Delete unused files.

apikey.ini

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-[OpenAI]
-OPENAI_API_KEYS = [sk-XXX, ]
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output_file/2023-04-08-15-32-46-FedSR_ A Simple and Effective Domain Generalization Abstract References Checklist.txt

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-## Paper:1
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-Would you please tell me why does the author use conditional mutual information instead of mutual information? The author explained that conditional mutual information is not as restrictive as mutual information. I want to know more details.
-The author chose to use conditional mutual information (CMI) instead of mutual information because CMI is less restrictive and provides more flexibility in achieving domain generalization. CMI measures the amount of information shared between two variables (in this case, the representation and the data given the label) while controlling for the influence of a third variable (in this case, the label). By using CMI, the model can focus on learning only the essential information relevant to the prediction task while ignoring spurious correlations such as background noise. In contrast, mutual information measures the total amount of information shared between two variables regardless of whether it is relevant to the prediction task or not. Therefore, CMI is a more suitable choice for achieving domain generalization where the goal is to learn a representation that is invariant across domains while still capturing relevant information for the prediction task.
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-Would you please tell me why does the auther report results of 3 runs?
-The author reports the results of 3 runs to ensure the stability and reproducibility of their proposed method. By running their experiment multiple times, they can observe the variance and the consistency of their method's performance. Variance in results could be due to various reasons such as initialization, randomization, or the stochastic nature of the algorithm used. Therefore, the author performs multiple runs and report the mean and standard deviation of the results. This helps to ensure that their proposed method's performance is not a random or outlier result and is instead an accurate representation of its actual performance.