Search Results for author:
Found 13 papers, 3 papers with code
Outlier galaxy images in the Dark Energy Survey and their identification with unsupervised machine learning
The Dark Energy Survey is able to collect image data of an extremely large number of extragalactic objects, and it can be reasonably assumed that many unusual objects of high scientific interest are hidden inside these data.
A data science and machine learning approach to continuous analysis of Shakespeare's plays
The analysis shows clear changes in the style of writing over time, with the most significant changes in the sentence length, frequency of adjectives and adverbs, and the sentiments expressed in the text.
Systematic biases when using deep neural networks for annotating large catalogs of astronomical images
One of the main downsides of DCNNs is the complex non-intuitive rules that make DCNNs act as a ``black box", providing annotations in a manner that is unclear to the user.
Self-Supervised Approach to Addressing Zero-Shot Learning Problem
In recent years, self-supervised learning has had significant success in applications involving computer vision and natural language processing.
SVMnet: Non-parametric image classification based on convolutional SVM ensembles for small training sets
Experimental results show that while "conventional" DCNN architectures such as ResNet-50 outperform SVMnet when the size of the training set is large, SVMnet provides a much higher accuracy when the number of "ground truth" training samples is small.
eGAN: Unsupervised approach to class imbalance using transfer learning
In the proposed approach we use the discriminator network to output a negative or positive score.
AdeNet: Deep learning architecture that identifies damaged electrical insulators in power lines
Several deep neural networks were tested, as were shallow learning methods.
Analysis of the alignment of non-random patterns of spin directions in populations of spiral galaxies
These aspects include the presence of duplicate objects in a dataset, errors in the galaxy annotation process, and non-random distribution of the asymmetry that does not necessarily form a dipole or quadrupole axes.
Cosmology and Nongalactic Astrophysics
Automatic identification of outliers in Hubble Space Telescope galaxy images
Rare extragalactic objects can carry substantial information about the past, present, and future universe.
A catalog of broad morphology of Pan-STARRS galaxies based on deep learning
The catalog contains morphology labels for 1, 662, 190 galaxies with ~95% accuracy.
Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era
The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration.
Computer Analysis of Architecture Using Automatic Image Understanding
Here we show that computer analysis of building images can perform quantitative analysis of architecture, and quantify similarities between city architectural styles in a quantitative fashion.
Combining human and machine learning for morphological analysis of galaxy images
The increasing importance of digital sky surveys collecting many millions of galaxy images has reinforced the need for robust methods that can perform morphological analysis of large galaxy image databases.
