Datasets:
id stringlengths 4 8 | image imagewidth (px) 4 1.34k | latex stringlengths 90 2.83k |
|---|---|---|
CV-26906 | \begin{tabular}{|l|c|c|}
\hline
Model & Test Accuracy & Perplexity \\
\hline
Nearest Neighbor & $14.09$ & \texttt{N/A} \\
\hline
CNN & $14.61$ & $0.1419$ \\
\hline
Our Model & $\mathbf{19.77}$ & $\mathbf{0.2362}$ \\
\hline
\end{tabular} | |
AI-8358 | \begin{tabular}{|p{50pt}|p{50pt}<{\centering}|p{50pt}<{\centering}|p{50pt}<{\centering}|}
\hline
& \textbf{Types} & \textbf{HAS} & \textbf{HAS+r} \\ \hline
\textbf{L.MDB} & Film & 0.38 & \textbf{0.44} \\ \hline
\multirow{15}{*}{\textbf{DBpedia}} & Airl. & 0.402 & \textbf{0.424} \\
& Band & 0.26 & \textbf{0.56} \... | |
CV-10464 | \begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|}
\hline
\multirow{3}*{Method} & \multicolumn{6}{c|}{IoU = 0.5} & \multicolumn{6}{c|}{IoU = 0.7} \\
\cline{2-13}
~ & \multicolumn{2}{c|}{ Easy} & \multicolumn{2}{c|}{Moderate} & \multicolumn{2}{c|}{Hard} & \multicolumn{2}{c|}{ Easy} & \multicolumn{2}{c|}{Moderate} & \multi... | |
SE-20655 | \begin{tabular}[c]{@{}l@{}}a)DifferentorganizationsgenerateSBOMsatdifferentSDLCstages.\\b)MoreorganizationsfavorincludingmorethanbaselineSBOMinformation.\end{tabular} | |
CV-28515 | \begin{tabular}{l|c|c|c|c}
\hline
Benchmark & Easy & Moderate & Hard & mAP \\
\hline
Cars~(3D Detection) & 88.21 & 77.85 & 75.62 & 80.56 \\
Cars~(BEV Detection) & 90.17 & 87.55 & 87.14 & 88.29 \\
Pedestrians~(3D Detection) & 70.80 & 63.45 & 58.22 & 64.16 \\
Pedestrians~(BEV Detection) & 76.70 & 70.76 & 65.13 & 7... | |
AI-22172 | \begin{tabular}{rl|rl|rl|rl|rl}
Freq. & Word & Freq. & Word & Freq. & Word & Freq. & Word & Freq. & Word \\
\hline
3,205 & sport & 464 & use & 296 & carrying & 155 & crossing & 92 & popular \\
1,153 & appear & 461 & chair & 287 & face & 150 & tires & 92 & planning \\
976 & fire & 430 & parked & 283 & writing & 148... | |
CR-44831 | \begin{tabular}{ccc}
\hline
Notations & Description & Time (ms) \\
\hline
${T_C}$ & Time cost of encryption & 0.096 \\
${T_{agg}}$ & Time cost of aggregating 200 readings & 2.21 \\
${T_{decAgg}}$ & Time cost of decrypting aggregated readings & 0.135 \\
${T_{DM}}$ & Time cost of public key generation $DW$ & 45.36... | |
SE-22997 | \begin{tabular}{lllll}
\hline\hline
program & size(KLOC) & Times(secs) & Bug Count & False Count \\
\hline
gcc & 230.4 & 213.1 & 36 & 6 \\
ammp & 13.4 & 10.4 & 23 & 5 \\
bash & 100.0 & 90.1 & 16 & 3 \\
mesa & 61.3 & 48.6 & 9 & 8 \\
cluster & 10.7 & 9.5 & 12 & 4 \\
openCV & 794.6 & 756.8 & 74 & 11 \\
bitcoin &... | |
CR-29638 | \begin{tabular}{ccccc}
\toprule
Image size & $1024\times 768$ & $1600\times 1200$ & $3240\times 2592$ & $4800\times 4800$ \\
\hline
DCT on laptop GPU & 0.41 ms & 0.79 ms & 3.67 ms & 9.98 ms \\
