论文标题
评估人工神经网络中的情报
Assessing Intelligence in Artificial Neural Networks
论文作者
论文摘要
这项工作的目的是开发指标来评估平衡神经网络规模和任务性能的网络体系结构。为此，引入了神经效率的概念来测量神经层的利用，并创建了一个称为人工智能商（AIQ）的第二个指标，以平衡神经网络性能和神经网络效率。为了研究AIQ和神经效率，对MNIST进行了两个简单的神经网络：完全连接的网络（LENET-300-100）和一个卷积神经网络（LENET-5）。 AIQ最高的LENET-5网络精确度降低了2.32％，但包含的参数比最高精度网络少了30,912倍。发现批归归式化和辍学层都提高了神经效率。最后，高AIQ网络被证明是记忆和抗训练过度，能够学习适当的数字分类，即使75％的班级标签是随机的，精度也为92.51％。这些结果证明了AIQ和神经效率作为平衡网络性能和大小平衡的指标的实用性。
The purpose of this work was to develop of metrics to assess network architectures that balance neural network size and task performance. To this end, the concept of neural efficiency is introduced to measure neural layer utilization, and a second metric called artificial intelligence quotient (aIQ) was created to balance neural network performance and neural network efficiency. To study aIQ and neural efficiency, two simple neural networks were trained on MNIST: a fully connected network (LeNet-300-100) and a convolutional neural network (LeNet-5). The LeNet-5 network with the highest aIQ was 2.32% less accurate but contained 30,912 times fewer parameters than the highest accuracy network. Both batch normalization and dropout layers were found to increase neural efficiency. Finally, high aIQ networks are shown to be memorization and overtraining resistant, capable of learning proper digit classification with an accuracy of 92.51% even when 75% of the class labels are randomized. These results demonstrate the utility of aIQ and neural efficiency as metrics for balancing network performance and size.
