IEEE

LQC guidance law with bounded acceleration command

Abstract: A novel missile guidance law that is dependent on the conditional probability density function of the estimated states is presented. The guidance law is derived by analyzing an interception ...
IEEE

Supervised Classification of Multisensor and Multiresolution Remote Sensing Images With a Hierarchical Copula-Based Approach

Abstract: In this paper, we develop a novel classification approach for multiresolution, multisensor [optical and synthetic aperture radar (SAR)], and/or multiband images. This challenging image ...
GitHub

TorchCFM: a Conditional Flow Matching library

Conditional Flow Matching (CFM) is a fast way to train continuous normalizing flow (CNF) models. CFM is a simulation-free training objective for continuous normalizing flows that allows conditional ...
The Lancet

Association of plasma high-density lipoprotein cholesterol level with risk of incident dementia: a cohort study of healthy older adults

aSchool of Public Health and Preventive Medicine, Monash University, Victoria, 3004, Australia bDepartment of Medical Education, Melbourne Medical School, The University of Melbourne, Victoria, 3010, ...
Frontiers

Fourier Ring Correlation and Anisotropic Kernel Density Estimation Improve Deep Learning Based SMLM Reconstruction of Microtubules

Single-molecule super-resolution microscopy (SMLM) techniques like dSTORM can reveal biological structures down to the nanometer scale. The achievable resolution is not only defined by the ...
PNAS

Density estimation using deep generative neural networks

Density estimation is among the most fundamental problems in statistics. It is notoriously difficult to estimate the density of high-dimensional data due to the “curse of dimensionality.” Here, we ...
eLife

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data ...