Nov 22, 2018 · We gave a new lower bound on the required target dimension for compressed multi-layer perceptron to ensure small distortion of the outputs, ...
We are interested in theoretical guarantees for classic 2-layer feed-forward neural networks with sigmoidal activation functions, having inputs linearly ...
It is shown that one can provably learn networks with arbitrarily large number of hidden units from randomly compressed data, as long as there is sufficient ...
We are interested in theoretical guarantees for classic 2-layer feed-forward neural networks with sigmoidal activation functions, having inputs linearly ...
We are interested in theoretical guarantees for classic 2-layer feed-forward neural networks with sigmoidal activation functions, having inputs.
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Tighter guarantees for the compressive multi-layer perceptron. A Kabán, Y Thummanusarn. International Conference on Theory and Practice of Natural Computing ...
Tighter Guarantees for the Compres- sive Multi-layer Perceptron. 7th International Conference on the Theory and Practice of Natural Computing (TPNC18) ...
– Benefits: accurate estimate of the gradient, convergence to local minimum is guaranteed under simpler conditions. ... Note: The hidden layer representation is ...
Tighter Guarantees for the Compres- sive Multi-layer Perceptron. 7th International Conference on the Theory and Practice of Natural Computing (TPNC18) ...
PAC-Bayes bounds give high-confidence guarantees on the expected loss of the randomised predictor. Since training is based on a surrogate loss function, ...