AES on laptop CPU & 0.19 ms & 0.47 ms & 2.05 ms & 5.87 ms \\
\bottomrule
\end{tabular} | |
AI-29454 | \begin{tabular}{|p{0.30\textwidth}|p{0.275\textwidth}|p{0.28\textwidth}|}
\hline
\textbf{Dynamic conbditions} & \textbf{Action} & \textbf{Static Conditions} \\ \hline
\makecell*[lt]{$robAt(R1)$} & \makecell*[lt]{$moveTo(R1,R0,D0)$} & \makecell*[lt]{$connected(D0,R0,R1)$} \\ \hline
\makecell*[lt]{$isHeld(K,G)$} & \m... | |
SE-4760 | \begin{tabular}{lccc}
Action class & Mean LOC & Formula & P(class) \\
\hline
\vspace{0.01in}
{\tt <int> := <[1..20]>} & 0 & $\frac{0.20}{2}$ & 0.100 \\
{\tt <ch> := <['r','w']>} & 0 & $\frac{0.20}{2}$ & 0.100 \\
\vspace{0.03in}
{\tt f(<int>)} & 30 & $\frac{30}{64} \times 0.80$ & 0.375 \\
{\tt g(<int>)} & 20 & $... | |
CR-12294 | \begin{tabular}{|c|c|c|c|c|}
\hline
Vulnerability & Arbiter & MEM & GNG & AES \\
\hline
Permissions and Privileges & & \checkmark & & \\
\hline
Resource Management & & & & \checkmark \\
\hline
Illegal States \& Transitions & \checkmark/\checkmark & \checkmark & & \\
\hline
Buffer Issues & & \checkmark & & \\
... | |
CR-40148 | \begin{tabular}{llcccccc}
\hline
\textbf{Label} & \textbf{Person} & \textbf{Sex} & \textbf{Language} & \textbf{Length(seconds)} & \textbf{Testing words} & \textbf{Training words} & \textbf{Overlapping words} \\ \hline \hline
User$_1$ & Bill Gates & male & English & 7068 & 179 & 12593 & 19 \\ \hline
User$_2$ & Feife... | |
AI-6294 | \begin{tabular}{|l|l|l|}
\hline
\multirow{2}{*}{Pretraining} & \# of Stays in Stay Level Pretraining & 100563 \\
& \# of Admissions in Admission Level Pretraining & 99000 \\ \hline
\multirow{2}{*}{Stay Level Tasks} & \# of Stays in ARF Prediction & 4205 \\
& \# of Stays in Shock Prediction & 6190 \\ \hline
Admi... | |
AI-935 | \begin{tabular}{lrrrrrr}
\hline
& Sum Sq & Mean Sq & NumDF & DenDF & F value & Pr($>$F) \\
\hline
transparency & 0.10 & 0.10 & 1.00 & 994.00 & 5.83 & 0.0159 \\
num\_features & 0.04 & 0.04 & 1.00 & 994.00 & 2.15 & 0.1427 \\
transparency:num\_features & 0.00 & 0.00 & 1.00 & 994.00 & 0.06 & 0.8143 \\
\hline
\end{t... | |
AI-27264 | \begin{tabular}{l|l|l|l|l}
\toprule
\makecell[l]{Engage- \\ment} & \makecell[l]{Question\\difficulty} & $P_{Rresp}$ & $P_{IRresp}$ & $P_{Nresp}$ \\
\midrule
\multirow{3}{*}{High} & Easy & $1$ & $0$ & $0$ \\
{} & Moderate & $1$ & $0$ & $0$ \\
{} & Difficult & $1$ & $0$ & $0$ \\
\hline
\multirow{3}{*}{Medium} & E... | |
CR-40936 | \begin{tabular}{|cc|}
\hline
\multicolumn{2}{|c|}{A2Y} \\
\hline
local & cloud \\
\hline \hline
0 & 0 \\
\hline
\end{tabular} | |
SE-6501 | \begin{tabular}{@{}p{65mm}@{}}
\emph{Project: avajs/ava; Issue: $1400$} \\
``... There is already a PR for this though, thanks
to @tdeschryver ...''
