Deep Cognitive Networks: Enhance Deep Learning by Modeling Human Cognitive Mechanism
Book information
Description
Although deep learning models have achieved great progress in vision, speech, language, planning, control, and many other areas, there still exists a large performance gap between deep learning models and the human cognitive system. Many researchers argue that one of the major reasons accounting for the performance gap is that deep learning models and the human cognitive system process visual information in very different ways. To mimic the performance gap, since 2014, there has been a trend to model various cognitive mechanisms from cognitive neuroscience, e.g., attention, memory, reasoning, and decision, based on deep learning models. This book unifies these new kinds of deep learning models and calls them deep cognitive networks, which model various human cognitive mechanisms based on deep learning models. As a result, various cognitive functions are implemented, e.g., selective extraction, knowledge reuse, and problem solving, for more effective information processing. This book first summarizes existing evidence of human cognitive mechanism modeling from cognitive psychology and proposes a general framework of deep cognitive networks that jointly considers multiple cognitive mechanisms. Then, it analyzes related works and focuses primarily but not exclusively, on the taxonomy of four key cognitive mechanisms (i.e., attention, memory, reasoning, and decision) surrounding deep cognitive networks. Finally, this book studies two representative cases of applying deep cognitive networks to the task of image-text matching and discusses important future directions. Preface Acknowledgments Contents 1 Introduction 1.1 Background 1.2 Content Organization References 2 General Framework 2.1 Overview 2.2 Attention 2.3 Memory 2.4 Reasoning 2.5 Decision 2.6 Brief Summary References 3 Attention-Based DCNs 3.1 Overview 3.2 Hard Attention 3.2.1 Sequential Attention 3.2.2 Transformable Attention 3.3 Soft Attention 3.3.1 Recurrent Attention 3.3.2 Cross-Modal Attention 3.3.3 Channel Attention 3.3.4 Self Attention 3.4 Brief Summary References 4 Memory-Based DCNs 4.1 Overview 4.2 Short-Term Memory 4.2.1 Working Memory 4.2.2 Short-Term and Long-Term Memory 4.3 Long-Term Memory 4.3.1 Episodic Memory 4.3.2 Conceptual Memory 4.3.3 Semantic Memory 4.4 Brief Summary References 5 Reasoning-Based DCNs 5.1 Overview 5.2 Analogical Reasoning 5.2.1 Memory Reasoning 5.2.2 Abstract Reasoning 5.3 Deductive Reasoning 5.3.1 Compositional Reasoning 5.3.2 Programmed Reasoning 5.4 Brief Summary References 6 Decision-Based DCNs 6.1 Overview 6.2 Normative Decision 6.2.1 Sequential Decision 6.2.2 Group Decision 6.3 Descriptive Decision 6.3.1 Emotional Decision 6.3.2 Imitative Decision 6.4 Brief Summary References 7 Conclusions and Future Trends 7.1 Conclusions 7.2 Open Problems and Future Trends References
Similar books
Context-Aware Collaborative Prediction (SpringerBriefs in Computer Science)
2018 · PDF
Architecture of Advanced Numerical Analysis Systems: Designing a Scientific Computing System using OCaml
2022 · PDF
The Oxford Handbook of Pragmatics
2017 · PDF
The Oxford Handbook of Pragmatics
2017 · EPUB
Pragmatics
2014 · EPUB
Pragmatics (Oxford Textbooks in Linguistics)
2015 · PDF
Machine Learning for Visionbased Motion Analysis
2011 · EPUB
Guidelines for Green Mine Construction and Management
2023 · PDF