Markov Chain Hebbian Learning Algorithm With Ternary Synaptic Units

Title
Markov Chain Hebbian Learning Algorithm With Ternary Synaptic Units
Authors
김인호김재욱블라디미르 코르니축김구현DOHUN KIMHYO CHEON WOOJIHUN KIMCHEOL SEONG HWANGDOO SEOK JEONG
Issue Date
2019-01
Publisher
IEEE Access
Citation
VOL 7-10223
Abstract
In spite of remarkable progress in machine learning techniques, the state-of-the-art machine learning algorithms often keep machines from real-time learning (online learning) due, in part, to computational complexity in parameter optimization. As an alternative, a learning algorithm to train a memory in real time is proposed, named the Markov chain Hebbian learning algorithm. The algorithm pursues efcient use in memory during training in that: 1) the weight matrix has ternary elements (􀀀 1, 0, 1) and 2) each update follows a Markov chainthe upcoming update does not need past weight values. The algorithm was veried by two proof-of-concept tasks: image (MNIST and CIFAR-10 datasets) recognition and multiplication table memorization. Particularly, the latter bases multiplication arithmetic on memory, which may be analogous to humans' mental arithmetic. The memory-based multiplication arithmetic feasibly offers the basis of factorization, supporting novel insight into memory-based arithmetic.
URI
http://pubs.kist.re.kr/handle/201004/69556
ISSN
2169-3536
Appears in Collections:
KIST Publication > Article
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