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Markov Chain Hebbian Learning Algorithm With Ternary Synaptic Units
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DOI：10.1109/ACCESS.2018.2890543.png)
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摘要
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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 efficient use in memory during training in that: 1) the weight matrix has ternary elements (-1, 0, 1) and 2) each update follows a Markov chain-the upcoming update does not need past weight values. The algorithm was verified 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.
Keyword:
Greedy edge-wise training
Hebbian learning
Markov chain
mental arithmetic
prime factorization
supervised learning
ternary unit
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3.6
论文数:
9.8W
被引数:
29.4W
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