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Multiplicity Boost Of Transit Signal Classifiers: Validation of 69 New Exoplanets Using The Multiplicity Boost of ExoMiner
Most existing exoplanets are discovered using validation techniques rather than being confirmed by complementary observations.
ExoMiner: A Highly Accurate and Explainable Deep Learning Classifier that Validates 301 New Exoplanets
ExoMiner is a highly accurate, explainable, and robust classifier that 1) allows us to validate 301 new exoplanets from the MAST Kepler Archive and 2) is general enough to be applied across missions such as the on-going TESS mission.
Relative Comparison Kernel Learning with Auxiliary Kernels
In this work, we explore methods for aiding the process of learning a kernel with the help of auxiliary kernels built from more easily extractable information regarding the relationships among objects.
Learning to Rank by Optimizing NDCG Measure
Learning to rank is a relatively new field of study, aiming to learn a ranking function from a set of training data with relevancy labels.
