Search Results for author:
Found 9 papers, 4 papers with code
An Analysis of the Recovery Path of the Consumer Sector in the Post-Pandemic Era
This paper proposes a referencable pattern of the recovery of the consumption sector, a new dimension to observe and evaluate the intrinsic value of the consumption sector, and proposes the concept of sensory-based consumption and the ranking of the weights of different categories;creates the concept of digital consumption index, coupled with digital RMB index and China-style digital economy index.
Stellar Spectra Fitting with Amortized Neural Posterior Estimation and nbi
Given the association of spectral data properties with the observing instrument, we discuss the utility of an ANPE "model zoo," where models are trained for specific instruments and distributed under the nbi framework to facilitate real-time stellar parameter inference.
nbi: the Astronomer's Package for Neural Posterior Estimation
We identify three critical issues: the need for custom featurizer networks tailored to the observed data, the inference inexactness, and the under-specification of physical forward models.
Portfolio selection models based on interval-valued conditional value at risk (ICVaR) and empirical analysis
Risk management is very important for individual investors or companies.
A Ubiquitous Unifying Degeneracy in Two-Body Microlensing Systems
While gravitational microlensing by planetary systems provides unique vistas on the properties of exoplanets, observations of a given 2-body microlensing event can often be interpreted with multiple distinct physical configurations.
Real-Time Likelihood-Free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation
Fast and automated inference of binary-lens, single-source (2L1S) microlensing events with sampling-based Bayesian algorithms (e. g., Markov Chain Monte Carlo; MCMC) is challenged on two fronts: high computational cost of likelihood evaluations with microlensing simulation codes, and a pathological parameter space where the negative-log-likelihood surface can contain a multitude of local minima that are narrow and deep.
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural Networks
Neural networks (NNs) have been shown to be competitive against state-of-the-art feature engineering and random forest (RF) classification of periodic variable stars.
Automating Inference of Binary Microlensing Events with Neural Density Estimation
Automated inference of binary microlensing events with traditional sampling-based algorithms such as MCMC has been hampered by the slowness of the physical forward model and the pathological likelihood surface.
deepCR: Cosmic Ray Rejection with Deep Learning
We demonstrate that at a false positive rate of 0. 5%, deepCR achieves close to 100% detection rates in both extragalactic and globular cluster fields, and 91% in resolved galaxy fields, which is a significant improvement over the current state-of-the-art method LACosmic.
