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In simpler terms, the EUBO is a method used to help a model learn from data while also making sure it doesn't forget important things it has already learned. It does this by considering two main factors: |
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In the context of your passage, "λ" is like a knob you can turn to control how much a model trusts its own beliefs versus how much it trusts the observed data.
So, λ essentially helps you fine-tune how much your model should trust its existing beliefs versus the new data it sees. Finding the right balance is important for getting good results from your model. |
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KL -> KULLBACK-LEIBLER
KL divergence -> it is used to measure the difference between the distribution, in this case it tells us how much data has been lost.
EUBO (Expected Unnormalized Bayesian Objective): This is a measure used to evaluate how well a model is learning from data and adjusting its beliefs. It considers two main factors:
The first term is about how well the model predicts the erased data (De) given its current beliefs about the parameters (θ).
The second term measures the difference between the current beliefs (qu(θ|Dr)) and the true beliefs (p(θ|Dr)) considering the observed data (Dr).
Minimizing EUBO: When we minimize the EUBO, we're essentially trying to make the model learn better from the observed data (Dr) while also adjusting its beliefs to match the true distribution of parameters (p(θ|Dr)).
Relationship to Variational Inference (VI): Minimizing the EUBO is similar to maximizing the Evidence Lower Bound (ELBO), which is a common approach in variational inference. Both methods aim to make the model's beliefs closer to the true distribution.
Interpretation of EUBO: The EUBO represents a trade-off between two things:
Trying to learn from erased data (De), which may not be ideal because the data is no longer available.
Maintaining the model's beliefs close to the true distribution of parameters, even when only some data (Dr) is available.
Regularization: The second term in the EUBO acts as a form of regularization, preventing the model from completely forgetting the true distribution of parameters. This helps avoid drastic changes in beliefs that could lead to poor performance.
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