Sequence recognition with spatio-temporal long-term memory organization
In this work, we propose a connectionist memory structure for spatio-temporal sequence learning and recognition inspired by the Long-Term Memory structure of human cortex. Besides symbolic data, our framework is able to continuously process real-valued multi-dimensional data stream. This capability...
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| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/98284 http://hdl.handle.net/10220/12399 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | In this work, we propose a connectionist memory structure for spatio-temporal sequence learning and recognition inspired by the Long-Term Memory structure of human cortex. Besides symbolic data, our framework is able to continuously process real-valued multi-dimensional data stream. This capability is made possible by addressing three critical problems in spatio-temporal learning, namely error tolerance, significance of sequence's elements and memory forgetting mechanism. We demonstrate the potential of the framework with a synthetic example and a real world example, namely the task of hand-sign language interpretation with the Australian Sign Language dataset. |
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