منابع مشابه
Annealed Importance Sampling for Neural Mass Models
Neural Mass Models provide a compact description of the dynamical activity of cell populations in neocortical regions. Moreover, models of regional activity can be connected together into networks, and inferences made about the strength of connections, using M/EEG data and Bayesian inference. To date, however, Bayesian methods have been largely restricted to the Variational Laplace (VL) algorit...
متن کاملNeural Implementation of Hierarchical Bayesian Inference by Importance Sampling
The goal of perception is to infer the hidden states in the hierarchical process by which sensory data are generated. Human behavior is consistent with the optimal statistical solution to this problem in many tasks, including cue combination and orientation detection. Understanding the neural mechanisms underlying this behavior is of particular importance, since probabilistic computations are n...
متن کاملQuick Training of Probabilistic Neural Nets by Importance Sampling
Our previous work on statistical language modeling introduced the use of probabilistic feedforward neural networks to help dealing with the curse of dimensionality. Training this model by maximum likelihood however requires for each example to perform as many network passes as there are words in the vocabulary. Inspired by the contrastive divergence model, we propose and evaluate sampling-based...
متن کاملBiased Importance Sampling for Deep Neural Network Training
Importance sampling has been successfully used to accelerate stochastic optimization in many convex problems. However, the lack of an efficient way to calculate the importance still hinders its application to Deep Learning. In this paper, we show that the loss value can be used as an alternative importance metric, and propose a way to efficiently approximate it for a deep model, using a small m...
متن کاملImportance Sampling
We describe an application of using a change of sampling density to get easier access to rare events during numeric simulations (this is called importance sampling). Our emphasis is on the derivation of the change of density instead of the algorithmic details. We work a small example to make the technique concrete.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ACM Transactions on Graphics
سال: 2019
ISSN: 0730-0301,1557-7368
DOI: 10.1145/3341156