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Parametric Activation Functions for Neural Networks: A Tutorial Survey
Activation functions are pivotal in neural networks, determining the output of each neuron.
Traditionally, functions like sigmoid and ReLU have been static and deterministic. However …
Traditionally, functions like sigmoid and ReLU have been static and deterministic. However …
Short-Term Forecasting of Daily Reference Crop Evapotranspiration Based on Calibrated Hargreaves–Samani Equation at Regional Scale
This study focuses on enhancing real-time irrigation decisions and stream flow forecasts
using short-term daily forecasts of reference evapotranspiration (ETo). While conventional …
using short-term daily forecasts of reference evapotranspiration (ETo). While conventional …
[HTML][HTML] Uninorm-like parametric activation functions for human-understandable neural models
We present a deep learning model for finding human-understandable connections between
input features. Our approach uses a parameterized, differentiable activation function, based …
input features. Our approach uses a parameterized, differentiable activation function, based …
Esc-rules: Explainable, semantically constrained rule sets
M Glauer, R West, S Michie, J Hastings - ar** of linguistic variable in membership functions
A new approach for Fuzzification and Defuzzification processes of a high degree of
overlap** between the linguistic variables through proportional and relative-dynamic …
overlap** between the linguistic variables through proportional and relative-dynamic …
Parametric activation functions modelling fuzzy connectives for better explainability of neural models
In this work, a neuro-fuzzy hybrid deep learning model is presented for finding human-
readable relationships between input features with the help of nilpotent fuzzy logic and multi …
readable relationships between input features with the help of nilpotent fuzzy logic and multi …
Optimizing neural networks through activation function discovery and automatic weight initialization
Automated machine learning (AutoML) methods improve upon existing models by
optimizing various aspects of their design. While present methods focus on hyperparameters …
optimizing various aspects of their design. While present methods focus on hyperparameters …
[ספר][B] Parameterizing and aggregating activation functions in deep neural networks
LB Godfrey - 2018 - search.proquest.com
The nonlinear activation functions applied by each neuron in a neural network are essential
for making neural networks powerful representational models. If these are omitted, even …
for making neural networks powerful representational models. If these are omitted, even …
Computing with words—A framework for human-computer interaction
In this paper we explore the possibility of using computation with words (CWW) systems and
CWW-based human-computer interface (HCI) and interaction to enable efficient computation …
CWW-based human-computer interface (HCI) and interaction to enable efficient computation …
Logical activation functions: logit-space equivalents of Boolean operators
SC Lowe, R Earle, J d'Eon, T Trappenberg, S Oore - 2021 - openreview.net
Neuronal representations within artificial neural networks are commonly understood as
logits, representing the log-odds score of presence (versus absence) of features within the …
logits, representing the log-odds score of presence (versus absence) of features within the …