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1. Visual and Camera Sensors.

Author

Park, Kang Ryoung, Kim, Euntai, Lee, Sangyoun, and Park, Kang Ryoung

Subjects
Information technology industries, 3D point clouds, BIM, CNN, DLT, DeblurGAN, DoF-dependent, Hough-space, OCD4M, PnP, VSLAM-photogrammetric algorithm, accuracy analysis, artificial intelligence, automatic online calibration, autonomous landing, blur image restoration, camera calibration, camera networks, classification, close range photogrammetry, computer vision, computer-aided diagnosis, convolutional auto-encoders, convolutional filter, convolutional neural network, covariance, deep-learning-based motion deblurring and marker detection, distortion partition, drone, ensemble learning, facial expression recognition system, flying object detection, image processing, in vivo endoscopy, lens distortion, local image patch, low-cost device, machine learning, multi-scale featured local binary pattern, multi-sensor fusion, multimodal human recognition, network slimming, open-pit mine slope monitoring, optimum deployment, pathological site classification, plank recognition, point pair feature, portable mobile mapping system, pruning model, robotic grasping, robustness, self-assembly device, semi-direct SLAM, side-rear-view monitoring system, three-dimensional reconstruction, uncertainty, unmanned aerial vehicle, unsharp masking, vision measurement, visual-inertial, weighted DLT
Abstract

Summary: This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors.

2. Approximate Bayesian Inference.

Author

Alquier, Pierre and Alquier, Pierre

Subjects
Mathematics & science, Research & information: general, Approximate Bayesian Computation, Bayesian inference, Bayesian sampling, Bayesian statistics, Bethe free energy, Edward-Sokal coupling, Gaussian, Gibbs posterior, Hamilton Monte Carlo, Kullback-Leibler divergence, Langevin Monte Carlo, Langevin dynamics, Laplace approximations, MCMC, MCMC-SAEM, Markov Chain Monte Carlo, Markov chain, Markov chain Monte Carlo, Markov kernels, Monte Carlo integration, PAC-Bayes, PAC-Bayes theory, Riemann Manifold Hamiltonian Monte Carlo, Sequential Monte Carlo, Stiefel manifold, approximate Bayesian computation, approximate Bayesian computation (ABC), bifurcation, complex systems, control variates, data imputation, data streams, deep learning, differential evolution, differential privacy (DP), discrete state space, dynamical systems, entropy, ergodicity, expectation-propagation, factor graphs, fixed-form variational Bayes, generalisation bounds, gradient descent, greedy algorithm, hyperparameters, integrated nested laplace approximation, machine learning, mean-field, message passing, meta-learning, network modeling, network variability, neural networks, no free lunch theorems, non-reversible dynamics, online learning, online optimization, particle flow, principal curves, priors, probably approximately correct, regret bounds, robustness, sequential Monte Carlo, sequential learning, sleeping experts, sparse vector technique (SVT), statistical learning theory, statistical mechanics, stochastic gradients, stochastic volatility, thinning, variable flow, variational Bayes, variational approximations, variational free energy, variational inference, variational message passing
Abstract

Summary: Extremely popular for statistical inference, Bayesian methods are also becoming popular in machine learning and artificial intelligence problems. Bayesian estimators are often implemented by Monte Carlo methods, such as the Metropolis-Hastings algorithm of the Gibbs sampler. These algorithms target the exact posterior distribution. However, many of the modern models in statistics are simply too complex to use such methodologies. In machine learning, the volume of the data used in practice makes Monte Carlo methods too slow to be useful. On the other hand, these applications often do not require an exact knowledge of the posterior. This has motivated the development of a new generation of algorithms that are fast enough to handle huge datasets but that often target an approximation of the posterior. This book gathers 18 research papers written by Approximate Bayesian Inference specialists and provides an overview of the recent advances in these algorithms. This includes optimization-based methods (such as variational approximations) and simulation-based methods (such as ABC or Monte Carlo algorithms). The theoretical aspects of Approximate Bayesian Inference are covered, specifically the PAC-Bayes bounds and regret analysis. Applications for challenging computational problems in astrophysics, finance, medical data analysis, and computer vision area also presented.