昆虫机器人：仿生特征生成器提高了有限数据上的机器学习准确性
Insect cyborgs: Bio-mimetic feature generators improve machine learning accuracy on limited data
摘要：
机器学习（ML）分类器总是受益于更多信息输入特征。我们寻求自动生成更强大的特征集，以解决ML方法在有限的训练数据下经常遇到的困难。各种生物神经网络（BNN）擅长快速学习，这意味着它们擅长提取信息特征。因此，我们可以从BNN中寻找在这种低数据状态下提高ML性能的工具。昆虫嗅觉网络通过三个关键要素非常迅速地学习新的气味：竞争性抑制层;高维稀疏塑料层;以及突触权重的Hebbian更新。在这项工作中，我们部署了昆虫嗅觉网络的计算模型MothNet作为自动特征生成器：作为前端预处理器，它的读出神经元提供了从原始特征派生的新特征，供标准ML分类器使用。我们发现，这些“昆虫机器人”，即部分昆虫模型和部分ML方法的分类器，在矢量化的MNIST数据集上具有比单独的基线ML方法更好的性能。MothNet特征生成器的性能也大大优于其他特征生成方法，如PCA，PLS和NN，以及预训练以初始化NN权重。Cyborgs将相对测试集准确度平均提高了6%到33%，这取决于基线ML准确度，而对于基线准确度更高的ML模型，测试集错误的相对减少超过50%。这些结果表明了BNN启发的特征生成器在ML上下文中的潜在价值。
英文摘要：
Machine learning (ML) classifiers always benefit from more informative input features. We seek to auto-generate stronger feature sets in order to address the difficulty that ML methods often experience given limited training data. A wide range of biological neural nets (BNNs) excel at fast learning, implying that they are adept at extracting informative features. We can thus look to BNNs for tools to improve ML performance in this low-data regime. The insect olfactory network learns new odors very rapidly, by means of three key elements: A competitive inhibition layer; a high-dimensional sparse plastic layer; and Hebbian updates of synaptic weights. In this work, we deployed MothNet, a computational model of the insect olfactory network, as an automatic feature generator: Attached as a front-end pre-processor, its Readout Neurons provided new features, derived from the original features, for use by standard ML classifiers. We found that these "insect cyborgs", i.e. classifiers that are part-insect model and part-ML method, had significantly better performance than baseline ML methods alone on a vectorized MNIST dataset. The MothNet feature generator also substantially out-performed other feature generating methods such as PCA, PLS, and NNs, as well as pre-training to initialize NN weights. Cyborgs improved relative test set accuracy by an average of 6% to 33% depending on baseline ML accuracy, while relative reduction in test set error exceeded 50% for higher baseline accuracy ML models. These results indicate the potential value of BNN-inspired feature generators in the ML context.
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