白日梦 Hopfield 网络及其对相关数据的惊人功效

Ludovica Serricchio, Dario Bocchi, Claudio Chilin, Raffaele Marino, Matteo Negri, Chiara Cammarota, Federico Ricci-Tersenghi
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引用次数: 0

摘要

为了提高霍普菲尔德模型的存储容量,我们开发了一个版本的做梦算法,它可以永久强化要存储的模式(如赫伯规则),并清除虚假记忆(如做梦算法)。因此,我们称之为 "白日梦"。白日梦算法不具有破坏性,而且它可以渐近收敛到静态检索图。在对随机无相关示例进行训练时,该模型在存储示例的吸引力盆地大小和重构质量方面都表现出最佳性能。我们还在通过随机特征模型获得的相关数据上对白日梦算法进行了训练,结果表明它能自发地利用相关性,从而进一步提高了存储容量和吸引盆地的大小。此外,白日梦算法还能稳定隐藏在数据中的特征。最后,我们在 MNIST 数据集上测试了 "白日梦 "算法,结果表明它的效果仍然出人意料地好,产生的吸引子接近于未见过的示例和类原型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Daydreaming Hopfield Networks and their surprising effectiveness on correlated data
To improve the storage capacity of the Hopfield model, we develop a version of the dreaming algorithm that perpetually reinforces the patterns to be stored (as in the Hebb rule), and erases the spurious memories (as in dreaming algorithms). For this reason, we called it Daydreaming. Daydreaming is not destructive and it converges asymptotically to stationary retrieval maps. When trained on random uncorrelated examples, the model shows optimal performance in terms of the size of the basins of attraction of stored examples and the quality of reconstruction. We also train the Daydreaming algorithm on correlated data obtained via the random-features model and argue that it spontaneously exploits the correlations thus increasing even further the storage capacity and the size of the basins of attraction. Moreover, the Daydreaming algorithm is also able to stabilize the features hidden in the data. Finally, we test Daydreaming on the MNIST dataset and show that it still works surprisingly well, producing attractors that are close to unseen examples and class prototypes.
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