相关论文: Machine Learning applications for Cataclysmic Vari…
Context. Observations of cataclysmic variables in globular clusters appear to show a dearth of outbursts compared to those observed in the field. A number of explanations have been proposed, including low mass-transfer rates and/or moderate…
Continual Learning (CL) enables machine learning models to learn from continuously shifting new training data in absence of data from old tasks. Recently, pretrained vision transformers combined with prompt tuning have shown promise for…
Convolutional neural networks (CNN) are known for their excellent feature extraction capabilities to enable the learning of models from data, yet are used as black boxes. An interpretation of the convolutional filtres and associated…
Extending spatio-temporal scale limitations of models for complex atomistic systems considered in biochemistry and materials science necessitates the development of enhanced sampling methods. The potential acceleration in exploring the…
Selective classification enables models to make predictions only when they are sufficiently confident, aiming to enhance safety and reliability, which is important in high-stakes scenarios. Previous methods mainly use deep neural networks…
Continual learning models allow to learn and adapt to new changes and tasks over time. However, in continual and sequential learning scenarios in which the models are trained using different data with various distributions, neural networks…
We present the results of a search for cataclysmic variables (CVs) in the Calan-Tololo survey. We detected a total number of 21 CVs, 12 of them are previously unknown objects. Our results suggest that the mismatch between the theoretically…
We report the discovery of 11 new cataclysmic variable (CV) candidates by the Isaac Newton Telescope (INT) Photometric H alpha Survey of the northern Galactic plane (IPHAS). Three of the systems have been the subject of further follow-up…
The 2dF QSO survey is a spectroscopic survey of 48,000 point-sources selected by colour with magnitudes in the range 18.35 < B < 20.95. Amongst QSOs, white dwarfs, narrow-line galaxies and other objects are some cataclysmic variables (CVs).…
Context. AM CVn binaries are potential progenitors of thermonuclear supernovae and strong sources of persistent gravitational wave radiation. For a long time, it has been believed that these systems cannot descend from cataclysmic variables…
We present the first volume-limited sample of cataclysmic variables (CVs), selected using the accurate parallaxes provided by the second data release (DR2) of the ESA $\mathit{Gaia}$ space mission. The sample is composed of 42 CVs within…
Estimating physical parameters from data is a crucial application of machine learning (ML) in the physical sciences. However, systematic uncertainties, such as detector miscalibration, induce data distribution distortions that can erode…
Cross-validation (CV) is a popular method for model-selection. Unfortunately, it is not immediately obvious how to apply CV to unsupervised or exploratory contexts. This thesis discusses some extensions of cross-validation to unsupervised…
Catastrophe theory was originally proposed to study dynamical systems that exhibit sudden shifts in behavior arising from small changes in input. These models can generate reasonable explanation behind abrupt jumps in nonlinear dynamic…
Modern time-domain surveys continuously monitor large swaths of the sky to look for astronomical variability. Astrophysical discovery in such data sets is complicated by the fact that detections of real transient and variable sources are…
Modern time-domain surveys produce alert streams at a scale that makes exhaustive manual inspection infeasible, requiring automated methods to identify unusual transients for follow-up. In this work, we present an unsupervised anomaly…
Transformers have captured growing attention in computer vision, thanks to its large capacity and global processing capabilities. However, transformers are data hungry, and their ability to generalize is constrained compared to…
Deep generative models (DGMs) can generate synthetic data samples that closely resemble the original dataset, addressing data scarcity. In this work, we developed a conditional variational autoencoder (CVAE) to augment critical heat flux…
Different from large-scale platforms such as Taobao and Amazon, CVR modeling in small-scale recommendation scenarios is more challenging due to the severe Data Distribution Fluctuation (DDF) issue. DDF prevents existing CVR models from…
Machine learning has emerged as a promising paradigm for enabling connected, automated vehicles to autonomously cruise the streets and react to unexpected situations. A key challenge, however, is to collect and select real-time and reliable…