The negative log-likelihoodL(w,b∣z)L(w,b∣z)is then what we usually call thelogistic loss. Note that the same concept extends to deep neural network classifiers. The only difference is that instead of calculatingzzas the weighted sum of the model inputs,z=wTx+bz=wTx+b, we calculate...
Compute the negative log likelihood for a sample.params
{W \times H} is a confidence map, also estimated by the nerwork \Phi from the image \mathbf{I} , which expresses the aleatoric uncertainty of the model.(表示了模型的任意不确定性)The loss can be interpreted as the negative log-likelihood of a factorized Laplacian distribution on the ...
Joint optimization of the auto-encoder and the latent density estimator is pursued via a formulation which learns both by minimizing a combination of the negative log-likelihood in the latent domain and the auto-encoder reconstruction loss. We demonstrate that the proposed model achieves very promisin...
free energy, which consists of two terms. The first term is the Kullback–Leibler divergence, which measures the complexity of the learned distribution against the Gaussian prior distribution. The second term is the negative log-likelihood, which measures the error with respect to the training ...
In this section we introduce our model functions and define the corresponding joint negative log-likelihood of the data and the random parameters. In addition, we express the negative log-likelihood of the data alone as an integral with respect to the random parameters. We denote an arbitrary va...
Maximum likelihood estimate for the dispersion parameter of the negative binomial distribution This paper shows that the maximum likelihood estimate (MLE) for the dispersion parameter of the negative binomial distribution is unique under a certain co... H Dai,Y Bao,M Bao - 《Statistics & Probabilit...
The fitting procedure was based on the minimization of a cost function, known as the logloss (aka logarithmic loss or cross-entropy loss), using a gradient descent method. The logloss is the normalized negative log-likelihood of the true responses, given the probabilistic outcomes of the model...
In the original algorithm, the number of clusters q* is determined by the Bethe free energy, which is equivalent to the approximate negative marginalised log-likelihood; among the models of different (maximum) numbers of clusters, the most parsimonious model with low Bethe free energy is ...
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