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SoftMax Inc logistic (softmax) choice function
Logistic (Softmax) Choice Function, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/sigmoid+function/sigmoid++softmax++function/pmc05764197-420-7-7
Average 90 stars, based on 1 article reviews
logistic (softmax) choice function - by Bioz Stars, 2026-09
90/100 stars

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Article Title: Neural Decoding of EEG Signals with Machine Learning: A Systematic Review
Article Snippet: [ ] , 2016 , AD , Own database , WT , MC-DCNN Sigmoid and Softmax (FC) , Accuracy = 82.

Article Title: Prefrontal mechanisms combining rewards and beliefs in human decision-making
Article Snippet: Following your suggestion, we replaced equations lines 671, 745 and 757 in the original manuscript with single equations using the standard softmax sigmoid function (see revised text, Methods, computational modelling, pp.

Article Title: Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and Generalization on Clustered (non- iid ) Data
Article Snippet: Conventional autoencoder Adversarial classifier Random effects convolution block Strided conv, batch norm, PReLU Dense output layer Sigmoid/Softmax Legend Dense layer Reshape Weight sharing ... Random effects subnetwork Image cluster classification loss Latent cluster classification loss G en er al iz at io n lo ss Mixing subnetwork ... ...

Article Title: A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors
Article Snippet: The Sigmoid (Softmax) trigger function has been used for the hidden (output) layer.

Article Title: Aberrant computational mechanisms of social learning and decision-making in schizophrenia and borderline personality disorder
Article Snippet: In the response model, we used the combined belief b ( t ) ( ) in a logistic sigmoid (softmax) function to model the probability P r o b g a z e ( t ) ( ).

Article Title: Circuit mechanisms for the chemical modulation of cortex-wide network interactions and behavioral variability
Article Snippet: The LFI on trial ( t ) LFI hor was defined as LFI hor ( t ) = LI hor ( t ) LI hor ( t ) + LI ver ( t ) (19) LFI hor was transformed into the choice probability p ( c t = hor), defined with respect to the horizontal target, through a sigmoidal (softmax) function ( ) p ( c t = hor ) = e β ∙ LFI hor ( t ) e β ∙ LFI hor ( t ) + e β ∙ LFI ver ( t ) , (20) where β was the inverse temperature parameter that governed decision noise, i.e., corrupting the mapping from LFI to the behavioral choice. β ranged from 0 to infinity (no noise).

Article Title: Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and Generalization on Clustered (non- iid ) Data
Article Snippet: Conventional CNN Random effects subnetwork Adversarial classifier Bayesian random effects layer Conv, batch norm, PReLU, max-pool Dense output layer Legend Dense layer Summation Sigmoid/Softmax Generalizationloss Nonlinear slope Intercept Cluster membership Input image Fixed effect prediction Mixed effect prediction Figure S3: ARMED convolutional neural network (ARMED-CNN).

Article Title: Decision-Making with Predictions of Others' Likely and Unlikely Choices in the Human Brain
Article Snippet: 250 words; Introduction 611 words; Discussion 1499 words 58



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