Related papers: Piecewise Linear Activation Functions For More Eff…

This paper was withdrawn by arXiv administrators upon request of the Chairperson and Spokesperson of the L3 Collaboration.

Erroneous submission in violation of copyright removed by arXiv admin.

This paper has been removed by arXiv administrators because it plagiarizes gr-qc/0203084, gr-qc/0607138, gr-qc/0011087, gr-qc/0102070, gr-qc/0607138, gr-qc/0109017, gr-qc/0212018, and gr-qc/9409039.

Withdrawn by arXiv administration because authors have forged affiliations and acknowledgements, and have not adequately responded to charges [hep-th/9912039] of unattributed use of verbatim material.

This paper has been withdrawn by the author due to some errors.

Subsampling layers play a crucial role in deep nets by discarding a portion of an activation map to reduce its spatial dimensions. This encourages the deep net to learn higher-level representations. Contrary to this motivation, we…

While physics-informed neural networks (PINNs) have become a popular deep learning framework for tackling forward and inverse problems governed by partial differential equations (PDEs), their performance is known to degrade when larger and…

This paper has been withdrawn by the author.

This paper has been withdrawn by the author due a few mistakes in the paper.

Today, it is more important than ever before for users to have trust in the models they use. As Machine Learning models fall under increased regulatory scrutiny and begin to see more applications in high-stakes situations, it becomes…

This paper has been withdrawn by the author due to the incorrect application of the divergence theorem to Eqs 7, 8 and 9.

Action recognition is an important yet challenging task in computer vision. In this paper, we propose a novel deep-based framework for action recognition, which improves the recognition accuracy by: 1) deriving more precise features for…

To solve ever more complex problems, Deep Neural Networks are scaled to billions of parameters, leading to huge computational costs. An effective approach to reduce computational requirements and increase efficiency is to prune unnecessary…

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arXiv admin note: This version has been removed as the user did not have the right to agree to the license at the time of submission

Activation Functions introduce non-linearity in the deep neural networks. This nonlinearity helps the neural networks learn faster and efficiently from the dataset. In deep learning, many activation functions are developed and used based on…

This paper has been withdrawn by the author due to a crucial sign error in equation 1

This article was withdrawn by the arXiv.org administrators since it plagiarizes math.GT/0011056.

We study the complexity of functions computable by deep feedforward neural networks with piecewise linear activations in terms of the symmetries and the number of linear regions that they have. Deep networks are able to sequentially map…

This paper has been withdrawn by the author, due to errors in the figures.