Datasets:
Tasks:
Text Classification
Modalities:
Image
Sub-tasks:
natural-language-inference
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English
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be407a0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | \documentclass[varwidth=598pt]{standalone}
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\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth}
\begin{tabular}{@{}lccc@{}}
\toprule
\multicolumn{1}{c}{Scheme} & val. accuracy in \% & accuracy gap in \% \\ \midrule
Baseline full batch GD & 73.10 & 8.99 \\
Gaussian & 70.58 & 8.53 \\
MPGD, $\gamma = 0.55$ & {\bf 75.97} (74.51) & 6.08 (6.58) \\
MPGD, $\gamma = 0.6$ & 75.78 (74.60) & {\bf 5.64} (6.15) \\
MPGD, $\gamma = 0.65$ & 74.39 (72.96) & 6.41 (6.85) \\
MPGD, $\gamma = 0.7$ & 73.72 (73.27) & 6.69 (6.04) \\
\bottomrule
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\caption{Table 2: ResNet-18 trained on CIFAR-10 for 1000 epochs. Here, accuracy gap = training accuracy - validation accuracy. The results in parenthesis are achieved with the variant of MPGD DS:sym. All the results are averaged over 5 models trained with different seed values.}
\end{table}
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