Deep learning appears to be a key magical ingredient for the realization of many artificial intelligence tasks. However, these tasks can be efficiently realized by the use of simpler shallow architectures. Shallow feedforward networks can efficiently learn non-trivial classification tasks with re
Recent research demonstrates that brain-inspired shallow feedforward networks can efficiently learn non-trivial classification tasks with reduced computational complexity, compared to deep learning architectures. It showed that shallow architectures can achieve the same classification success rates as deep learning architectures, but with less complexity.
Deep learning appears to be a key magical ingredient for the realization of many artificial intelligence tasks. However, these tasks can be efficiently realized by the use of simpler shallow architectures. Shallow feedforward networks can efficiently learn non-trivial classification tasks with reduced computational complexity compared to deep learning architectures, according to research published in. This finding may direct the development of unique, energy-efficient hardware for shallow learning.
The earliest artificial neural network, the Perceptron, was introduced approximately 65 years ago and consisted of just one layer. However, to address solutions for more complex classification tasks, more advanced neural network architectures consisting of numerous feedforward layers were later introduced. This is the essential component of the current implementation of deep learning algorithms.
Scheme of Deep Machine Learning consisting of many layers vs. Shallow Brain Learning consisting of a few layers with enlarged width . Credit: Prof. Ido Kanter, Bar-Ilan Universityis whether efficient learning of non-trivial classification tasks can be achieved using brain-inspired shallow feedforward networks, while potentially requiring less computational complexity.
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