Search Results for author:
Found 8 papers, 4 papers with code
Hairmony: Fairness-aware hairstyle classification
We instead choose a classification approach which can represent the diversity of hairstyles required for a truly robust and inclusive system.
Look Ma, no markers: holistic performance capture without the hassle
In this work, we introduce the first technique for marker-free, high-quality reconstruction of the complete human body, including eyes and tongue, without requiring any calibration, manual intervention or custom hardware.
Investigation of a Machine learning methodology for the SKA pulsar search pipeline
We have successfully demonstrated this algorithm by detecting candidate signatures on a simulation dataset.
MeerCRAB: MeerLICHT Classification of Real and Bogus Transients using Deep Learning
It is designed to filter out the so called 'bogus' detections from true astrophysical sources in the transient detection pipeline of the MeerLICHT telescope.
Imbalance Learning for Variable Star Classification
In this work, we attempt to further improve hierarchical classification performance by applying 'data-level' approaches to directly augment the training data so that they better describe under-represented classes.
Comparing Multi-class, Binary and Hierarchical Machine Learning Classification schemes for variable stars
Upcoming synoptic surveys are set to generate an unprecedented amount of data.
DeepSource: Point Source Detection using Deep Learning
For uniformly-weighted images it achieves a Purity x Completeness (PC) score at SNR = 3 of 0. 73, compared to 0. 31 for the best PyBDSF model.
Marginal Likelihoods from Monte Carlo Markov Chains
In this paper, we present a method for computing the marginal likelihood, also known as the model likelihood or Bayesian evidence, from Markov Chain Monte Carlo (MCMC), or other sampled posterior distributions.
Computation Cosmology and Nongalactic Astrophysics