\end{tabular} | |
CR-10565 | \begin{tabular}{lrrrrrr}
\multicolumn{1}{c}{Dataset} & \multicolumn{3}{c}{CIFAR-10} & \multicolumn{3}{c}{CIFAR-100} \\
\cmidrule(lr){1-1}
\cmidrule(lr){2-4}
\cmidrule(lr){5-7}
Defense level & No Def. & Mixup+MMD & Mem-Guard & No Def. & Mixup+MMD & Mem-Guard \\
\cmidrule(lr){1-4}
\cmidrule(lr){4-7}
Training accu... | |
PL-1297 | \begin{tabular}{@{}p{7em}cccccp{6em}@{}}
Unrelated & $\bigcirc$ & $\bigcirc$ & $\bigcirc$ & $\bigcirc$ & $\bigcirc$ & Related \\
\end{tabular} | |
AI-522 | \begin{tabular}{|l|c|c|c|}
\hline
Variant & hit@30 & Mean Rank & Mean Percentile\tabularnewline
\hline
\hline
Original & 0.368 & 1298.44 & 92.70\tabularnewline
\hline
Relation-weighted & \textbf{0.375} & \textbf{1186.81} & \textbf{93.32}\tabularnewline
\hline
\end{tabular} | |
CV-2727 | \begin{tabular}{lccclll}
& \multicolumn{3}{c}{Dice} & \multicolumn{3}{c}{HD95} \\
\multicolumn{1}{c}{} & enh. & whole & core & enh. & whole & core \\
\hline
Isensee et al. (2017) & 70.69 & 89.51 & 82.76 & 6.24 & 6.04 & 6.95 \\
baseline & 73.43 & 89.76 & 82.17 & 4.88 & 5.86 & 7.11 \\
baseline + reg & 73.81 & 90.0... | |
CR-36686 | \begin{tabular}{ccccc}
\toprule
\textbf{} & \textbf{Time(s)} & \textbf{Space(KB)} & \textbf{$T_{total}\Delta$Acc} & \textbf{$T_{total}\Delta$Loss} \\ \midrule
$^v$BN & \cellcolor[HTML]{67000d}\color{white}263.5 & \cellcolor[HTML]{fff5f0}4 & \cellcolor[HTML]{f7f6f6} -0.53 & \cellcolor[HTML]{f7f5f4} 0.02 \\
$^v$ME & ... | |
AI-39429 | \begin{tabular}{ccc}
\toprule
\multicolumn{1}{c}{\multirow{1}[1]{*}{\textbf{Shorthand}}} & \multirow{1}[1]{*}{$\mathcal{T}_\text{train}$} & \multicolumn{1}{c}{\multirow{1}[1]{*}{$\mathcal{T}_\text{test}$}} \\
\midrule
\textit{random} & $100$ random & $20$ random \\
\textit{non-cls} & $35$ non-cls. & $42$ non-cls.(... | |
AI-24546 | \begin{tabular}{l|c|c|c}
\hline
& $P_1$ & $P_2$ & $P_3$ \\
\hline
Accuracy & 99.6 & 99.6 & 100 \\
\hline
\end{tabular} | |
CR-46597 | \begin{tabular}{|c|c|c|c|c|c|}
\hline
& ANN & SVM & NBC & Random Forest & Average \\
\hline
Precision & 0.9985 & 0.9833 & 0.9937 & \textbf{0.9987} & 0.9936 \\
\hline
Recall & 0.9112 & \textbf{0.9339} & 0.8537 & 0.9084 & 0.9018 \\
\hline
F1-Score & 0.9529 & \textbf{0.9579} & 0.9185 & 0.9514 & 0.9452 \\
\hline
... | |
SE-25170 | \begin{tabular}[l]{@{}l@{}}\textit{``Promotingwomentoseniorjobsandleadershipwouldhelpyoungertalentstoidentify}\\\textit{themselveswiththecompany,givingthemconfidenceandmoreprospectsofcontinuingtheir}\\\textit{careerinthecompany"}(S65)
\end{tabular} | |
PL-1837 | \begin{tabular}{lr}
\toprule
\textbf{Category} & \textbf{\#Apps Studied} \\ \midrule
Banking & 6 \\
Business & 10 \\
Education & 8 \\
Entertainment & 16 \\
Health & 10 \\
Online Payments & 25 \\
Music & 13 \\
News & 19 \\
Shopping & 17 \\
Social & 11 \\
Top Grossing & 30 \\
Top Apps & 22 \\
Travel & 9 \\... | |
SE-23461 | \begin{tabular}{lcccccccccc}
