【论文:即时学习深度网络——永续学习机】On-the-Fly Learning in a Perpetual Learning Machine

On-the-Fly Learning in a Perpetual Learning Machine

Andrew J.R. Simpson

(Submitted on 3 Sep 2015 (v1), last revised 8 Sep 2015 (this version, v2))

Despite the promise of brain-inspired machine learning, deep neural networks (DNN) have frustratingly failed to bridge the deceptively large gap between learning and memory. Here, we introduce a Perpetual Learning Machine; a new type of DNN that is capable of brain-like dynamic 'on the fly' learning because it exists in a self-supervised state of Perpetual Stochastic Gradient Descent. Thus, we provide the means to unify learning and memory within a machine learning framework.

尽管脑启发的机器学习的承诺，深层神经网络（DNN）都令人沮丧未能弥合学习和记忆之间的欺骗性很大的差距。在这里，我们介绍一个永久的学习机;一种新型DNN的是能够脑般的动感'飞'的学习，因为它存在于永久随机梯度下降的自我监管的状态。因此，我们提供给机器学习框架内统一的学习和记忆的手段。（google翻译）

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