相关论文: On the efficiency of parameter space exploration: …
Models of weak-scale supersymmetry offer viable dark matter (DM) candidates. Their parameter spaces are however rather large and complex, such that pinning down the actual parameter values from experimental data can depend strongly on the…
We present a novel deep learning method for estimating time-dependent parameters in Markov processes through discrete sampling. Departing from conventional machine learning, our approach reframes parameter approximation as an optimization…
The recent astronomical measurements of distant supernovae as well as other observations indicate that our universe is presently accelerating. There are different proposals for the explanation of this acceleration, such as the cosmological…
Semantic segmentation tasks can be well modeled by Markov Random Field (MRF). This paper addresses semantic segmentation by incorporating high-order relations and mixture of label contexts into MRF. Unlike previous works that optimized MRFs…
We present a neural net algorithm for parameter estimation in the context of large cosmological data sets. Cosmological data sets present a particular challenge to pattern-recognition algorithms since the input patterns (galaxy redshift…
Obtaining the set of cosmological parameters consistent with observational data is an important exercise in current cosmological research. It involves finding the global maximum of the likelihood function in the multi-dimensional parameter…
Classical parameter-space Bayesian inference for Bayesian neural networks (BNNs) suffers from several unresolved prior issues, such as knowledge encoding intractability and pathological behaviours in deep networks, which can lead to…
We show that Ma's scotogenic model, which is arguably one of the simplest settings containing a Dark Matter candidate and generating a naturally suppressed active neutrino mass at 1-loop level, suffers from a potentially severe…
Advances in mobile computing technologies have made it possible to monitor and apply data-driven interventions across complex systems in real time. Markov decision processes (MDPs) are the primary model for sequential decision problems with…
Nonlinear regression problem is one of the most popular and important statistical tasks. The first methods like least squares estimation go back to Gauss and Legendre. Recent models and developments in statistics and machine learning like…
This work uses a combination of a variational auto-encoder and generative adversarial network to compare different dark energy models in light of observations, e.g., the distance modulus from type Ia supernovae. The network finds an…
We extend our two-scale neural-network method for scalar singularly perturbed problems with one small parameter to dynamical systems with multiple small parameters. To accommodate multiple small parameters, we use a single effective scale…
We examine the Scotogenic model employing the TM$_2$ mixing matrix, $U_{\text{TM}_2}$, for neutrinos and parameterize the Yukawa coupling matrix $y$ based on the diagonalization condition for the neutrino mass matrix, $m_{\nu}$. Our…
Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of…
We propose a minimal extention of the Standard Model where neutrino masses are generated radiatively at one-loop level via Scotogenic scanario. The model is augmented with $A_4$ modular symmetry as a scotogenic and flavor symmetry. With…
A new approach for deriving neutrino masses from the known masses of quarks, charged leptons, and the parameters of the Pontecorvo-Maki-Nakagawa-Sakata (PMNS) matrix is proposed. This framework is based on the simultaneous diagonalization…
A convincing identification of dark matter (DM) particles can probably be achieved only through a combined analysis of different detections strategies, which provides an effective way of removing degeneracies in the parameter space of DM…
We present a neural net algorithm for parameter estimation in the context of large cosmological data sets. Cosmological data sets present a particular challenge to pattern-recognition algorithms since the input patterns (galaxy redshift…
The future detection and measurement of the diffuse neutrino supernova background will shed light on the rate of supernovae events in the Universe, the star formation rate and the neutrino spectrum from each supernova. Little has been said…
In this work, we propose a new flow-matching Markov chain Monte Carlo (FM-MCMC) algorithm for estimating the orbital parameters of exoplanetary systems, especially for those only one exoplanet is involved. Compared to traditional methods…