& KNN & LNR & SVR & RFT & CART & RDCART & GSCART & FLASH & DECART & ASKL \\
commit & \cellcolor[HTML]{F0F0F0}160\
contributor & \cellcolor[HTML]{EFEFEF}102\
openPR & \cellcolor[HTML]{F0F0F0}151\
closePR & \cellcolor[HTML]{EFEFEF}100\
openISSUE & \cellcolor[HTML]{F0F0F0}150\
closedIS... | |
CR-21878 | \begin{tabular}{rcl}
\hline
$U_{i}$ & & $S$ \\
\hline
Chooses $b$ as random number and inputs $ID_{i}$, $PW_{i}$ \& $b$ & & \\
Computes $PWB_{i}=h(PW_{i}\oplus b)$ & $\xrightarrow{PWB_{i}, ID_{i}}$ & Computes \\
& & $Q_{i}=h(ID_{i}\Vert x)\oplus PWB_{i}$ \\
& & $R_{i}=h(PWB_{i}\Vert ID_{i})$ \\
Stores random ... | |
CV-2811 | \begin{tabular}{|l||r|r|r||r|}
\hline
{} & {\em ss} & {\em gs} & {\em noa} & Total \\
\hline\hline
Training & $648$ & $2\rm{,}002$ & $7\rm{,}000$ & $ 9\rm{,}650$ \\
\hline
Testing & $237$ & $618$ & $2\rm{,}828$ & $ 3\rm{,}683$ \\
\hline\hline
Total & $885$ & $2\rm{,}620$ & $9\rm{,}828$ & $13\rm{,}333$ \\
\hlin... | |
CV-24619 | \begin{tabular}{|l|c|}
\hline
Method & Accuracy \\
\hline\hline
Chance & 0.1 \\
Analogous Attr & 1.4 \\
Red wine & 13.1 \\
Attribute as Operator & 14.2 \\
VisProd NN & 13.9 \\
Label Embedded+ & 14.8 \\
Our & \textbf{15.2} \\
\hline
\end{tabular} | |
CR-43677 | \begin{tabular}{|l|l|l|l|}
\hline
\multicolumn{2}{|c|}{\textbf{Sample of Secret Set}} & \multicolumn{2}{|c|}{\textbf{Sample of Camouflaged Training Set}} \\\hline
\multicolumn{1}{|c|}{\textbf{Class}} & \multicolumn{1}{|c|}{\textbf{Article}} & \multicolumn{1}{|c|}{\textbf{Class}} & \multicolumn{1}{|c|}{\textbf{Articl... | |
CV-332 | \begin{tabular}{|c|c|cc|}
\hline
& & Surface & Joint \\
Output & Method & Error & Error \\
\hline
\multirow{3}{*}{P} & Tung \textit{et al.} & 74.5 & 64.4 \\
& Pavlakos \textit{et al.} & 151.5 & - \\
& SMPLR & 75.4 & 55.8 \\
\hline
V & BodyNet & 65.8 & - \\
\hline
\multirow{2}{*}{S} & Baseline & 101 & 85.7... | |
AI-24907 | \begin{tabular}{c|ccc}
& \multicolumn{2}{c}{Evaluation Level} \\
Game & 1 & 3 \\ \hline
Clusters & 0.00 $\pm$ 0.00 & 0.7 $\pm$ 0.46 \\
Cook Me Pasta & 4.00 $\pm$ 0.00 & 0.00 $\pm$ 0.00 \\
Bait & -0.09 $\pm$ 0.29 & 1.78 $\pm$ 0.42 \\
Sokoban 2 & 0.00 $\pm$ 0.00 & 0.00 $\pm$ 0.00 \\
Zen Puzzle & 23.00 $\pm$ 0.00 ... | |
CV-19981 | \begin{tabular}{c|c|c}
\hline
& w/o & w/ \\
& noise module & noise module \\
\hline\hline
DSQ & 84.11 & \textbf{84.46} \\
BNN+ & 84.59 & \textbf{84.87} \\
FDA-BNN & 85.83 & \textbf{86.20} \\
\hline
\end{tabular} | |
CR-12610 | \begin{tabular}{|c|c|c|c|c|}
\hline
solution & abbreviation & sparsification & perturbation & budget \\
\hline
non-private & NP & full/random/topk & - & $\infty$ \\
\hline
flat & PM/HM/Duchi & random sampling & $\epsilon^{\prime}$ & $\epsilon^{\prime}$ \\
\hline
compressed & -RP & random projection & $\epsilon^... | |
AI-16366 | \begin{tabular}{|l|l|l|l|l|}
\hline
