In this paper, we develop the first deep learning‐based method for particle‐based rendering, and specifically focus on photon density estimation, the core of all particle‐based methods. We train a novel deep neural network to predict a kernel function to aggregate photon contributions at ...
因此, 人群计数(Crowd Counting)或者人群密度估计(Crowd Density Estimation)是计算机视觉和智能视频监控领域的重要研究内容。 人群计数的通常的方法大致可以分为三种: 1 )行人检测: 这种方法比较直接,在人群较稀疏的场景中,通过检测视频中的每一个行人,进而得到人群计数的结果,一般是用基于外观和运动特征的boosting,贝...
密度估计(Density Estimation)在统计学和机器学习领域有着重要的应用。本文就介绍一下Density Estimation的相关技术,主要包括下面几个方面: Maximum Likelihood Neural Density Estimation Score Matching Kernel Exponential Family Deep Kernel A. Maximum Likelihoood 在刚开始接触机器学习时,可能大家都会接触到最大似然估计...
Density estimation is a challenging problem for high-dimensional data [1]. Some techniques or models have recently been developed in the framework of deep learning under the term generative modeling. Generative models are usually with likelihood-based methods, such as the autoregressive models [2, ...
The marriage of density functional theory (DFT) and deep-learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural network approach to represent the DFT Hamiltonian (DeepH) of crystalline materials, aiming to bypass the computationally dema...
Deep learning has made substantial progress in crowd density estimation, but in practical application, due to the interference factors such as uneven distribution of crowd and changes in illumination, the existing methods still have large errors in counting. To solve the above problems, a crowd dens...
DNBSEQ employs a patterned array to facilitate massively parallel sequencing of DNA nanoballs (DNBs), leading to a considerable boost in throughput. By employing the ultra-high-density (UHD) array with an increased density of DNB binding sites, the throu
Deep Learning Density Estimation Image Classification Probabilistic Deep Learning Weakly Supervised Classification Datasets Edit Add Datasets introduced or used in this paper Results from the Paper Edit Submit results from this paper to get state-of-the-art GitHub badges and help the community compare...
Keywords Density estimation Á Deep learning Á Data-driven Á Kernel density estimation Á Probability density function 1 Introduction Many data analysis problems, reaching from population analysis to computer vision [6, 28], require estimating continuous models from discrete samples. Formally, ...
In this paper, we develop density estimation methods using smoothing kernels. We use the framework of deconvoluting kernel density estimators to remove the effect of privacy-preserving noise. This approach also allows us to adapt the results from non-parametric regression with errors-in-variables ...