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Softmax function

Known as: Normalized exponential, Softmax, Softmax activation function 
In mathematics, in particular probability theory and related fields, the softmax function, or normalized exponential, is a generalization of the… Expand
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Related topics

Related topics

18 relations
Activation functionArtificial neural networkConvolutional neural networkCross entropy
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Broader (1)

Computational neuroscience

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2018
Highly Cited
2018
Additive Margin Softmax for Face Verification
In this letter, we propose a conceptually simple and intuitive learning objective function, i.e., additive margin softmax, for… Expand
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Highly Cited
2017
Highly Cited
2017
ImageNet classification with deep convolutional neural networks
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC… Expand
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Highly Cited
2017
Highly Cited
2017
Categorical Reparameterization with Gumbel-Softmax
Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks… Expand
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Highly Cited
2017
Highly Cited
2017
SphereFace: Deep Hypersphere Embedding for Face Recognition
This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have… Expand
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Highly Cited
2016
Highly Cited
2016
Convolutional Two-Stream Network Fusion for Video Action Recognition
Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different… Expand
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Highly Cited
2013
Highly Cited
2013
Distributed Representations of Words and Phrases and their Compositionality
The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector… Expand
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Highly Cited
2012
Highly Cited
2012
Acoustic Modeling Using Deep Belief Networks
Gaussian mixture models are currently the dominant technique for modeling the emission distribution of hidden Markov models for… Expand
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Highly Cited
2010
Highly Cited
2010
Softmax-Margin CRFs: Training Log-Linear Models with Cost Functions
We describe a method of incorporating task-specific cost functions into standard conditional log-likelihood (CLL) training of… Expand
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Highly Cited
2010
Highly Cited
2010
Gated Softmax Classification
We describe a "log-bilinear" model that computes class probabilities by combining an input vector multiplicatively with a vector… Expand
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Highly Cited
1989
Highly Cited
1989
Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition
  • J. Bridle
  • NATO Neurocomputing
  • 1989
    • Corpus ID: 59636530
We are concerned with feed-forward non-linear networks (multi-layer perceptrons, or MLPs) with multiple outputs. We wish to treat… Expand
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