\textbf{Algorithm} & \textbf{F1 Score} & \textbf{Precision} & \textbf{Recall} & \textbf{AUC} \\ \hline
\textit{RECON} & 0.61 & 0.56 & 0.68 & 0.51 \\ \hline
\textit{ImRec} & 0.71 & 0.60 & 0.88 & 0.65 \\ \hline
\textit{TIRR} & 0.87 & 0.86 & 0.88 & 0.91 \\ \hline
\end{tabular} | |
AI-19935 | \begin{tabular}{ll}
\hline
\hline
\textbf{dialogue His.} & what is your favorite food ? [SEP] ice cream . \\
\textbf{Gold Resp.} & what flavor ? \\
\hline
TA-Seq2Seq & what kind of ice cream ? \\
THRED & what kind of ice cream ? \\
C-Trans-ED & ice cream is the best food i have ever eaten \\
C-Trans-Dec & i 'm... | |
SE-9694 | \begin{tabular}{lll}
\hline
Paper & Context & Type of study \\ \hline
Abdullah et al. & Compliance management & Case study \\
Conmy and Paige & Safety standards (avionics) & Educated opinion \\
Boella et al. & Business processes & Educated opinion \\
Ghanavati et al. & Business processes & Experience \\
Nekvi an... | |
CV-3840 | \begin{tabular}{c|cccc|cccc|cccc|cccc}
Model & \multicolumn{4}{c|}{OMP Models} & \multicolumn{4}{c}{25 mm} & \multicolumn{4}{c}{50 mm} & \multicolumn{4}{c}{ 100 mm} \\
\hline
ZV & 42 & 100 & 149 & 188 & 53 & 106 & 154 & 191 & \textbf{66} & 118 & 164 & 199 & 106 & 151 & 187 & 223 \\
RNN & 41 & 93 & 135 & 169 & 52 & ... | |
CR-30622 | \begin{tabular}{c|c|c|c|c}
\hline
& $\delta$-reweight & $\gamma$-reweight & Soft($\delta=1.0$) & Soft($\delta=2.0$) \\
\midrule[0.1pt]
$\epsilon=0.0$ & $0.9997 \pm 0.0005$ & $0.9936 \pm 0.0016$ & $0.8446 \pm 0.0069$ & $0.9705 \pm 0.0030$ \\
$\epsilon=0.1$ & $0.9569 \pm 0.0021$ & $0.9297 \pm 0.0030$ & $0.7871 \pm ... | |
SE-15205 | \begin{tabular}{lrrl}
\hline
{\bfseries Method} & {\bfseries Mean Recall} & {\bfseries Dunn's test Rank} & {\bfseries Comments} \\
\hline
\hline
Proportion Moving Window & 0.84 & 1 & \\
Proportion Cold Start & 0.82 & 1 & \\
Proportion Increment & 0.81 & 1.5 & Significantly lower than Proportion Moving Window \\
... | |
CR-55836 | \begin{tabular}[c]{@{}l@{}}1.Thestrategybasedonknowledgeextractionwasusedtoovercome\\thecommunicationbottleneckinFL.\\2.Thearticleproducedsatisfactoryresultsonthreedifferent\\medicaldatasets.\end{tabular} | |
CR-21138 | \begin{tabular}{|l|r|}
\cline{2-2}
\multicolumn{1}{c|}{\ } & Mean ($\pm$ Std) \\ \hline \hline
Capacity per Token & 4.41 ($\pm$ 0.78) \\ \hline
Encoded Expansion & 8.13 ($\pm$ 2.12) \\ \hline
Plaintext Bits per Covertext Bits & 0.11 ($\pm$ 0.02) \\ \hline
Median Sender-side Time & 5.21 \\ \hline
Sentinel Value C... | |
AI-18467 | \begin{tabular}{|c|c|c|c|}
\hline
\textbf{Algorithm} & \textbf{Rounds} & \textbf{MNIST} & \textbf{CIFAR-10} \\
\hline
Genetic CFL & 10 & 97.99 & 76.88 \\
\hline
Byzantine Robustness of CFL & 200 & 97.4 & 75.3 \\
\hline
FedZip & 20 & 98.03 & - \\
\hline
Iterative federated clustering & - & 95.25 & 81.51 \\
\h... | |
CL-885 | \begin{tabular}{lcccc}
\hline
Features & Category & $P$ & $R$ & $F1$ \\
\hline
