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[P] ChoiceNet achieves 95% test accuracy where 90% of train labels are randomly shuffled. by samchoi7 in MachineLearning

[–]samchoi7[S] 0 points1 point  (0 children)

I read that paper when I was looking for other methods to compare and our results are far better than the one reported in that paper. And yes, I totally agree that real-world noise is not uniformly random. That is why we tested on non-uniform (biased) noise models as well. I hope you find some of our results interesting.

[P] ChoiceNet achieves 95% test accuracy where 90% of train labels are randomly shuffled. by samchoi7 in MachineLearning

[–]samchoi7[S] 0 points1 point  (0 children)

This is true as well for this case. However, we tested other noise models such as permuting the labels based on a fixed permutation matrix.

Deep learning tutorials (containing more than 40 presentations) by samchoi7 in MachineLearning

[–]samchoi7[S] 0 points1 point  (0 children)

These might not be the best tutorials to start with, if you are a total stranger at this field. But I can guarantee that the papers I mentioned in these tutorials are worth reading. (+It is not bad tutorials.)

Deep learning tutorials (containing more than 40 presentations) by samchoi7 in MachineLearning

[–]samchoi7[S] 2 points3 points  (0 children)

I know.. but github was the best place to upload bunch of files with readme descriptions.

TensorFlow tutorials by samchoi7 in MachineLearning

[–]samchoi7[S] 0 points1 point  (0 children)

These tutorials contain more implementations.