Questions tagged [variance]
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6 questions
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How is the variance for a diffusion kernel derived for a diffusion model?
So I'm watching this video tutorial from CVPR this year on diffusion models, and I am confused by the variance term in the distribution on the left on the video. I understand that in the forward ...
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Prooving the convergence rate of estimators (machine Learning)
I want to estimate a quantity and have two choices for estimators (they both sample from the same distribution). I suspect one of them has a higher variance and thus a slower convergence rate. I want ...
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Why my classification results are correlated with the proportionality of my data?
I'm facing a problem. I'm working on mixed data model with NN (MLP & Word Embedding). My results are not pretty good. And I observed that the proportionality of my data are corelated with my ...
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How does high entropy targets relate to less variance of the gradient between training cases?
I've been trying to understand the Distilling the Knowledge in a Neural Network paper by Hinton et al. But I cannot fully understand this:
When the soft targets have high entropy, they provide much ...
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Is there a bias-variance equivalent in unsupervised learning?
In supervised learning, bias, variance are pretty easy to calculate with labeled data. I was wondering if there's something equivalent in unsupervised learning, or like a way to estimate such things?
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user98235
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Why does this formula $\sigma^2 + \frac{1}{T}\sum_{t=1}^Tf^{\hat{W_t}}(x)^Tf^{\hat{W_t}}(x_t)-E(y)^TE(y)$ approximate the variance?
How does:
$$\text{Var}(y) \approx \sigma^2 + \frac{1}{T}\sum_{t=1}^Tf^{\hat{W_t}}(x)^Tf^{\hat{W_t}}(x_t)-E(y)^TE(y)$$
approximate variance?
I'm currently reading What Uncertainties Do We Need in ...
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