Unification of popular artificial neural network activation functions

• URL: http://arxiv.org/abs/2302.11007v3
• Date: Mon, 07 Oct 2024 20:45:38 GMT
• Title: Unification of popular artificial neural network activation functions
• Authors: Mohammad Mostafanejad,
• Abstract summary: We present a unified representation of the most popular neural network activation functions. Adopting Mittag-Leffler functions of fractional calculus, we propose a flexible and compact functional form.
• Score: 0.0
• License:
• Abstract: We present a unified representation of the most popular neural network activation functions. Adopting Mittag-Leffler functions of fractional calculus, we propose a flexible and compact functional form that is able to interpolate between various activation functions and mitigate common problems in training neural networks such as vanishing and exploding gradients. The presented gated representation extends the scope of fixed-shape activation functions to their adaptive counterparts whose shape can be learnt from the training data. The derivatives of the proposed functional form can also be expressed in terms of Mittag-Leffler functions making it a suitable candidate for gradient-based backpropagation algorithms. By training multiple neural networks of different complexities on various datasets with different sizes, we demonstrate that adopting a unified gated representation of activation functions offers a promising and affordable alternative to individual built-in implementations of activation functions in conventional machine learning frameworks.

Related papers

• Your Network May Need to Be Rewritten: Network Adversarial Based on High-Dimensional Function Graph Decomposition [0.994853090657971]
  We propose a network adversarial method to address the aforementioned challenges. This is the first method to use different activation functions in a network. We have achieved a substantial improvement over standard activation functions regarding both training efficiency and predictive accuracy.
  arXiv  Detail & Related papers  (2024-05-04T11:22:30Z)
• Adaptive Activation Functions for Predictive Modeling with Sparse Experimental Data [2.012425476229879]
  This study investigates the influence of adaptive or trainable activation functions on classification accuracy and predictive uncertainty in settings characterized by limited data availability. Our investigation reveals that adaptive activation functions, such as Exponential Linear Unit (ELU) and Softplus, with individual trainable parameters, result in accurate and confident prediction models.
  arXiv  Detail & Related papers  (2024-02-08T04:35:09Z)
• Fractional Concepts in Neural Networks: Enhancing Activation and Loss Functions [0.7614628596146602]
  The paper presents a method for using fractional concepts in a neural network to modify the activation and loss functions. This will enable neurons in the network to adjust their activation functions to match input data better and reduce output errors.
  arXiv  Detail & Related papers  (2023-10-18T10:49:29Z)
• Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection [50.14730810124592]
  Submodular functions and variants, through their ability to characterize diversity and coverage, have emerged as a key tool for data selection and summarization. We propose FLEXSUBNET, a family of flexible neural models for both monotone and non-monotone submodular functions.
  arXiv  Detail & Related papers  (2022-10-20T06:00:45Z)
• Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning [53.17258888552998]
  This work proposes an exploration variant of the basic $Q$-learning protocol with linear function approximation. We show that the performance of the algorithm degrades very gracefully under a novel and more permissive notion of approximation error.
  arXiv  Detail & Related papers  (2022-06-01T23:26:51Z)
• Data-Driven Learning of Feedforward Neural Networks with Different Activation Functions [0.0]
  This work contributes to the development of a new data-driven method (D-DM) of feedforward neural networks (FNNs) learning.
  arXiv  Detail & Related papers  (2021-07-04T18:20:27Z)
• A Functional Perspective on Learning Symmetric Functions with Neural Networks [48.80300074254758]
  We study the learning and representation of neural networks defined on measures. We establish approximation and generalization bounds under different choices of regularization. The resulting models can be learned efficiently and enjoy generalization guarantees that extend across input sizes.
  arXiv  Detail & Related papers  (2020-08-16T16:34:33Z)
• UNIPoint: Universally Approximating Point Processes Intensities [125.08205865536577]
  We provide a proof that a class of learnable functions can universally approximate any valid intensity function. We implement UNIPoint, a novel neural point process model, using recurrent neural networks to parameterise sums of basis function upon each event.
  arXiv  Detail & Related papers  (2020-07-28T09:31:56Z)
• A survey on modern trainable activation functions [0.0]
  We propose a taxonomy of trainable activation functions and highlight common and distinctive proprieties of recent and past models. We show that many of the proposed approaches are equivalent to adding neuron layers which use fixed (non-trainable) activation functions.
  arXiv  Detail & Related papers  (2020-05-02T12:38:43Z)
• Towards Efficient Processing and Learning with Spikes: New Approaches for Multi-Spike Learning [59.249322621035056]
  We propose two new multi-spike learning rules which demonstrate better performance over other baselines on various tasks. In the feature detection task, we re-examine the ability of unsupervised STDP with its limitations being presented. Our proposed learning rules can reliably solve the task over a wide range of conditions without specific constraints being applied.
  arXiv  Detail & Related papers  (2020-05-02T06:41:20Z)
• Invariant Feature Coding using Tensor Product Representation [75.62232699377877]
  We prove that the group-invariant feature vector contains sufficient discriminative information when learning a linear classifier. A novel feature model that explicitly consider group action is proposed for principal component analysis and k-means clustering.
  arXiv  Detail & Related papers  (2019-06-05T07:15:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.