Related papers: Interpreting automatic AGN classifiers with salien…

We used random forest algorithms to classify all objects in a large portion of the sky, using optical light curves obtained, or built from images provided, by the Zwicky Transient Facility (ZTF). We compare different selection sets based on…

Industrial image anomaly detection under the setting of one-class classification has significant practical value. However, most existing models struggle to extract separable feature representations when performing feature embedding and…

We present a technique for translating a black-box machine-learned classifier operating on a high-dimensional input space into a small set of human-interpretable observables that can be combined to make the same classification decisions. We…

In the absence of the two emission lines H$\alpha$ and [NII] (6584\AA) in a BPT diagram, we show that other spectral information is sufficiently informative to distinguish AGN galaxies from star-forming galaxies. We use pattern recognition…

We use data from the All Wavelength Extended Groth Strip International Survey to construct stacked X-ray maps of optically bright active galaxies (AGN) and an associated control sample of galaxies at high redshift (z less than 0.6). From…

Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models. While most existing GNN models for…

Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification. However, the current GCN-based methods treat graph construction and image…

Machine learning (ML) methods have become popular for parameter inference in cosmology, although their reliance on specific training data can cause difficulties when applied across different data sets. By reproducing and testing networks…

One significant challenge of exploiting Graph neural networks (GNNs) in real-life scenarios is that they are always treated as black boxes, therefore leading to the requirement of interpretability. To address this, model-level…

Anomaly detection is often considered a challenging field of machine learning due to the difficulty of obtaining anomalous samples for training and the need to obtain a sufficient amount of training data. In recent years, autoencoders have…

The cosmological evolution of X-ray-selected and optically selected Active Galactic Nuclei (AGNs) show different behaviours interpreted in terms of two different populations. The difference is evident mainly for low luminosity AGNs…

The survey of the COSMOS field by the VLT Survey Telescope is an appealing testing ground for variability studies of active galactic nuclei (AGN). With 54 r-band visits over 3.3 yr and a single-visit depth of 24.6 r-band mag, the dataset is…

The crux of graph classification lies in the effective representation learning for the entire graph. Typical graph neural networks focus on modeling the local dependencies when aggregating features of neighboring nodes, and obtain the…

Suicide prediction is the key for prevention, but real data with sufficient positive samples is rare and causes extreme class imbalance. We utilized machine learning (ML) to build the model and deep learning (DL) techniques, like Generative…

Graph Neural Networks (GNNs) have gained popularity in various learning tasks, with successful applications in fields like molecular biology, transportation systems, and electrical grids. These fields naturally use graph data, benefiting…

In recent years, due to the powerful abilities to deal with highly complex tasks, the artificial neural networks (ANNs) have been studied in the hope of achieving human-like performance in many applications. Since the ANNs have the ability…

Most active supermassive black holes (SMBH) in present-day galaxies are underfed and consist of low-luminosity active galactic nuclei (LLAGN). They have multiwavelength broadband spectral energy distributions (SED) dominated by non-thermal…

Saliency maps are often used in computer vision to provide intuitive interpretations of what input regions a model has used to produce a specific prediction. A number of approaches to saliency map generation are available, but most require…

X-rays provide a robust method in identifying AGN. However, in the high-redshift Universe, their space density is relatively low, and, in combination with the small areas covered by X-ray surveys, the selected AGN are poorly sampled. Deep…

In recent years, machine learning (ML) has become a key enabling technology for the sciences and industry. Especially through improvements in methodology, the availability of large databases and increased computational power, today's ML…