کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4946664 1439411 2017 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
2017 Special IssueDual-memory neural networks for modeling cognitive activities of humans via wearable sensors
ترجمه فارسی عنوان
شبکه های عصبی دوگانه حافظه 2017 برای مدل سازی فعالیت های شناختی انسان از طریق حسگرهای پوشیدنی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Wearable devices, such as smart glasses and watches, allow for continuous recording of everyday life in a real world over an extended period of time or lifelong. This possibility helps better understand the cognitive behavior of humans in real life as well as build human-aware intelligent agents for practical purposes. However, modeling the human cognitive activity from wearable-sensor data stream is challenging because learning new information often results in loss of previously acquired information, causing a problem known as catastrophic forgetting. Here we propose a deep-learning neural network architecture that resolves the catastrophic forgetting problem. Based on the neurocognitive theory of the complementary learning systems of the neocortex and hippocampus, we introduce a dual memory architecture (DMA) that, on one hand, slowly acquires the structured knowledge representations and, on the other hand, rapidly learns the specifics of individual experiences. The DMA system learns continuously through incremental feature adaptation and weight transfer. We evaluate the performance on two real-life datasets, the CIFAR-10 image-stream dataset and the 46-day Lifelog dataset collected from Google Glass, showing that the proposed model outperforms other online learning methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 92, August 2017, Pages 17-28
نویسندگان
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