Zobrazeno 1 - 10
of 5 500
pro vyhledávání: '"A. Paisley"'
Recently, a sparse version of Student-t Processes, termed sparse variational Student-t Processes, has been proposed to enhance computational efficiency and flexibility for real-world datasets using stochastic gradient descent. However, traditional gr
Externí odkaz:
http://arxiv.org/abs/2408.06699
Traditional deep Gaussian processes model the data evolution using a discrete hierarchy, whereas differential Gaussian processes (DIFFGPs) represent the evolution as an infinitely deep Gaussian process. However, prior DIFFGP methods often overlook th
Externí odkaz:
http://arxiv.org/abs/2408.06069
Bayesian Last Layer (BLL) models focus solely on uncertainty in the output layer of neural networks, demonstrating comparable performance to more complex Bayesian models. However, the use of Gaussian priors for last layer weights in Bayesian Last Lay
Externí odkaz:
http://arxiv.org/abs/2408.03746
Deep Gaussian processes (DGPs) provide a robust paradigm for Bayesian deep learning. In DGPs, a set of sparse integration locations called inducing points are selected to approximate the posterior distribution of the model. This is done to reduce com
Externí odkaz:
http://arxiv.org/abs/2407.17033
Normalizing Flows (NFs) have gained popularity among deep generative models due to their ability to provide exact likelihood estimation and efficient sampling. However, a crucial limitation of NFs is their substantial memory requirements, arising fro
Externí odkaz:
http://arxiv.org/abs/2407.04958
Deep neural networks have revolutionized many fields, but their black-box nature also occasionally prevents their wider adoption in fields such as healthcare and finance, where interpretable and explainable models are required. The recent development
Externí odkaz:
http://arxiv.org/abs/2402.12518
Recently, Gaussian processes have been used to model the vector field of continuous dynamical systems, referred to as GPODEs, which are characterized by a probabilistic ODE equation. Bayesian inference for these models has been extensively studied an
Externí odkaz:
http://arxiv.org/abs/2309.09222
Autor:
Helia Hosseini, MS, Fortunay Diatta, MD, MBE, Neil Parikh, BA, Alna Dony, MRes, Catherine T. Yu, BS, Elijah Persad-Paisley, BA, Johnny Chuieng-Yi Lu, MD, MSCI, Elspeth Jane Rose Hill, MD, PhD
Publikováno v:
Journal of Hand Surgery Global Online, Vol 6, Iss 5, Pp 766-778 (2024)
Purpose: Vascularized nerve grafts (VNGs) have been proposed as encouraging alternatives to conventional nerve grafting; however, there is ongoing debate regarding the clinical advantages of the approach compared with standard grafting. This review a
Externí odkaz:
https://doaj.org/article/15cef0b4372345b19ee3d5b24e5a8969
Several approximate inference methods have been proposed for deep discrete latent variable models. However, non-parametric methods which have previously been successfully employed for classical sparse coding models have largely been unexplored in the
Externí odkaz:
http://arxiv.org/abs/2303.08230
We introduce a novel nonlinear Kalman filter that utilizes reparametrization gradients. The widely used parametric approximation is based on a jointly Gaussian assumption of the state-space model, which is in turn equivalent to minimizing an approxim
Externí odkaz:
http://arxiv.org/abs/2303.04450