Name
Abstract | ||
|---|---|---|
Sequence learning from real-time videos is one of the hard challenges to current machine learning technologies and classic neural networks. Since existing supervised learning technologies are heavily dependent on intensive data and prior training, new methodologies for learning temporal sequences by unsupervised learning theories and technologies are yet to be developed. This paper presents the design and implementation of a novel Differential Neural Network (∇NN) for unsupervised sequence learning. The methodology is developed with a set of fundamental theories and enabling technologies for solving the problems of visual object recognition, motion detection, and visual semantic analysis in video sequence. A set of experiments on ∇NN for sequence learning is demonstrated. This work has not only led to a theoretical breakthrough to novel machine sequence learning, but also applicable to a wide range of challenging problems in computational intelligence and the AI industry. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCICC46617.2019.9146103 | 2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) |
Keywords | DocType | ISBN |
Sequence learning,neural networks (NNs),recurrent NNs,differential NNs,denotational mathematics,cognitive systems,visual sequence learning,visual knowledge base,experiments | Conference | 978-1-7281-1419-4 |
Citations | PageRank | References |
2 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingxu Wang | 1 | 3339 | 314.66 |
| Omar Zatarain | 2 | 2 | 0.35 |
| Tony Tsai | 3 | 2 | 0.35 |
| Daniel T. Graves | 4 | 9 | 4.87 |