Meta-learning : theory, algorithms and applications
Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI).Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve.
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| Format: | E-Book Buch |
| Sprache: | Englisch |
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London
Academic Press
2023
Elsevier Science & Technology |
| Ausgabe: | 1 |
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| ISBN: | 0323899315, 9780323899314 |
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| Abstract | Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI).Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. |
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| AbstractList | Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. Meta-Learning: Theory, Algorithms and Applications shows how meta-learning in combination with DNNs advances towards AGI. Meta-Learning: Theory, Algorithms and Applications explains the fundamentals of meta-learning by providing answers to these questions: What is meta-learning?; why do we need meta-learning?; how are self-improved meta-learning mechanisms heading for AGI ?; how can we use meta-learning in our approach to specific scenarios? The book presents the background of seven mainstream paradigms: meta-learning, few-shot learning, deep learning, transfer learning, machine learning, probabilistic modeling, and Bayesian inference. It then explains important state-of-the-art mechanisms and their variants for meta-learning, including memory-augmented neural networks, meta-networks, convolutional Siamese neural networks, matching networks, prototypical networks, relation networks, LSTM meta-learning, model-agnostic meta-learning, and the Reptile algorithm. The book takes a deep dive into nearly 200 state-of-the-art meta-learning algorithms from top tier conferences (e.g. NeurIPS, ICML, CVPR, ACL, ICLR, KDD). It systematically investigates 39 categories of tasks from 11 real-world application fields: Computer Vision, Natural Language Processing, Meta-Reinforcement Learning, Healthcare, Finance and Economy, Construction Materials, Graphic Neural Networks, Program Synthesis, Smart City, Recommended Systems, and Climate Science. Each application field concludes by looking at future trends or by giving a summary of available resources. Meta-Learning: Theory, Algorithms and Applications is a great resource to understand the principles of meta-learning and to learn state-of-the-art meta-learning algorithms, giving the student, researcher and industry professional the ability to apply meta-learning for various novel applications. Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI).Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. |
| Author | Zou, Lan |
| Author_xml | – sequence: 1 fullname: Zou, Lan |
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| Discipline | Computer Science |
| EISBN | 0323903703 9780323903707 |
| Edition | 1 |
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| ISBN | 0323899315 9780323899314 |
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| Notes | Includes bibliographical references and index |
| OCLC | 1351202841 |
| PQID | EBC7134465 |
| PageCount | 404 |
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| PublicationDate | c2023 2022 2022-11-05 |
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| Snippet | Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI).Meta-learning with... Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with... |
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| SubjectTerms | Artificial intelligence Neural networks (Computer science) |
| TableOfContents | 3.4.5. Extended algorithm 3 -- 3.5. Relation network -- 3.5.1. Background knowledge -- 3.5.2. Methodology -- C-Way one-shot -- C-Way K-shot -- C-Way zero-shot -- Objective function -- 3.6. Summary -- References -- Chapter 4: Optimization-based meta-learning approaches -- 4.1. Introduction -- 4.2. LSTM meta-learner -- 4.2.1. Background knowledge -- Covariate shift -- Batch normalization -- Long short-term memory -- Gradient-based optimization -- 4.2.2. Methodology -- Gradient independent assumption and initialization -- Meta-training and meta-testing batch normalization -- Parameter sharing -- 4.3. Model-agnostic meta-learning -- 4.3.1. Background knowledge -- Transfer