相关论文: Fink: early supernovae Ia classification using act…
Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent active learning frameworks, Large Language Models (LLMs)…
In the era of large all-sky surveys, there will be a need for rapid, automatic classifications of newly discovered transient objects. Our focus here is the classification of supernovae (SNe). We consider random forest machine learning…
While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. However, manually annotating data is expensive and…
The nature of Type Ia supernova (SN Ia) progenitor systems and the mechanisms that lead up to their explosions are still widely debated. In rare cases the SN ejecta interact with circumstellar material (CSM) that was ejected from the…
We initiate the study of active learning algorithms for classifying strategic agents. Active learning is a well-established framework in machine learning in which the learner selectively queries labels, often achieving substantially higher…
The Hubble constant ($H_0$) is one of the fundamental parameters in cosmology, but there is a heated debate around the $>$4$\sigma$ tension between the local Cepheid distance ladder and the early Universe measurements. Strongly lensed Type…
While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. However, manually annotating data is expensive and…
Strongly lensed Type Ia supernovae (LSNe Ia) are a promising probe to measure the Hubble constant ($H_0$) directly. To use LSNe Ia for cosmography, a time-delay measurement between the multiple images, a lens-mass model, and a mass…
This paper presents FAMIE, a comprehensive and efficient active learning (AL) toolkit for multilingual information extraction. FAMIE is designed to address a fundamental problem in existing AL frameworks where annotators need to wait for a…
Among the supernovae (SNe) that show strong interaction with the circumstellar medium, there is a rare subclass of Type Ia supernovae, SNe Ia-CSM, that show strong narrow hydrogen emission lines much like SNe IIn but on top of a diluted…
Type Ia supernovae (SNe Ia) are essential tools for addressing key cosmic questions, including the Hubble tension and the nature of dark energy. Modern surveys are predominantly photometry-based, making the construction of a clean…
More than 3000 spectroscopically confirmed Type Ia supernovae (SNe Ia) are presented in the Zwicky Transient Facility SN Ia Data Release 2 (ZTF DR2). In this paper, we detail the spectral properties of 482 SNe Ia near maximum light, up to a…
Observations of type Ia supernovae (SNe Ia) have led to suggestions of multiple progenitor and explosion scenarios. Distinguishing between scenarios and tying specific SNe Ia to individual scenarios however has so far been challenging.…
Function is increasingly recognized as an important indicator of whole-person health, although it receives little attention in clinical natural language processing research. We introduce the first public annotated dataset specifically on…
Strongly lensed supernovae (SNe) are a rare class of transient that can offer tight cosmological constraints that are complementary to methods from other astronomical events. We present a follow-up study of one recently-discovered strongly…
Active learning (AL) is a label-efficient machine learning paradigm that focuses on selectively annotating high-value instances to maximize learning efficiency. Its effectiveness can be further enhanced by incorporating weak supervision,…
One of the biggest challenges that complicates applied supervised machine learning is the need for huge amounts of labeled data. Active Learning (AL) is a well-known standard method for efficiently obtaining labeled data by first labeling…
Active Learning (AL) is a powerful tool for learning with less labeled data, in particular, for specialized domains, like legal documents, where unlabeled data is abundant, but the annotation requires domain expertise and is thus expensive.…
Federated learning (FL) has been intensively investigated in terms of communication efficiency, privacy, and fairness. However, efficient annotation, which is a pain point in real-world FL applications, is less studied. In this project, we…
Photometric classifications of supernova (SN) light curves have become necessary to utilize the full potential of large samples of observations obtained from wide-field photometric surveys, such as the Zwicky Transient Facility (ZTF) and…