Research Topic

Resolution of Limitations of Deep Learning to Develop New AI Paradigms

  • Submission closed.

About this Research Topic

Deep learning is a new branch of black-box state-of-the-art machine learning algorithms that have been proven to be a powerful feature extraction tool in computer vision. However, there are some limitations of deep learning about: (1) mechanism for learning abstraction through large numbers of training ...

Deep learning is a new branch of black-box state-of-the-art machine learning algorithms that have been proven to be a powerful feature extraction tool in computer vision. However, there are some limitations of deep learning about: (1) mechanism for learning abstraction through large numbers of training examples, e.g. convolutional neural networks will face exponential inefficiencies, (2) limited capacity for transfer knowledge, (3) insufficiently transparent, i.e. opacity of black-box deep neural networks, (4) not well integrated with prior knowledge, (5) working well as a universal approximator but hard to be fully trusted. Surprisingly, little work has been conducted in relation to these limitations of deep learning. Bridging these gaps could be expected to contribute to greatly impact a variety of real-world engineering applications such as the safety and reliability of fully automated driving, medical applications such as digital pathology and radiology, and material science applications.

We welcome all types of articles. Potential topics include, but are not limited to the following:
• Use of deep learning for explainable AI,
• Perspectives on deep learning in small numbers of training examples,
• Transferring knowledge for deep learning,
• Approximation capability by deep learning with well trusted,
• Improvements of opacity of black-box deep neural networks,
• Various applications in deep learning with reduced limitations, e.g. Learning, Cognitive Science, Neuropsychology, Systems Biology, Information Theory, Digital Pathology, Radiology, Diabetology.


Keywords: Deep Learning, Transparency of deep neural networks, Explainable AI, Effective use of Deep Learning, Engineering and medical applications, Learning, Cognitive Science, Neuropsychology, Systems Biology, Information Theory, Digital Pathology, Radiology, Diabetology


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Recent Articles

Loading..

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Topic Editors

Loading..

Submission Deadlines

Submission closed.

Participating Journals

Loading..

Topic Editors

Loading..

Submission Deadlines

Submission closed.

Participating Journals

Loading..
Loading..

total views article views article downloads topic views

}
 
Top countries
Top referring sites
Loading..

Comments

Loading..

Add a comment

Add comment
Back to top