RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth}
\begin{tabular}{ccccccc}
\toprule
Network&DGeoLIP & NGeoLIP & LiPopt & MP & Sample & BruF \\
\midrule
2-layer/16 units   &185.18& 185.18& 259.44 & 578.54 & 175.24 & 175.24\\
2-layer/256 units    & 425.04&425.04& 1011.65& 2697.38 & 306.98 & N/A\\
8-layer/64 units per layer &8327.2 &-----& N/A& $8.237*10^7$ & 1130.6 & N/A\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Table 1: $\ell_\infty$-FGL estimation of various methods: DGeoLIP and NGeoLIP induce the same values on two layer networks. DGeoLIP always produces tighter estimations than LiPopt and MP do.}
\end{table}