\multirow {2} {*} {all} & $I$ & 66.93 & \textbf{77.32} & \textbf{71.75} \\
& $NI$ & \textbf{73.13} & 61.78 & \textbf{66.97} \\
\hline
\multirow {2} {*} {- tropes} & $I$ & \textbf{67.70} & {48.00} & 56.18 \\
& $NI$ & {59.70} & \text... | |
AI-22501 | \begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Contr.} & \textbf{Domain} & \textbf{Application} & \textbf{Focus} & \textbf{Value} (main) \\
\hline
\hline
& Business & Decision Support System & Conceptual & Interoperability \\
& N\textbackslash A & N\textbackslash A & Conceptual & Interoperability \\
& N\textback... | |
SE-23702 | \begin{tabular}{ccrrrc}
\toprule
& sub- & fail-only & pass-only & fail \& & failure \\
signature & pattern & events & events & pass & strings* \\
\midrule
A & 1 & 1 & 0 & 0 & yes \\
A & 2 & 2 & 0 & 0 & no \\
B & 1 & 2 & 0 & 0 & yes \\
C & 1 & 21 & 0 & 0 & yes \\
C & 2 & 21 & 0 & 0 & yes \\
D & 1 & 4 & 0 & 0 ... | |
AI-1767 | \begin{tabular}{lrrrrrr}
\toprule
\multirow{2}*{Methods} & \multicolumn{3}{c}{CNNDM} & \multicolumn{3}{c}{XSum} \\
\cmidrule(r{4pt}){2-4} \cmidrule{5-7}
~ & IF & RL & FL & IF & RL & FL \\
\midrule
PSP & {\bf 0.500} & {\bf 0.708} & {\bf 0.667} & {\bf 0.217} & {\bf 0.275} & {\bf 0.492} \\
Prompt Tuning & -0.317 & ... | |
CV-28731 | \begin{tabular}{@{\hspace{1mm}}c@{\hspace{9mm}}c@{\hspace{15mm}}c@{\hspace{16mm}}c@{\hspace{13mm}}c@{\hspace{13mm}}c}
(a) ground-truth & (b) RPM-HTB & (c) Go-ICP & (d) FRS & (e) TEASER++ & (f) GORE
\end{tabular} | |
CR-28559 | \begin{tabular}{lc}
\hline
\textbf{Command} & \textbf{Output} \\
\hline
\verb|{\c c}| & {\c c} \\
\verb|{\u g}| & {\u g} \\
\verb|{\l}| & {\l} \\
\verb|{\~n}| & {\~n} \\
\verb|{\H o}| & {\H o} \\
\verb|{\v r}| & {\v r} \\
\verb|{\ss}| & {\ss} \\
\hline
\end{tabular} | |
CV-409 | \begin{tabular}{lccccccc}
\toprule
$D$ & 2 & 8 & 16 & 32 & 64 & 128 & 256 \\
\midrule
Scenes-daytime & 85 & 87 & \textbf{91} & 92 & 92 & 95 & 95 \\
\midrule
Handbags-color & 96.3 & \textbf{99.1} & 99.0 & 99.3 & 98.3 & 98.9 & 98.4 \\
Handbags-texture & 64.2 & 65.2 & 66.4 & \textbf{87.0} & 91.3 & 92.8 & 95.4 \\
\... | |
AI-19315 | \begin{tabular}{|l|l|}
\hline
$P(x|z_1)$ & 0.1 \\
\hline
$P(x|z_2)$ & 0.4 \\
\hline
$P(x|z_3)$ & 0.5 \\
\hline
$P(x|z_4)$ & 0.7 \\
\hline
\end{tabular} | |
CR-56856 | \begin{tabular}{|c|c|c|c|c|}
\hline
Candidate Models & DNN1 & DNN2 & DNN3 & VGG-16 \\ \hline
Accuracy & 79.63\
\end{tabular} | |
CV-27536 | \begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
\hline
\multicolumn{2}{|c|}{Branches} & \multicolumn{4}{c|}{Regular Text} & \multicolumn{4}{c|}{Irregular Text} \\ \hline
Attn & CTC & IIIT5K & SVT & IC03 & IC13 & IC15-2077 & IC15-1811 & SVTP & CUTE \\ \hline
& \checkmark & 88.6 & 87.3 & 92.4 & 90.3 & 72.1 & 76.5 & 77.1 & 78... | |
SE-4747 | \begin{tabular}{|lc|c|c|c|c|c|c|c|}
\hline
\multicolumn{1}{|l|}{\textbf{Distance}} & \textbf{Total} & \textbf{TP} & \textbf{FP} & \textbf{FN} & \textbf{P} & \textbf{R} & \textbf{F1} & \textbf{AP@0.5} \\ \hline
