal and multidisciplinary
machine learning approach |
| Joshua Speagle | 707 | Incorporating Errors in Machine
Learning Methods |
| Melissa Lopez | 1744 | Simulating Transient Noise Bursts
in LIGO with Generative Adversarial Networks |
| Mike Walmsley | 1150 | Galaxy Zoo: Practical Methods for
Large-Scale Learning |
| Raquel Ruiz Valença | 2501 | Comparing machine learning
and deep learning models to estimate quasar photometric redshifts |
| Steffani Grondin | 1203 | Searching for the extra-tidal
stars of Galactic globular clusters with high-dimensional clustering
analysis |
| Vishal Upendran | 515 | Accelerating astronomy workflow
with deep learning and interpretable A.I |
| Yuan-Sen Ting | 503 | Quantifying non-Gaussianity with
mathematical insights from machine learning |
