IAUS368: ML Pitfalls
NameAbs no.Title
Annalisa Pillepich2178ERGO-ML: Extracting Reality from Galaxy Observables with Machine Learning
David Parkinson555Detecting complex sources in large surveys using an apparent complexity measure
Dennis Crake2021In Search of the Peculiar: An Unsupervised Approach to Anomaly Detection in the Transient Universe.
Didier Fraix-Burnet1003Unsupervised classification: a necessary step for Deep Learning?
Gordian Edenhofer1049Iterative Grid Refinement: Approximate Gaussian Processes for Billions of Parameters
Jeroen Audenaert1830Unraveling the physical mechanisms of pulsating stars through a multimodal and multidisciplinary machine learning approach
Joshua Speagle707Incorporating Errors in Machine Learning Methods
Melissa Lopez1744Simulating Transient Noise Bursts in LIGO with Generative Adversarial Networks
Mike Walmsley1150Galaxy Zoo: Practical Methods for Large-Scale Learning
Raquel Ruiz Valença2501Comparing machine learning and deep learning models to estimate quasar photometric redshifts
Steffani Grondin1203Searching for the extra-tidal stars of Galactic globular clusters with high-dimensional clustering analysis
Vishal Upendran515Accelerating astronomy workflow with deep learning and interpretable A.I
Yuan-Sen Ting503Quantifying non-Gaussianity with mathematical insights from machine learning