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Deep Learning Architecture and Applications
Xiang Zhang (editor) and Xiaoxiao Li (editor)
2023
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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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Keywords
- Abaqus Explicit
- acoustic echo cancellation
- acute myeloid leukemia
- analytical model
- ANN flow law
- ANOVA
- ARIMA
- average treatment effect
- benchmark
- Benzene
- Bioimage Analysis
- capsule network
- cheapfakes
- Classification
- CNN
- compressive measurement matrix
- Computer science
- Computer vision
- Computing & information technology
- constitutive behavior
- control tokens
- convolutional neural network
- convolutional neural network (CNN)
- convolutional neural networks
- crop/weed classification
- cyclic learning
- data augmentation
- deep forest
- deep learning
- deep-learning
- defect detection
- defect detection for X-ray images
- defect recognition
- dissolution kinetics
- duffing’s equation
- dynamic force identification
- Economics, finance, business & management
- feature extraction
- Finite element method
- finite element simulation
- Forecasting
- Fourier neural operator
- generalised additive models
- generative adversarial networks
- graph neural network
- GrC15
- human evaluation
- Hurricane
- hybrid beamforming
- Image analysis
- Image processing
- image-text matching
- Industry & industrial studies
- Information technology industries
- interpretability
- ion activity
- lithium-ion battery
- long short-term memory
- long short-term memory network
- LSTM
- Machine learning
- machine learning techniques
- massive MIMO
- Media, information & communication industries
- Misinformation
- Natural Language Processing
- nearest neighbours
- non-linear oscillators
- numerical implementation
- photoacoustic imaging
- physics informed neural network
- plausibility checks
- pooling
- prognostics
- radial return algorithm
- Reconstruction
- reinforcement learning
- residual echo suppression
- Risk factors
- RoGPT2
- routing algorithm
- self-explaining neural networks
- sensitivity analysis
- Shapley values
- Siamese networks
- simulation
- single cell cultivation
- small-shape data
- source code comments
- speech enhancement
- spring mass damper system
- storm surge
- subsurface fluid flow
- summarization
- surface defect detection
- synthetic data
- text generation
- thema EDItEUR::U Computing and Information Technology::UY Computer science
- transfer learning
- transformer encoder
- tricalcium silicate
- unsupervised learning
- uplift modelling
- variational autoencoder
- VUHARD
Links
DOI: 10.3390/books978-3-0365-8831-5Editions