It turns out the interplay between architecture and feature optimizations improves the final compressed models, and the proposed method is compared favorably to existing methods, in terms of both models sizes and accuracies for a wide range of applications including image classification, image ...
The choice of architecture is often by trial and error or with Neural ... Z Huang,M Montazerin,A Srivastava 被引量: 0发表: 2024年 DualConv: Dual Convolutional Kernels for Lightweight Deep Neural Networks Convolutional neural network (CNN) architectures are generally heavy on memory and ...
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'. ...
Neural Architecture Search (NAS) usually requires to train quantities of candidate neural networks on a dataset for choosing a high-performance network architecture and optimising hyperparameters, which is very time consuming and computationally expensive. In order to resolve the issue, the authors try...
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...
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...
Magnetic RAM-based architecture could pave way for implementing neural networks on edge IoT devices There are, without a doubt, two broad technological fields that have been developing at an increasingly fast pace over the past decade: artificial intelligence (AI) and the Internet of Things (IoT)...
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...
Subsequently, VGG network18 was presented with a novel idea of utilizing a deep network with small-sized convolutional filters, and it secured second position at the ILSVRC during 2014. At this point, Szegedy et al.19 introduced the Inception architecture by staking multiple smaller convolutional ...
In this work, we propose a deep neural network architecture combining Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN). Our architecture works well for face anti-spoofing by utilizing the LSTM units' ability of finding long relation from its input sequences as well ...