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On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks
A change of the prevalent supervised learning techniques is foreseeable in the near future: from the complex, computational expensive algorithms to more flexible and elementary training ones.
Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks
In the supervised learning domain, considering the recent prevalence of algorithms with high computational cost, the attention is steering towards simpler, lighter, and less computationally extensive training and inference approaches.
Perceptron Theory Can Predict the Accuracy of Neural Networks
The perceptron theory compares favorably to other methods that do not rely on training an estimator model.
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