Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advoc...

Bitcoin is a decentralized digital currency without central bank or single administrator sent from user to on the peer-to-peer bitcoin blockchain network intermediaries' need. In this trend analysis work, initial attributes are considered five sectors based financial, social, token, network, and that count thirteen attributes. The price, volume, market cap, mean dollar invested age, social domi...

Bayesian Neural Networks (BNNs) are trained to optimize an entire distribution over their weights instead of a single set, having significant advantages in terms of, e.g., interpretability, multi-task learning, and calibration. Because the intractability resulting optimization problem, most BNNs either sampled through Monte Carlo methods, or by minimizing suitable Evidence Lower BOund (ELBO) on...

Building on Dryden et al. (2021), this note presents the Bayesian estimation of a regression model for size-and-shape response variables with Gaussian landmarks. Our proposal fits into framework latent variable models and, potentially, allows highly flexible modelling framework.

In this paper, we present 3 different neural network-based methods to perform variable selection. OCD Optimal Cell Damage is a pruning method, which evaluates the usefulness of a variable and prunes the least useful ones (it is related to the Optimal Brain Damage method of J_.e Cun et al.). Regularization theory proposes to constrain estimators by adding a term to the cost function used to trai...

This article explores the explicative capabilities of neural network classifiers with automatic relevance determination weight regularization, and reports the findings from applying these networks for personal injury protection automobile insurance claim fraud detection. The automatic relevance determination objective function scheme provides us with a way to determine which inputs are most inf...

Nowadays, firms apply the merger and acquisition strategy for gaining synergy, increasing the wealth of stockholders, economics of scales, enhancing efficiency, increasing the ability to research and develop, developing the firm and decreasing the risk. Developing an optimized model with the ability to identify the effective variables on the merger and acquisition process has a significant ...

In a This paper reports about an application of Bayes' inferred neural network classifiers in the field of automatic sleep staging. The reason for using Bayesian learning for this task is two-fold. First, Bayesian inference is known to embody regularization automatically. Second, a side effect of Bayesian learning leads to larger variance of network outputs in regions without training data. Thi...

Neural networks are exible tools for nonlinear function approximation and by expanding the network any relevant target function can be approximated 6]. The risk of overrtting on noisy data is of major concern in neural network design 2]. By using regularization, overrtting is reduced, thereby improving generalization ability on future data. In this contribution we present a scheme for estimatio...