Sigmoid function ≈ Sigmoid function
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Sigmoid-weighted linear units for neural network function approximation in reinforcement learning Open
In recent years, neural networks have enjoyed a renaissance as function approximators in reinforcement learning. Two decades after Tesauro's TD-Gammon achieved near top-level human performance in backgammon, the deep reinforcement learning…
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Searching for Activation Functions Open
The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although va…
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An abstract domain for certifying neural networks Open
We present a novel method for scalable and precise certification of deep neural networks. The key technical insight behind our approach is a new abstract domain which combines floating point polyhedra with intervals and is equipped with ab…
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Mish: A Self Regularized Non-Monotonic Neural Activation Function Open
The concept of non-linearity in a Neural Network is introduced by an activation function which serves an integral role in the training and performance evaluation of the network. Over the years of theoretical research, many activation funct…
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Swish: a Self-Gated Activation Function Open
The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although va…
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A Novel Sigmoid-Function-Based Adaptive Weighted Particle Swarm Optimizer Open
In this paper, a novel particle swarm optimization (PSO) algorithm is put forward where a sigmoid-function-based weighting strategy is developed to adaptively adjust the acceleration coefficients. The newly proposed adaptive weighting stra…
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The Influence of the Activation Function in a Convolution Neural Network Model of Facial Expression Recognition Open
The convolutional neural network (CNN) has been widely used in image recognition field due to its good performance. This paper proposes a facial expression recognition method based on the CNN model. Regarding the complexity of the hierarch…
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CryptoDL: Deep Neural Networks over Encrypted Data Open
Machine learning algorithms based on deep neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sen…
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An all-optical neuron with sigmoid activation function Open
We present an all-optical neuron that utilizes a logistic sigmoid activation function, using a Wavelength-Division Multiplexing (WDM) input & weighting scheme. The activation function is realized by means of a deeply-saturated differential…
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Modeling of type IV and V sigmoidal adsorption isotherms Open
Interpretation of type IV adsorption isotherms not by a composed but unified concept.
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Efficient Neural Network Robustness Certification with General Activation Functions Open
Finding minimum distortion of adversarial examples and thus certifying robustness in neural network classifiers for given data points is known to be a challenging problem. Nevertheless, recently it has been shown to be possible to give a n…
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Modelling methane production kinetics of complex poultry slaughterhouse wastes using sigmoidal growth functions Open
The kinetic evaluation of the methane potential from poultry slaughterhouse waste streams was performed using modified sigmoidal bacterial growth curve equations (Richards, logistic, Gompertz) in order to investigate their suitability to d…
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fitplc - an R package to fit hydraulic vulnerability curves Open
We describe a toolkit to fit hydraulic vulnerability curves, such as the percent loss of xylem hydraulic conductivity ('PLC curves') as a function of the water potential. The toolkit is implemented as an R package, and is thus free to use …
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Sigmoid Activation Function in Selecting the Best Model of Artificial Neural Networks Open
The aim of the research is to make predictions from the best architectural model of backpropagation neural networks. In determining the outcome in the form of a prediction model, the activation function in the artificial neural network is …
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Learning Sparse Neural Networks through $L_0$ Regularization Open
We propose a practical method for $L_0$ norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up traini…
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Multi-class Generative Adversarial Networks with the L2 Loss Function. Open
Generative adversarial networks (GANs) have achieved huge success in unsupervised learning. Most of GANs treat the discriminator as a classifier with the binary sigmoid cross entropy loss function. However, we find that the sigmoid cross e…
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A New Insight in Determining the Percolation Threshold of Electrical Conductivity for Extrinsically Conducting Polymer Composites through Different Sigmoidal Models Open
The electrical conductivity of extrinsically conducting polymer composite systems passes through a transition state known as percolation threshold. A discussion has been made on how different Sigmoidal models (S-models), such as Sigmoidal–…
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Comparison of Multiple Linear Regression, Artificial Neural Network, Extreme Learning Machine, and Support Vector Machine in Deriving Operation Rule of Hydropower Reservoir Open
Operation rule plays an important role in the scientific management of hydropower reservoirs, because a scientifically sound operating rule can help operators make an approximately optimal decision with limited runoff prediction informatio…
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Definition of the Rectum Open
Background: The wide global variation in the definition of the rectum has led to significant inconsistencies in trial recruitment, clinical management, and outcomes. Surgical technique and use of preoperative treatment for a cancer of the …
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An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer Open
Skin cancer is the most commonly diagnosed and reported malignancy worldwide. To reduce the death rate from cancer, it is essential to diagnose skin cancer at a benign stage as soon as possible. To save lives, an automated system that can …
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Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification Open
Feature selection or dimensionality reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously. Despite being a multi-objective …
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Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN Open
Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and exploding problems and hard to learn long-term patterns. Long…
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Classification of Indian Classical Music With Time-Series Matching Deep Learning Approach Open
Music is a heavenly way of expressing feelings about the world. The language of music has vast diversity. For centuries, people have indulged in debates to stratisfy between Western and Indian Classical Music. But through this paper, an un…
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Comparison of non-linear activation functions for deep neural networks on MNIST classification task Open
Activation functions play a key role in neural networks so it becomes fundamental to understand their advantages and disadvantages in order to achieve better performances. This paper will first introduce common types of non linear activati…
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SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition Open
Deep face recognition has achieved great success due to large-scale training databases and rapidly developing loss functions. The existing algorithms devote to realizing an ideal idea: minimizing the intra-class distance and maximizing the…
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Integrated Mutation Strategy With Modified Binary PSO Algorithm for Optimal PMUs Placement Open
Optimal phasor measurement units (PMUs) placement refers to the strategic placement of PMUs to achieve the full observability of power systems with a minimum number of PMUs. A strategic placement is needed because of the economic or techni…
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Quantifying the Absorption Onset in the Quantum Efficiency of Emerging Photovoltaic Devices Open
The external quantum efficiency (EQE), also known as incident‐photon‐to‐collected‐electron spectra are typically used to access the energy dependent photocurrent losses for photovoltaic devices. The integral over the EQE spectrum results i…
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Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density Open
Artificial Neural Network (ANN) is often used to solve forecasting cases. As in this study. The artificial neural network used is with backpropagation algorithm. The study focused on cases concerning overcrowding forecasting based District…
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Binary Whale Optimization Algorithm for Dimensionality Reduction Open
Feature selection (FS) was regarded as a global combinatorial optimization problem. FS is used to simplify and enhance the quality of high-dimensional datasets by selecting prominent features and removing irrelevant and redundant data to p…
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Analysis of Artificial Neural Network Backpropagation Using Conjugate Gradient Fletcher Reeves In The Predicting Process Open
Backpropagation is a good artificial neural network algorithm used to predict, one of which is to predict the rate of Consumer Price Index (CPI) based on the foodstuff sector. While conjugate gradient fletcher reeves is a suitable optimiza…