A survey on modern trainable activation functions

• URL: http://arxiv.org/abs/2005.00817v4
• Date: Thu, 25 Feb 2021 21:34:34 GMT
• Title: A survey on modern trainable activation functions
• Authors: Andrea Apicella, Francesco Donnarumma, Francesco Isgr\`o and Roberto Prevete
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In neural networks literature, there is a strong interest in identifying and defining activation functions which can improve neural network performance. In recent years there has been a renovated interest of the scientific community in investigating activation functions which can be trained during the learning process, usually referred to as "trainable", "learnable" or "adaptable" activation functions. They appear to lead to better network performance. Diverse and heterogeneous models of trainable activation function have been proposed in the literature. In this paper, we present a survey of these models. Starting from a discussion on the use of the term "activation function" in literature, we propose a taxonomy of trainable activation functions, highlight common and distinctive proprieties of recent and past models, and discuss main advantages and limitations of this type of approach. We show that many of the proposed approaches are equivalent to adding neuron layers which use fixed (non-trainable) activation functions and some simple local rule that constraints the corresponding weight layers.

Related papers

• Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features [115.33889811527533]
  Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks.
  arXiv  Detail & Related papers  (2024-10-04T16:05:14Z)
• Multilinear Operator Networks [60.7432588386185]
  Polynomial Networks is a class of models that does not require activation functions. We propose MONet, which relies solely on multilinear operators.
  arXiv  Detail & Related papers  (2024-01-31T16:52:19Z)
• Globally Optimal Training of Neural Networks with Threshold Activation Functions [63.03759813952481]
  We study weight decay regularized training problems of deep neural networks with threshold activations. We derive a simplified convex optimization formulation when the dataset can be shattered at a certain layer of the network.
  arXiv  Detail & Related papers  (2023-03-06T18:59:13Z)
• Evaluating CNN with Oscillatory Activation Function [0.0]
  CNNs capability to learn high-dimensional complex features from the images is the non-linearity introduced by the activation function. This paper explores the performance of one of the CNN architecture ALexNet on MNIST and CIFAR10 datasets using oscillating activation function (GCU) and some other commonly used activation functions like ReLu, PReLu, and Mish.
  arXiv  Detail & Related papers  (2022-11-13T11:17:13Z)
• Stochastic Adaptive Activation Function [1.9199289015460212]
  This study proposes a simple yet effective activation function that facilitates different thresholds and adaptive activations according to the positions of units and the contexts of inputs. Experimental analysis demonstrates that our activation function can provide the benefits of more accurate prediction and earlier convergence in many deep learning applications.
  arXiv  Detail & Related papers  (2022-10-21T01:57:25Z)
• How important are activation functions in regression and classification? A survey, performance comparison, and future directions [0.0]
  We survey the activation functions that have been employed in the past as well as the current state-of-the-art. In recent years, a physics-informed machine learning framework has emerged for solving problems related to scientific computations.
  arXiv  Detail & Related papers  (2022-09-06T17:51:52Z)
• Transformers with Learnable Activation Functions [63.98696070245065]
  We use Rational Activation Function (RAF) to learn optimal activation functions during training according to input data. RAF opens a new research direction for analyzing and interpreting pre-trained models according to the learned activation functions.
  arXiv  Detail & Related papers  (2022-08-30T09:47:31Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Evolution of Activation Functions: An Empirical Investigation [0.30458514384586394]
  This work presents an evolutionary algorithm to automate the search for completely new activation functions. We compare these new evolved activation functions to other existing and commonly used activation functions.
  arXiv  Detail & Related papers  (2021-05-30T20:08:20Z)
• Activation Functions in Artificial Neural Networks: A Systematic Overview [0.3553493344868413]
  Activation functions shape the outputs of artificial neurons. This paper provides an analytic yet up-to-date overview of popular activation functions and their properties.
  arXiv  Detail & Related papers  (2021-01-25T08:55:26Z)
• 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)

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.