Results 71 to 80 of about 2,238,851 (367)
A deep learning-based method for predicting the low-cycle fatigue life of austenitic stainless steel
Materials Research Express, 2023 In modern engineering, predicting the fatigue life of materials is crucial for safety assessment. The relationship between fatigue life and its influencing factors is difficult to predict by traditional methods, and deep learning can achieve great power ...
Hongyan Duan +5 more
doaj +1 more source
Design of an Intelligent Educational Evaluation System Using Deep Learning
IEEE Access, 2023 Nowadays, online education has been a more general demand in context of COVID-19 epidemic. The intelligent educational evaluation systems assisted by intelligent techniques are in urgent demand.
Yan Pei, Genshu Lu
doaj +1 more source
Benchmark Analysis of Representative Deep Neural Network Architectures [PDF]
IEEE Access, 2018 This paper presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition. For each DNN, multiple performance indices are observed, such as recognition accuracy, model complexity ...
S. Bianco +3 more
semanticscholar +1 more source
Search for deep graph neural networks
Information Sciences, 2023 Current GNN-oriented NAS methods focus on the search for different layer aggregate components with shallow and simple architectures, which are limited by the 'over-smooth' problem. To further explore the benefits from structural diversity and depth of GNN architectures, we propose a GNN generation pipeline with a novel two-stage search space, which ...
Guosheng Feng +2 more
openaire +2 more sources
StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity
IEEE Access, 2016 Deep neural networks are a branch in machine learning that has seen a meteoric rise in popularity due to its powerful abilities to represent and model high-level abstractions in highly complex data.
Mohammad Javad Shafiee +2 more
doaj +1 more source
Deep-learning-based data page classification for holographic memory
, 2017 We propose a deep-learning-based classification of data pages used in holographic memory. We numerically investigated the classification performance of a conventional multi-layer perceptron (MLP) and a deep neural network, under the condition that ...
Hasegawa, Satoki +11 more
core +1 more source
TextBoxes: A Fast Text Detector with a Single Deep Neural Network [PDF]
AAAI Conference on Artificial Intelligence, 2016 This paper presents an end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard non-maximum ...
Minghui Liao +4 more
semanticscholar +1 more source
Ambient backscatter communication-based smart 5G IoT network
EURASIP Journal on Wireless Communications and Networking, 2021 In this paper, we propose an ambient backscatter communication-based smart 5G IoT network. The network consists of two parts, namely a real-time data transmission system based on ambient backscatter communication and a real-time big data analysis system ...
Qiang Liu +3 more
doaj +1 more source
Spectrum-based deep neural networks for fraud detection
, 2017 In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data. We propose a novel framework that combines deep neural networks and spectral graph analysis. In particular, we use the node projection (called as
Li, Jun +3 more
core +1 more source
Deep limits of residual neural networks
Research in the Mathematical Sciences, 2022 AbstractNeural networks have been very successful in many applications; we often, however, lack a theoretical understanding of what the neural networks are actually learning. This problem emerges when trying to generalise to new data sets. The contribution of this paper is to show that, for the residual neural network model, the deep layer limit ...
Thorpe, Matthew (author) +1 more
openaire +4 more sources