laurencebont / fact-full-grad-uva Goto Github PK
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License: Other
We will use this Github repo for the project
License: Other
Just as food for thought: Instead of measuring something like an "absolute fractional output change", one may also try to measure how much the output distribution of the network as a whole changes. I.e.: Regard the output of the network as a distribution over classes and measure how much that distribution changes if you remove pixels.
Here is a list of statistical distances; the Kullback-Leibler divergence is probably the most well-known:
I propose using snake-case, a proposed name is:
We should find a method to calculate these values, or load them from a trusted source or something. These values have an influence on the way the data is processed
Hi,
The answer is:
In any way, I just trust you to do the right thing there: Think about what would be useful for someone who wants to get to know your code, and then provide a notebook that's most useful in that regard.
She answered: "i’m not aware of any but this would be an EXCELLENT extension of the paper if that’s what they want to do."
See you on Tuesday!
Best,
Leon
Hi,
congrats, you win the contest about best implementation!
Grades will follow on Canvas (probably this Friday).
If you want, you can already do some last clean-ups to your code before it goes public on the joint codebase with all other winning teams. More details on how this works will also follow (but I don't know when).
For one of the experiments we need a module that calculates the accuracy of the model given a dataset.
Hello,
since all other TA groups have a session next Friday, which is replaced by the presentations, Ana decided that it's fairer that we have only one session next week instead of two (additional to the presentations).
Therefore, TUESDAY IS THE LAST SESSION. There is no session anymore next Wednesday. Please tell your group members so that everyone knows.
Best,
Leon
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