N eural networks are very powerful as nonlinear signal processors, but obtained results are often far from satisfactory. The purpose of this article is to evaluate the reasons for these frustrations and show how to make these neural networks successful. The following are the main challenges of neural network applications: 1) Which neural network architectures should be used? 2) How large should...

One of the major shortcomings of neural network as a problem solving tool lies in its opaque nature of knowledge representation and manipulation. For instance, the way that a learning algorithm modifies the connection weights of a network cannot be easily understood in the context of the application domain knowledge. Thus, the applications of neural networks is limited in areas where user’s und...

Background and goals Herbal medicine is used to treat a variety of oral health problems. Therefore, it essential comprehend fully. To determine whether the amount risky, crucial understand dosages medicinal plants. Before performing multiple linear regression (MLR) modeling, this paper uses multilayer feedforward (MLFF) neural network (NN) technique propose variable selection. A data set with s...

A decoupled sliding-mode neural network variable-bound control system (DSMNNVB) is proposed to control rotating stall and surge in jet engine compression systems in presence of disturbance and uncertainty. The control objective is to drive the systemstate to the original equilibriumpoint and it proves that ccepted 7 February 2010 vailable online 11 February 2010 eywords: xial compressors liding...

A measure known as inspection time (IT) has been shown to account for approximately 25% of the variance in peoples’ intellectual abilities, as measured by IQ. Despite this strong relationship, inspection time has had only limited success in expanding our knowledge regarding the nature of intelligence, which can be attributed to a lack of understanding of the neurophysiology underlying IT. In th...

Methods for neural network hyperparameter optimization and architecture search are computationally expensive due to the need to train a large number of model configurations. In this paper, we show that simple regression models can predict the final performance of partially trained model configurations using features based on network architectures, hyperparameters, and time-series validation per...

Abstract A common challenge encountered when using Deep Neural Network models for automatic ICD coding is their potential inability to effectively handle unseen clinical texts, especially these are only trained on a limited number of examples. This because rely solely the patterns and relationships present in training data, may not be able incorporate additional knowledge about between medical ...

In this paper, we propose novel Gradient Estimation black-box attacks to generate adversarial examples with query access to the target model’s class probabilities, which do not rely on transferability. We also propose strategies to decouple the number of queries required to generate each adversarial example from the dimensionality of the input. An iterative variant of our attack achieves close ...

We leverage recent insights from second-order optimisation for neural networks to construct a Kronecker factored Laplace approximation to the posterior over the weights of a trained network. Our approximation requires no modification of the training procedure, enabling practitioners to estimate the uncertainty of their models currently used in production without having to retrain them. We exten...

Training methods for neural networks are primarily variants on stochastic gradient descent. Techniques that use (approximate) second-order information are rarely used because of the computational cost and noise associated with those approaches in deep learning contexts. We can show that feedforward and recurrent neural networks exhibit an outer product derivative structure but that convolutiona...