learning -- Fine-tuning -- 4.3.2. Methodology -- Task adaptation -- 4.3.3. Illustration 1: Few-shot regression and few-shot classification -- 4.3.4. Illustration 2: Policy gradient reinforcement learning -- 4.3.5. Illustration 3: Meta-imitation learning -- 4.3.6. Related Algorithm 1: Meta-SGD -- 4.3.7. Related Algorithm 2: Feature reuse-The effectiveness of MAML -- 4.3.8. Related Algorithm 3: Adaptive hyperparameter generation for fast adaptation -- 4.4. Reptile -- 4.4.1. Background knowledge -- First-order model-agnostic meta-learning -- 4.4.2. Methodology -- 4.4.2.1. Serial version -- 4.4.2.2. Parallel or batch version -- The optimization assumption -- Analysis -- 4.4.3. Related Algorithm 1 -- 4.4.4. Related Algorithm 2 -- 4.4.5. Related Algorithm 3 -- 4.4.6. Related Algorithm 4 -- 4.5. Summary -- References -- Part II: Applications -- Chapter 5: Meta-learning for computer vision -- 5.1. Introduction -- 5.1.1. Limitations -- 5.2. Image classification -- 5.2.1. Introduction -- Development -- Approaches -- Benchmarks -- One-stage semisupervised learning -- One-stage unsupervised learning -- Multistage semisupervised learning 5.2.2. Decision boundary sharpness and few-shot image classification -- 5.2.3. Semisupervised few-shot image classification with refined prototypical network -- 5.2.4. Few-shot unsupervised image classification -- 5.2.5. One-shot image deformation -- 5.2.6. Heterogeneous multitask learning in image classification -- 5.2.7. Few-shot classification with transductive inference -- 5.2.8. Closed-form base learners -- 5.2.9. Long-tailed image classification -- 5.2.10. Image classification via incremental learning without forgetting -- Comparison and contrast of iTAML and reptile -- Lower bound of sample -- 5.2.11. Few-shot open set recognition -- 5.2.12. Deficiency of pretrained knowledge in few-shot learning -- 5.2.13. Bayesian strategy with deep kernel for regression and cross-domain image classification in a few-shot setting -- 5.2.14. Statistical diversity in personalized models of federated learning -- 5.2.15. Meta-learning deficiency in few-shot learning -- 5.3. Face recognition and face presentation attack -- 5.3.1. Introduction -- Facial recognition -- Face antispoofing -- 5.3.2. Person-specific talking head generation for unseen people and portrait painting in few-shot regimes -- 5.3.3. Face presentation attack and domain generalization -- 5.3.4. Anti-face-spoofing in few-shot and zero-shot scenarios -- 5.3.5. Generalized face recognition in the unseen domain -- 5.4. Object detection -- 5.4.1. Introduction -- Approaches -- Benchmarks -- 5.4.2. Long-tailed data object detection in few-shot scenarios -- 5.4.3. Object detection in few-shot scenarios -- 5.4.4. Unseen object detection and viewpoint estimation in low-data settings -- 5.5. Fine-grained image recognition -- 5.5.1. Introduction -- Approaches -- Benchmarks -- 5.5.2. Fine-grained visual categorization -- 5.5.3. One-shot fine-grained visual recognition Intro -- Meta-Learning: Theory, Algorithms and Applications -- Copyright -- Dedication -- Contents -- Preface -- Acknowledgments -- Chapter 1: Meta-learning basics and background -- 1.1. Introduction -- 1.2. Meta-learning -- 1.2.1. Definitions -- 1.2.2. Evaluation -- 1.2.3. Datasets and benchmarks -- 1.3. Machine learning -- 1.3.1. Models -- 1.3.2. Limitations -- 1.3.3. Related concepts -- 1.3.4. Further Reading -- 1.4. Deep learning -- 1.4.1. Models -- 1.4.2. Limitations -- 1.4.3. Further readings -- 1.5. Transfer learning -- 1.5.1. Multitask learning -- 1.6. Few-shot learning -- 1.7. Probabilistic modeling -- 1.8. Bayesian inference -- References -- Part I: Theory & -- mechanisms -- Chapter 2: Model-based meta-learning approaches -- 2.1. Introduction -- 2.2. Memory-augmented neural networks -- 2.2.1. Background knowledge -- 2.2.2. Methodology -- Task setup -- Memory retrieval -- Least recently used access -- 2.2.3. Extended algorithm 1 -- 2.2.4. Extended algorithm 2 -- 2.3. Meta-networks -- 2.3.1. Background knowledge -- 2.3.2. Methodology -- Slow weights and fast weights -- Layer augmentation -- 2.3.3. Main loss functions and representation loss functions -- 2.4. Summary -- References -- Chapter 3: Metric-based meta-learning approaches -- 3.1. Introduction -- 3.2. Convolutional Siamese neural networks -- 3.2.1. Background knowledge -- 3.2.2. Methodology -- Combination of the twin Siamese networks -- Objective function -- Optimization -- 3.2.3. Extended algorithm 1 -- 3.3. Matching networks -- 3.3.1. Background knowledge -- 3.3.2. Methodology -- The attention kernel -- Full context embedding -- Episode-based training -- 3.3.3. Extended algorithm 1 -- 3.4. Prototypical networks -- 3.4.1. Background knowledge -- 3.4.2. Methodology -- Bregman divergence requirement -- 3.4.3. Extended algorithm 1 -- 3.4.4. Extended algorithm 2 6.1.1. Limitations -- 6.2. Semantic parsing -- 6.2.1. Introduction -- Development -- Benchmarks -- 6.2.2. Natural language to structured query generation in few-shot learning -- Implementation -- 6.2.3. Semantic parsing in low-resource scenarios -- 6.2.4. Context-dependent semantic parser with few-shot learning -- 6.3. Machine translation -- 6.3.1. Introduction -- 6.3.2. Multidomain neural machine translation in low-resource scenarios -- 6.3.3. Multilingual neural machine translation in few-shot scenarios -- 6.4. Dialogue system -- 6.4.1. Introduction -- 6.4.2. Few-shot personalizing dialogue generation -- 6.4.3. Domain adaptation in a dialogue system -- 6.4.4. Natural language generation by few-shot learning concerning task-oriented dialogue systems -- 6.5. Knowledge graph -- 6.5.1. Introduction -- 6.5.2. Multihop knowledge graph reasoning in few-shot scenarios -- 6.5.3. Knowledge graphs link prediction in few-shot scenarios -- 6.5.4. Knowledge base complex question answering -- 6.5.5. Named-entity recognition in cross-lingual scenarios -- 6.6. Relation extraction -- 6.6.1. Introduction -- 6.6.2. Few-shot supervised relation classification -- 6.6.3. Relation extraction with few-shot and zero-shot learning -- 6.7. Sentiment analysis -- 6.7.1. Introduction -- Benchmark and dataset -- 6.7.2. Text emotion distribution learning with small samples -- 6.8. Emerging topics -- 6.8.1. Domain-specific word embedding under lifelong learning setting -- Background knowledge -- Methodology -- 6.8.2. Multilabel classification -- Background knowledge -- Methodology -- 6.8.3. Representation under a low-resource setting -- Background knowledge -- Methodology -- 6.8.4. Compositional generalization -- Background knowledge -- Methodology -- 6.8.5. Zero-shot transfer learning for query suggestion -- Background knowledge -- Methodology -- 6.9. Summary -- References 5.5.4. Few-shot fine-grained image recognition -- 5.6. Image segmentation -- 5.6.1. Introduction -- Modern development -- 5.6.2. Multiobject few-shot semantic segmentation -- 5.6.3. Few-shot static object instance-level detection -- 5.7. Object tracking -- 5.7.1. Introduction -- 5.7.2. Offline object tracking -- 5.7.3. Real-time online object tracking -- 5.7.4. Real-time object tracking with channel pruning -- One-shot channel pruning -- 5.7.5. Object tracking via instance detection -- 5.8. Label noise -- 5.8.1. Introduction -- Approaches -- Benchmarks -- 5.8.2. Reweighting examples through online approximation -- 5.8.3. Hallucinated clean representation for noisy-labeled visual recognition -- 5.8.4. Data valuation using reinforcement learning -- 5.8.5. Teacher-student networks for image classification on noisy labels -- 5.8.6. Sample reweighting function construction -- 5.8.7. Loss correction approach -- 5.8.8. Meta-relabeling through data coefficients -- 5.8.9. Meta-label correction -- 5.9. Superresolution -- 5.9.1. Introduction -- Approaches -- Datasets and benchmarks -- 5.9.2. Meta-transfer learning for zero-shot superresolution -- 5.9.3. LR-HR image pair superresolution -- 5.9.4. No-reference image quality assessment -- 5.10. Multimodal learning -- 5.10.1. Introduction -- Deep learning approaches -- Benchmarks -- 5.10.2. Visual question answering system -- 5.11. Other emerging topics -- 5.11.1. Domain generalization -- 5.11.2. High-accuracy 3D appearance-based gaze estimation in few-shot regimes -- 5.11.3. Benchmark of cross-domain few-shot learning in vision tasks -- 5.11.4. Latent embedding optimization in low-dimensional space -- 5.11.5. Image captioning -- 5.11.6. Memorization issue -- 5.11.7. Meta-pseudo label -- 5.12. Summary -- References -- Chapter 6: Meta-learning for natural language processing -- 6.1. Introduction Chapter 7: Meta-reinforcement learning |
| Title | Meta-learning : theory, algorithms and applications |
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