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How to replicate the results shown in the paper?

Very nice work! But I'm wondering what the experimental settings are that can replicate the results shown in your paper (which is ~90% MCC performance).

If I understand it the right way, I run the following command which is exactly the settings that you described in the paper (i.e. M=40, L=1000, DimOfHiddenLayers={50, 100, 200}, #layers={3, 4, 5, 6}, lr={1e-2, 1e-3}, n=d=5, NumberOfMixingLayers=3.)

nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 50 -d 4 -l 1e-3 -c > log2.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 50 -d 5 -l 1e-3 -c > log3.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 50 -d 6 -l 1e-3 -c > log4.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 100 -d 3 -l 1e-3 -c > log5.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 100 -d 4 -l 1e-3 -c > log6.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 100 -d 5 -l 1e-3 -c > log7.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 100 -d 6 -l 1e-3 -c > log8.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 200 -d 3 -l 1e-3 -c > log9.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 200 -d 4 -l 1e-3 -c > log10.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 200 -d 5 -l 1e-3 -c > log11.txt 2>&1 &
nohup python main.py -x "1000_40_5_5_3_1_gauss_xtanh_u_f" -g 200 -d 6 -l 1e-3 -c > log12.txt 2>&1 &

They all gave me the poor results (~40% MCC), which is far from ~90%, however. Do you mind providing the exact experimental settings for reproducing the results you described in the paper?

Thanks a lot!

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