Related papers: Piecewise Linear Activation Functions For More Eff…
This article has been removed by arXiv administrators because the submitter did not have the rights to agree to the license at the time of submission
This paper has been withdrawn by the authors as they feel it inappropriate to publish this paper for the time being.
This paper has been withdrawn by the author because it needs a deep methodological revision.
This paper has been withdrawn by the author due to rewritting and skipping crucial sign errors.
This paper has been withdrawn by arXiv administrators because of disputed claims of authorship among former collaborators
Artificial neural networks typically have a fixed, non-linear activation function at each neuron. We have designed a novel form of piecewise linear activation function that is learned independently for each neuron using gradient descent.…
arXiv admin comment: This version has been removed by arXiv administrators as the submitter did not have the rights to agree to the license at the time of submission
This paper has been withdrawn by the author due to rewritting and skipping crucial sign errors.
This paper is withdrawn due to some errors, which are corrected in arXiv:0912.0071v4 [cs.LG].
There is a technical issue in the analysis that is not easily fixable. We, therefore, withdraw the submission. Sorry for the inconvenience.
This article has been withdrawn.
This submission has been withdrawn by arXiv administrators because it contains excessive and unattributed reuse of content from other authors.
The choice of activation functions is crucial for modern deep neural networks. Popular hand-designed activation functions like Rectified Linear Unit(ReLU) and its variants show promising performance in various tasks and models. Swish, the…
arXiv admin note: This submission has been removed by arXiv administrators due to unprofessional personal attack.
This paper has been withdrawn by the authour.
In exchange for large quantities of data and processing power, deep neural networks have yielded models that provide state of the art predication capabilities in many fields. However, a lack of strong guarantees on their behaviour have…
This article has been removed by arXiv administrators due to falsified authorship.
This paper has been withdrawn by the author due to an extended and largely modified version of the paper was published in arXiv (see arXiv:0807.3694, Disjoint minimal graphs).
This paper has been withdrawn because the author no longer believes the firewall argument is correct.
This paper has been withdrawn, as it should not have been a new submission. Please instead see the latest version of arXiv:0904.0436.