Auxiliary gradient-based sampling al- gorithms. Journal of the Royal Statistical Society: Series B (Statistical Methodol- ogy) 80 (4), 749-767.Titsias, M. K. and O. Papaspiliopoulos (2018). Auxiliary gradient-b
Cancer recognition is frequently applied in medical diagnostic analysis and has attracted keen interest from researchers. Deep learning-based methods have made significant advances in cancer recognition. Hou et al. [9] investigated an improved deep convolutional neural network model based on data augmenta...
Auxiliary models based multi-innovation generalized extended stochastic gradient algorithms; 基于辅助模型的多新息广义增广随机梯度算法 2. Corresponding auxiliary models are obtained in the meaning of different criteria. 提出了一类区间值模糊线性规划问题 ,讨论了区间值模糊数的排序方法 ,在不同的排序准则下获...
If this value is too small it is determined on step 5 of the above algorithms and the K value is increased. Though any initial value of auxiliary parameter \(V_{0}\) may be used, we used such a value that satisfies (15). It can be easily estimated on a pre-computing step before...
Stochastic Gradient Descent with Momentum is used to develop the Convolutional Neural Network (CNN). To classify images, the system comprises a neural network, three recurrent as well as averaging convolution layers, and a softmax densely integrated output neuron. This article with ischemic stroke ...
Extending Gal's work [11], we have experimented with auxiliary regularization to recent reinforcement algorithms, Deep Deterministic Policy Gradient (DDPG) [12] and Trust Region Policy Optimization (TRPO) [13] and the method has outperformed the standard neural networks, achieving higher rewards in ...
Based on the non-uniform input-output dataset, an auxiliary model-based stochastic gradient (AM-SG) algorithm is developed in this paper to estimate the parameters of Hammerstein systems. To improve the identification performance of the AM-SG algorithm, an auxiliary model-based multi-innovation stoc...
The whole process needs a model of the entire gridded/topology map and includes search-based algorithms (Dijkstra [14], A* [15], D* [16]) and sampling-based algorithms (RRT [17], RPM [18]). The artificial potential field method uses local environmental information obtained by airborne ...
Godinho, S.; Guiomar, N.; Gil, A. Using a stochastic gradient boosting algorithm to analyse the effectiveness of Landsat 8 data for montado land cover mapping: Application in southern Portugal.Int. J. Appl. Earth Obs. Geoinf.2016,49, 151–162. [Google Scholar] [CrossRef] ...
The solution to the above linear system can be approximated using iterative methods such as conjugate gradient algorithms, leading to an approximate sample of the sought distribution [30,31]. This issue has been considered in [32] by adding a Metropolis step in the sampling algorithm. In [24,...