The training of neural networks can be viewed as a problem of inference, which can be addressed from a Bayesian viewpoint. This perspective leads to a method, new to the field of particle physics, called Bayesian neural networks (BNN). After a brief overview of the method we illustrate how...
According to the method, the Bayesian neural network is implemented by using a memristor cross array, the power consumption is low, the calculation speed is high, and the calculation energy efficiency is high.WU, HUAQIANGGAO, BINLIN, YUDENG...
之后会介绍一下model-based Bayesian RL和 model-free Bayesian RL 算法。其实我觉得贝叶斯方法在RL中已经广泛运用了起来,比如policy-based method中policy函数就是一个在action空间的概率分布,而SAC就是一种max entropy的方法进行exploration和exploit的权衡。同时Prof.Sergey Levine也介绍过如何用概率的角度去理解强化学习...
是一个概率模型,Bayesian neural network是一个参数带先验分布的神经网络。即:参数是分布的神经网络。 Bayesian neural network 的概率图模型如何 inference bayesian neural network?1. variational inference 2. … Probabilistic encoder 最后一个.probabilistic encoder又叫inference network,也叫recognition model。Probabili...
We propose to perform continual learning with Bayesian neural networks and develop a new method which exploits the inherent measure of uncertainty therein to adapt the learning rate of individual parameters (Sec. 4). Second, we introduce a hard-threshold variant of our method that decides which ...
Then, utilizing the weight uncertainty introduced by conductance drift, we propose a weight optimization method based on the Bayesian neural network, which can greatly improve the network performance. Furthermore, an ensemble approach is proposed to enhance network reliability without increasing training ...
In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships. Conclusions The proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient ...
Gal finds that this method exceeds traditional variational inference methods both in terms of speed and test set performance for most tasks, with the only doubts occurring in some CNNs. He also finds it outperforms traditional methods in terms of test set performance, with the added bonus that...
We propose a Bayesian convolutional neural network built upon Bayes by Backprop and elaborate how this known method can serve as the fundamental construct of our novel reliable variational inference method for convolutional neural networks. First, we show how Bayes by Backprop can be applied to convo...
This study adopts a hierarchical Bayesian model averaging (HBMA) method to analyze prediction uncertainty resulted from uncertain components in artificial ... N Chitsazan,AA Nadiri,TC Tsai - 《Journal of Hydrology》 被引量: 27发表: 2015年 Hierarchical Bayesian neural network for gene expression tem...