A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges
IEEE Transactions on Knowledge and Data Engineering, 2020 In this modern era, traffic congestion has become a major source of severe negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate traffic congestion is through future traffic prediction.
David Alexander Tedjopurnomo +2 more
exaly +2 more sources
Distributed learning of deep neural network over multiple agents [PDF]
Journal of Network and Computer Applications, 2019 In domains such as health care and finance, shortage of labeled data and computational resources is a critical issue while developing machine learning algorithms.
Gupta, Otkrist, Raskar, Ramesh
core +2 more sources
Deep neural network in QSAR studies using deep belief network
Applied Soft Computing Journal, 2018 There are two major challenges in the current high throughput screening drug design: the large number of descriptors which may also have autocorrelations and, proper parameter initialization in model prediction to avoid over-fitting problem.
Fahimeh Ghasemi +2 more
exaly +2 more sources
A Deep Learning Approach to Predict the Flow Field and Thermal Patterns of Nonencapsulated Phase Change Materials Suspensions in an Enclosure [PDF]
Journal of Applied and Computational Mechanics, 2022 The flow and heat transfer of a novel type of functional phase change nanofluids, nano-encapsulated phase change suspensions, is investigated in the present study using a deep neural networks framework.
Mohammad Edalatifar +2 more
doaj +1 more source
Pruning and Quantization for Deep Neural Network Acceleration: A Survey [PDF]
Neurocomputing, 2021 Deep neural networks have been applied in many applications exhibiting extraordinary abilities in the field of computer vision. However, complex network architectures challenge efficient real-time deployment and require significant computation resources ...
Tailin Liang +3 more
semanticscholar +1 more source
Deep Neural Networks and An Application in Health Sciences
Van Tıp Dergisi, 2021 INTRODUCTION: Because there is more than one hidden layer between the input and output layers in the neural network algorithm, it is called "Deep Neural Networks". In the study, the Deep Neural Networks algorithm; different input (number of layers, epoch,
Sadi Elasan
doaj +1 more source
Survey on Backdoor Attacks and Countermeasures in Deep Neural Network [PDF]
Jisuanji kexue yu tansuo, 2023 The neural network backdoor attack aims to implant a hidden backdoor into the deep neural network, so that the infected model behaves normally on benign test samples, but behaves abnormally on poisoned test samples with backdoor triggers.
QIAN Hanwei, SUN Weisong
doaj +1 more source
A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Modern deep neural networks, particularly recent large language models, come with massive model sizes that require significant computational and storage resources.
Hongrong Cheng +2 more
semanticscholar +1 more source
Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting [PDF]
International Joint Conference on Artificial Intelligence, 2017 Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect ...
Ting Yu, Haoteng Yin, Zhanxing Zhu
semanticscholar +1 more source
EIE: Efficient Inference Engine on Compressed Deep Neural Network [PDF]
International Symposium on Computer Architecture, 2016 State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets.
Song Han +6 more
semanticscholar +1 more source