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240202 ||| eng |
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|a 9783036588308
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|a 9783036588315
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|a books978-3-0365-8831-5
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| 100 |
1 |
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|a Zhang, Xiang
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| 245 |
0 |
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|a Deep Learning Architecture and Applications
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2023
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| 300 |
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|a 1 electronic resource (406 p.)
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| 653 |
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|a benchmark
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|a machine learning
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|a constitutive behavior
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|a physics informed neural network
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|a non-linear oscillators
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|a GrC15
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|a Convolutional Neural Network (CNN)
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|a synthetic data
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|a Shapley values
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| 653 |
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|a finite element method
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|a data augmentation
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| 653 |
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|a capsule network
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| 653 |
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|a deep learning
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|a source code comments
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| 653 |
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|a Fourier neural operator
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|a small-shape data
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|a tricalcium silicate
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|a ANOVA
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| 653 |
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|a text generation
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| 653 |
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|a dissolution kinetics
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| 653 |
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|a long short-term memory network
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| 653 |
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|a image-text matching
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|a pooling
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| 653 |
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|a unsupervised learning
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|a defect detection for X-ray images
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|a surface defect detection
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| 653 |
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|a compressive measurement matrix
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|a reinforcement learning
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|a Information technology industries / bicssc
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| 653 |
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|a generalised additive models
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| 653 |
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|a Abaqus Explicit
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| 653 |
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|a massive MIMO
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| 653 |
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|a image analysis
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| 653 |
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|a hybrid beamforming
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| 653 |
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|a convolutional neural networks
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|a transformer encoder
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|a ion activity
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| 653 |
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|a computer vision
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|a natural language processing
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|a prognostics
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| 653 |
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|a nearest neighbours
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| 653 |
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|a uplift modelling
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| 653 |
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|a crop/weed classification
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| 653 |
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|a duffing's equation
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| 653 |
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|a lithium-ion battery
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| 653 |
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|a deep forest
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| 653 |
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|a acute myeloid leukemia
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|a dynamic force identification
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|a speech enhancement
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|a risk factors
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|a analytical model
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|a benzene
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|a cyclic learning
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| 653 |
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|a forecasting
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| 653 |
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|a image processing
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| 653 |
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|a defect recognition
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|a human evaluation
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| 653 |
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|a plausibility checks
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|a ARIMA
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|a generative adversarial networks
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|a self-explaining neural networks
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| 653 |
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|a VUHARD
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| 653 |
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|a LSTM
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| 653 |
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|a radial return algorithm
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| 653 |
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|a simulation
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| 653 |
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|a storm surge
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| 653 |
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|a control tokens
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| 653 |
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|a feature extraction
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| 653 |
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|a cheapfakes
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| 653 |
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|a misinformation
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| 653 |
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|a Siamese networks
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|a transfer learning
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| 653 |
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|a residual echo suppression
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|a deep-learning
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|a summarization
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|a graph neural network
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|a photoacoustic imaging
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|a hurricane
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| 653 |
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|a spring mass damper system
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| 653 |
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|a Computer science / bicssc
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| 653 |
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|a ANN flow law
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| 653 |
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|a bioimage analysis
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| 653 |
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|a sensitivity analysis
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| 653 |
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|a routing algorithm
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| 653 |
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|a defect detection
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| 653 |
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|a variational autoencoder
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| 653 |
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|a machine learning techniques
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| 653 |
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|a finite element simulation
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| 653 |
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|a CNN
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| 653 |
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|a subsurface fluid flow
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| 653 |
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|a long short-term memory
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| 653 |
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|a acoustic echo cancellation
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| 653 |
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|a RoGPT2
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| 653 |
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|a numerical implementation
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| 653 |
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|a classification
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| 653 |
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|a convolutional neural network
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| 653 |
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|a average treatment effect
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| 653 |
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|a reconstruction
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| 653 |
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|a single cell cultivation
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| 653 |
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|a interpretability
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| 700 |
1 |
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|a Li, Xiaoxiao
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| 700 |
1 |
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|a Zhang, Xiang
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| 700 |
1 |
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|a Li, Xiaoxiao
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
|
| 024 |
8 |
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|a 10.3390/books978-3-0365-8831-5
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| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/8070
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/128618
|z DOAB: description of the publication
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|a As one of the fastest-growing topics in machine learning, deep learning algorithms have achieved unprecedented success in recent years. Novel paradigms (such as contrastive learning and few-shot learning) in deep learning and rising neural network architectures (e.g., transformer and masked autoencoder) are dramatically changing the field of data-driven algorithms. More importantly, deep learning models are redefining the next generation of industrial applications spanning image recognition, speech processing, language translation, healthcare, and other sciences. For example, recent advances in deep representation learning are allowing us to learn about protein 3D structures, which sheds new light on fundamental medicine and biology along with potentially bringing in billions of dollars (e.g., in the pharmaceutical market). This collection gathers the advanced studies of novel deep learning algorithms/frameworks and their applications in real-world scenarios. The topics cover, but are not limited to, supervised learning, explainable deep learning, finance, healthcare, and sciences.
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