\multicolumn{1}{|l|}{All} & 139,526 & 134,948 & 711 & 191 & 0.9948 & 0.9986 & 0.9967 & 0.9942 \\ \hline
\m... | |
AI-33039 | \begin{tabular}{@{~}lll}
\toprule
\textbf{Notation} & \textbf{Desription} \\
\midrule
$\bm{\mathcal{G}}$ & a directed graph \\
$\bm{\mathcal{V}} $ & set of nodes \\
$\bm{\mathcal{E}} $ & set of edges \\
$\bm{\mathcal{S}}$ & set of multiple-paths \\
$\bm{\mathcal{T}}$ & set of single-paths \\
$N$ & number of no... | |
AI-30452 | \begin{tabular}{lccccc}
\toprule
Symbol & $a_1$ & $a_2$ & $a_3$ & $a_4$ & $a_5$ \\
\midrule
Probability & $0.32$ & $0.08$ & $0.16$ & $0.02$ & $0.42$ \\
$\ell_{a_i}$ & $32$ & $8$ & $16$ & $2$ & $42$ \\
$b_{a_i}$ & $0$ & $32$ & $40$ & $56$ & $58$ \\
\bottomrule
\end{tabular} | |
CR-48768 | \begin{tabular}{l|l|c}
\toprule[1.5pt]
\multicolumn{2}{l|}{Violated Rules} & \# of Apps\tabularnewline
\midrule[1pt]
\multicolumn{2}{l|}{Rule 1} & 41 \tabularnewline
\hline
\multirow{4}{*}{Rule 2} & Rule 2-1 & 162\tabularnewline
\cline{2-3}
& Rule 2-2 & 67\tabularnewline
\cline{2-3}
& Rule 2-3 & 125\tabular... | |
PL-303 | \begin{tabular}{l|r|r|r|r|r}
\toprule
Service type
& \thead{\# Positive \\ responses} & \thead{\# Negative\\ responses} & \thead{\# Total\\ responses} & \thead{\# No\\ responses} & \thead{\# Total\\ PRs}\\
\midrule
Nudge-LT
& 1829 & 2062 & 3891 & 226 & 4117 \\
Nudge-FULL
& 3199 & 882 & 4081 & 302 & 4383 \\
... | |
CL-2692 | \begin{tabular}{lcccc}
\toprule
& \multicolumn{2}{c}{Train (sec.)} & \multicolumn{2}{c}{Test (sec.)} \\
Model & Turn & Total & Turn & Total \\
\toprule
\small GLAD & 1.78 & 89 & 2.32 & 76 \\
\small GCE (Ours) & \textbf{1.16} & \textbf{60} & \textbf{1.92} & \textbf{63} \\
\bottomrule
\end{tabular} | |
SE-23563 | \begin{tabular}{p{5cm}|p{2cm}}
\hline
\textbf{Survey item} & \textbf{Average score} \\
\hline
My understanding of real world problems related to project management was promoted. & 1.4 \\
\hline
My interest on the course objectives and content was aroused. & 1.9 \\
\hline
The importance of the material for my pr... | |
CV-8215 | \begin{tabular}{||c|c|c|c||}
\hline
Method & PSNR & SSIM & CPBD \\ [0.5ex]
\hline\hline
$L_{pix}$ & 25.874 & 0.813 & 0.366 \\
$L_{pix}+L_{adv}$ & 25.951 & 0.814 & 0.373 \\
$L_{pix}+L_{adv}+L_{reg}$ & \textbf{26.153} & \textbf{0.818} & \textbf{0.386} \\
\hline
\end{tabular} | |
SE-4363 | \begin{tabular}{lrrrrrrrr}
\toprule
\multirow{2}{*}{Models} &
\multicolumn{4}{c}{Accuracy} &
\multicolumn{4}{c}{MRR} \\
\cmidrule(lr){2-5}
\cmidrule(lr){6-9}
& k = 1 & k=3 & k=5 & k=7 & k = 1 & k=3 & k=5 & k=7 \\
\hline
(1) No words or files & 0.02 & 0.08 & 0.13 & 0.16 & 0.01 & 0.04 & 0.05 & 0.06 \\
(2) Word... | |
CV-5301 | \begin{tabular}{|l|c|c|c|c|c|c|c|}
\hline
\textbf{Model} & \textbf{\# Par. Subnets} & \textbf{48 cores} & \textbf{2 GPUs} & \textbf{4 GPUs} & \textbf{8 GPUs} \\
