A new model of the central tendency towards drift in synapses – The neural networks (NN) have recently shown remarkable potential to improve the prediction performance of deep neural networks (DNNs). However, most existing neural networks models can only deal with sparse networks. We make the challenge of learning sparse model to handle high-dimensional data more difficult. This paper addresses the problem by proposing an efficient neural network architecture for the purpose of high-dimensional data analysis using a sparse network. First, we extend the classical DNN approach of learning sparse data to the new sparse network architecture that adapts to a high-dimensional data set. Then we extend the model’s learning process using data from a single low-dimensional component into a multimodal network which can learn to predict a low-dimensional dimension that it can use to estimate the prediction accuracy. Finally, we conduct an experiment where high-dimensional data from a single CNN can be used to model a high-dimensional image. The empirical test data, generated in four dimensions, are shown to be different from the previous ones, showing that the new method consistently achieves similar or better performance than the previous one.
In this paper, we present a new approach for nonlinear sequencelets with linear temporal dynamics, which we call Sequencelets on the Genetic Algorithm (SAGA). The SAGA is one of the most effective algorithms for nonlinear sequencelet in terms of its convergence to a target, hence its usefulness. We demonstrate that the SAGA’s convergence is more than that of classical algorithms, which are more accurate than alternative methods. The SAGA converges to the target in nearly all situations, even when the state space is not the full, which also contributes to its speed.
The Role of Information Fusion and Transfer in Learning and Teaching Evolution
Stochastic optimization via generative adversarial computing
A new model of the central tendency towards drift in synapses
Predicting Student’s P-Value and Gradient of Big Data from Low-Rank Classifiers
Nonlinear Sequencelets for Nonlinear Decomposable MetricsIn this paper, we present a new approach for nonlinear sequencelets with linear temporal dynamics, which we call Sequencelets on the Genetic Algorithm (SAGA). The SAGA is one of the most effective algorithms for nonlinear sequencelet in terms of its convergence to a target, hence its usefulness. We demonstrate that the SAGA’s convergence is more than that of classical algorithms, which are more accurate than alternative methods. The SAGA converges to the target in nearly all situations, even when the state space is not the full, which also contributes to its speed.