Learning on tree architectures outperforms a convolutional feedforward network

Advanced deep learning architectures consist of tens of fully connected and convolutional hidden layers, which are already extended to hundreds, and are far from their biological realization. Their implausible biological dynamics is based on changing a weight in a non-local manner, as the number of routes between an output unit and a weight is typically large, using the backpropagation technique. Here, offline and online CIFAR-10 database learning on 3-layer tree architectures, inspired by experimental-based dendritic tree adaptations, outperforms the achievable success rates of the 5-layer convolutional LeNet. Its highly pruning tree backpropagation procedure, where a single route connects an output unit and a weight, represents an efficient dendritic deep learning.

Power-law Scaling to Assist with Key Challenges in Artificial Intelligence

Power-law scaling, a central concept in critical phenomena, is found to be useful in deep learning, where optimized test errors on handwritten digit examples converge as a power-law to zero with database size. For rapid decision making with one training epoch, each example is presented only once to the trained network, the power-law exponent increased with the number of hidden layers. For the largest dataset, the obtained test error was estimated to be in the proximity of state-of-the-art algorithms for large epoch numbers. Power-law scaling assists with key challenges found in current artificial intelligence applications and facilitates an a priori dataset size estimation to achieve a desired test accuracy. It establishes a benchmark for measuring training complexity and a quantitative hierarchy of machine learning tasks and algorithms.