相关论文: Learning Theory Informed Priors for Bayesian Infer…
Many quintessence models possess scaling or attractor solutions where the fraction of dark energy follows the dominant component in previous epochs of the expansion, or phase transitions may happen close to matter-radiation equality time. A…
New early dark energy (NEDE) makes the cosmic microwave background consistent with a higher value of the Hubble constant inferred from supernovae observations. It is an improvement over the old early dark energy model (EDE) because it…
Probabilistic neural networks are typically modeled with independent weight priors, which do not capture weight correlations in the prior and do not provide a parsimonious interface to express properties in function space. A desirable class…
We study the implications of Planck data for models of dark energy (DE) and modified gravity (MG), beyond the cosmological constant scenario. We start with cases where the DE only directly affects the background evolution, considering…
There has been increasing interest by cosmologists in applying Bayesian techniques, such as Bayesian Evidence, for model selection. A typical example is in assessing whether observational data favour a cosmological constant over evolving…
Bayesian inference allows the transparent communication of uncertainty in material flow analyses (MFAs), and a systematic update of uncertainty as new data become available. However, the method is undermined by the difficultly of defining…
We investigate constraints on early dark energy (EDE) using ACT DR4, SPT-3G 2018, Planck polarization, and restricted Planck temperature data (at $\ell<650$), finding a $3.3\sigma$ preference ($\Delta\chi^2=-16.2$ for 3 additional degrees…
Large neural networks trained on large datasets have become the dominant paradigm in machine learning. These systems rely on maximum likelihood point estimates of their parameters, precluding them from expressing model uncertainty. This may…
Some cosmological models with non-negligible dark energy fractions in particular windows of the pre-recombination epoch are capable of alleviating the Hubble tension quite efficiently, while keeping the good description of the data that are…
Learned image compression (LIC) has achieved remarkable coding efficiency, where entropy modeling plays a pivotal role in minimizing bitrate through informative priors. Existing methods predominantly exploit internal contexts within the…
Compression and computational efficiency in deep learning have become a problem of great significance. In this work, we argue that the most principled and effective way to attack this problem is by adopting a Bayesian point of view, where…
Obtaining samples from the posterior distribution of inverse problems with expensive forward operators is challenging especially when the unknowns involve the strongly heterogeneous Earth. To meet these challenges, we propose a…
Bayesian inferences in high energy physics often use uniform prior distributions for parameters about which little or no information is available before data are collected. The resulting posterior distributions are therefore sensitive to…
One fundamental physics interpretation of Dark Energy Spectroscopic Instrument (DESI) results is that the observed accelerated expansion of the Universe is driven not by the cosmological constant, but by a slowly rolling scalar field, a…
Datasets in engineering applications are often limited and contaminated, mainly due to unavoidable measurement noise and signal distortion. Thus, using conventional data-driven approaches to build a reliable discriminative model, and…
We present a joint Bayesian analysis to impose constraints on the generalized $\alpha$-Starobinsky inflationary model using the high-precision cosmological datasets: Planck, CMB lensing from ACT DR6, and Baryon Acoustic Oscillations (BAO)…
Deep neural networks have achieved remarkable progress in enhancing low-light images by improving their brightness and eliminating noise. However, most existing methods construct end-to-end mapping networks heuristically, neglecting the…
Recent baryon acoustic oscillation measurements from the DESI, when combined with CMB data and Type Ia supernovae observations, indicate a preference for dynamical dark energy when considering the Chevallier-Polarski-Linder (CPL) model,…
We report the application of implicit likelihood inference to the prediction of the macro-parameters of strong lensing systems with neural networks. This allows us to perform deep learning analysis of lensing systems within a well-defined…
We investigate the presence of extra relativistic degrees of freedom in the early Universe, contributing to the effective number of neutrinos $N_\text{eff}$, as $\Delta N_\text{eff}\equiv N_\text{eff}-3.044\geq 0$, in light of the recent…