C Zhang,C Yu,JHL Hansen - 《IEEE Journal of Selected Topics in Signal Processing》 被引量: 18发表: 2017年 Deep Neural Network Architecture Search via Decomposition-Based Multi-Objective Stochastic Fractal Search Deep neural networks often outperform classical machine learning algorithms in solving real...
This article proposes and evaluates a Gaussian Mixture Model (GMM) represented as the last layer of a Deep Neural Network (DNN) architecture and jointly op... E Variani,E Mcdermott,G Heigold - IEEE 被引量: 25发表: 2015年 A new deep neural network based on a stack of single-hidden-lay...
UsinganalyzeNetwork, view the network architecture and locate the convolutional layers. analyzeNetwork(net) Features on Convolutional Layer 1 Setlayerto be the first convolutional layer. This layer is the second layer in the network and is named'conv1-7x7_s2'. ...
A central question in systems neuroscience is how brain-network architecture supports a wide repertoire of human behavior across the lifespan. Childhood is a period of rapid neural development and behavioral changes across cognition, personality, and mental health1,2,3. Consequently, there is particul...
The integration of computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM) systems is significantly enhanced by employing deep learning-based automatic feature recognition (AFR) methods. These methods o
Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. Cell 133, 1106–1117 (2008). Article CAS PubMed Google Scholar Azuara, V. et al. Chromatin signatures of pluripotent cell lines. Nat. Cell Biol. 8, 532–538 (2006). Article CAS PubMed...
In the simulation experiment of this article, the deep learning framework is Keras, and the back-end framework is TensorFlow; the running hardware and operating system environment are shown in Table 2. In terms of neural network architecture, 2 LSTM layers and 2 fully connected layers are used...
In an attempt to bridge the performance gap between HC features and deep representations, we built a hybrid version of each architecture. There, the HC features are concatenated with the flattened representations of each model and fed to a fusion layer before entering the final classification layer...
Figure 4.Architecture of machine learning algorithms employed in gas detection/classification: (a) ANN (2-layer structure), (b) DNN (4-layer structure), (c) 1D CNN, and (d) 2D CNN. To optimize the deep learning algorithms, the effect of the hyperparameters on the deep learning performanc...
rapidly growing field of deep learning have found that deep neural network architectures have a natural resilience to errors due to the backpropagation algorithm used in training them, and some have argued that 16-bit floating point (half precision, or FP16) is sufficient for trainingneural ...