Zobrazeno 1 - 10
of 1 361
pro vyhledávání: '"A, Särkkä"'
This paper proposes multi-target filtering algorithms in which target dynamics are given in continuous time and measurements are obtained at discrete time instants. In particular, targets appear according to a Poisson point process (PPP) in time with
Externí odkaz:
http://arxiv.org/abs/2411.19814
The immense progress in data collection and storage capacities have yielded rather complex, challenging spatial event-type data, where each event location is augmented by a non-simple mark. Despite the growing interest in analysing such complex event
Externí odkaz:
http://arxiv.org/abs/2410.16903
Autor:
Yaghoobi, Fatemeh, Särkkä, Simo
This paper presents parallel-in-time state estimation methods for systems with Slow-Rate inTegrated Measurements (SRTM). Integrated measurements are common in various applications, and they appear in analysis of data resulting from processes that req
Externí odkaz:
http://arxiv.org/abs/2410.00627
Model predictive control (MPC) is a powerful framework for optimal control of dynamical systems. However, MPC solvers suffer from a high computational burden that restricts their application to systems with low sampling frequency. This issue is furth
Externí odkaz:
http://arxiv.org/abs/2409.20027
This paper introduces the Inside-Out Nested Particle Filter (IO-NPF), a novel, fully recursive, algorithm for amortized sequential Bayesian experimental design in the non-exchangeable setting. We frame policy optimization as maximum likelihood estima
Externí odkaz:
http://arxiv.org/abs/2409.05354
In this paper, physics-informed neural network models are developed to predict the concentrate gold grade in froth flotation cells. Accurate prediction of concentrate grades is important for the automatic control and optimization of mineral processin
Externí odkaz:
http://arxiv.org/abs/2408.15267
Given an unconditional diffusion model $\pi(x, y)$, using it to perform conditional simulation $\pi(x \mid y)$ is still largely an open question and is typically achieved by learning conditional drifts to the denoising SDE after the fact. In this wor
Externí odkaz:
http://arxiv.org/abs/2405.13794
Publikováno v:
Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging
Cone-beam computed tomography (CBCT) has become a vital imaging technique in various medical fields but scatter artifacts are a major limitation in CBCT scanning. This challenge is exacerbated by the use of large flat panel 2D detectors. The scatter-
Externí odkaz:
http://arxiv.org/abs/2402.17397
In this paper, we propose a novel approach to Bayesian experimental design for non-exchangeable data that formulates it as risk-sensitive policy optimization. We develop the Inside-Out SMC$^2$ algorithm, a nested sequential Monte Carlo technique to i
Externí odkaz:
http://arxiv.org/abs/2402.07868
Gaussian processes are probabilistic models that are commonly used as functional priors in machine learning. Due to their probabilistic nature, they can be used to capture the prior information on the statistics of noise, smoothness of the functions,
Externí odkaz:
http://arxiv.org/abs/2402.00544