Questions tagged [density-estimation]
6 questions
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Understanding a GAN’s value function
I'm currently studying the original GAN objective, and I'm struggling to understand that.
$$
\min_{G}\,\max_{D}\;V(D,G)
\;=\;
\mathbb{E}_{x\sim p_{\mathrm{data}}(x)}\bigl[\log D(x)\bigr]
\;+\;
\mathbb{E}_{z\sim p_{z}(z)}\bigl[\log\bigl(1 -…
Matteo
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Geometry adaptive kernel estimation for crowd counting based on bounding box dimensions
I'm generating a ground truth density map set for an object counting task involving different object classes. The objects are labeled with bounding boxes (i.e [class, x, y, w, h]), which vary in size due to object type and camera angle. The bounding…
yuki
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How can I interpret the value returned by score(X) method of sklearn.neighbors.KernelDensity?
For sklearn.neighbors.KernelDensity, its score(X) method according to the sklearn KDE documentation says:
Compute the log-likelihood of each sample under the model
For 'gaussian' kernel, I have implemented hyper-parameter tuning for the…
Arun
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Optimize parametric Log-Likelihood with a Decision Tree
Suppose there are some objects with features, and the target is parametric density estimation. Density estimation is model-based. Parameters are obtained by maximizing log-likelihood.
$LL = \sum_{i \in I_1} \log \left( \sum_{j \in K_i} \theta_j…
nekrald
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How to estimate conditional density using neural network?
Conditional Variational Autoencoders (CVAE) and Mixture Density Networks (MDN) are supposed to address this issue. However, these models provide the distribution parameters, e.g., mean and standard deviation, for each given sample, while I need a…
user51060
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Is knowing the class of probability density function mandatory for explicit density estimation?
In deep learning, models may learn the probability distribution that generated the dataset. Observe the following paragraph from Chapter 5: Machine Learning Basics from the book titled Deep Learning (by Aaron Courville et al.)
Unsupervised learning…
hanugm
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