divergence loss
[/daɪˈvɜːrdʒəns lɔːs/]
nounpl: divergence losses
perda de divergência
1. A measure in machine learning and statistics that quantifies the difference or distance between two probability distributions or data sets
The divergence loss function is commonly used in training neural networks to minimize the difference between predicted and actual distributions.
A função de perda de divergência é comumente usada no treinamento de redes neurais para minimizar a diferença entre distribuições previstas e reais.
2. In information theory, a metric used to measure how one probability distribution differs from a reference probability distribution
Kullback-Leibler divergence loss is a popular choice for comparing probability distributions in machine learning models.
A perda de divergência de Kullback-Leibler é uma escolha popular para comparar distribuições de probabilidade em modelos de aprendizado de máquina.
3. The quantified error or penalty resulting from distributions that fail to converge or align properly
Minimizing the divergence loss ensures that the model's output distribution matches the target distribution more closely.
Minimizar a perda de divergência garante que a distribuição de saída do modelo corresponda mais de perto à distribuição alvo.
Divergence loss is a technical term predominantly used in academic and professional machine learning communities in both the USA and Brazil. It is part of the specialized vocabulary in artificial intelligence research and is used identically across English and Portuguese-speaking technical communities, reflecting the globalized nature of AI research and development.
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