\hline
\multicolumn{6}{|c|}{\textbf{without multi-rate clocks}} \\
\hline
sequential & 1 & $6.0~(1.0\times)$ & $18.6~(1.0\times)$ & $18.0~(1.0\times)$ & ... | |
CR-11747 | \begin{tabular}{llrrr}
\toprule
Application & Description & Version & LOC & Files \\
\midrule
Nodegoat & Educational & 1.3.0 & 970\,450 & 12\,180 \\
Keystone & CMS & 4.0.0 & 1\,393\,144 & 13\,891 \\
Apostrophe & CMS & 2.0.0 & 774\,203 & 5\,701 \\
Juice-shop & Educational & 8.3.0 & 725\,101 & 7\,449 \\
Mongo-exp... | |
CV-29652 | \begin{tabular}{|l|l|l|}
\hline Index & Layer Description & Output \\
\hline
1 & Warp($I_R$,$\mathbf{d_L^3}$) - $I_L$ & H x W x 3 \\
2 & concat 1, $I_L$ & H x W x 6 \\
3 & Warp($\mathbf{d_R^3}$, $\mathbf{d_L^3}$) - $\mathbf{d_L^3}$ & H x W x 1 \\
4 & concat 3, $\mathbf{d_L^3}$ & H x W x 2 \\
5 & 3x3 conv on 2, 1... | |
CR-48333 | \begin{tabular}{ccccc}
\toprule
\textbf{Subjects} & \textbf{Version} & \textbf{Format} & \textbf{Size} & \textbf{LoC} \\
\midrule
boringssl @@ & 2016-02-12 & lib & 6.8M & 0.3k \\
freetype @@ & 2017 & font & 6.3M & 0.5k \\
libcxx @@ & 2017-01-27 & lib & 1.9M & 5.0k \\
libxml @@ & libxml2-v2.9.2 & xml & 12M & 15.7... | |
AI-25582 | \begin{tabular}{|c|c|c|c|}
\hline
\multirow{2}{*}{Embedding} & \multicolumn{3}{c|}{Distance} \\\cline{2-4}
& $\ell_1$ & $\ell_2$ & Cosine \\\hline
FACSNet-CL-F & 47.1 & 47.1 & 40.7 \\\hline
FACSNet-CL-P & 45.3 & 44.2 & 48.3 \\\hline
AFFNet-CL-F & 49.0 & 47.7 & 49.0 \\\hline
AFFNet-CL-P & 52.4 & 51.6 & 53.3 \\\h... | |
CR-29305 | \begin{tabular}{@{}l@{}}
Substitute \\
Gap($i$)$\rightarrow$Msg($j$) \\
Msg($j$)$\rightarrow$Gap($i$)
\end{tabular} | |
CR-6454 | \begin{tabular}{|p{8cm}|}
\Xhline{1pt}
\begin{center}
$\mathtt{\pi}_{\rm S-SIP}$: Functionality of S-SIP
\end{center}
\textbf{Input:} The client (named $P_0$) holds a set of $t$ pairs $(X, S)=\{(x_1, s_1), \cdots, (x_t, s_t)\}$, while the server (named $P_1$) holds dataset of key-values pairs $(Y, G)=\{(y_1, g_1)... | |
CR-33963 | \begin{tabular}{|c|c|c|}
\hline
\textbf{Parameter} & \textbf{Type} & \textbf{Description} \\
\hline
drcId & bytes32 & Identifier of the DRC \\
\hline
farAvailable & uint256 & FAR (Floor Area Ratio) available for allocation \\
\hline
landCount & uint256 & Total count of sub-divided lands \\
\hline
owner & addr... | |
CR-16760 | \begin{tabular}{ll|ll|ll}
\textbf{Training / Testing Set} & $\bm{\sigma^2}$ & \textbf{PP (dev)} & \textbf{PP (test)} & \textbf{PP (dev, large)} & \textbf{PP (test, large)} \\ \hline
Brown / Reddit\_10k & 0 & 1561.20 & 1584.54 & 1652.65 & 1677.42 \\
Reddit\_10k / Reddit\_10k & 0 & 3805.83 & 3787.68 & 1254.48 & 1259.2... | |
SE-19395 | \begin{tabular}{lc}
\hline
\multicolumn{1}{c}{\textbf{Search engines}} & \textbf{\#non-duplicated search result} \\
\hline
Google search & 495 \\
Medium search & 358 \\\hline
\textbf{Total} & \textbf{853} \\\hline
\hline
\end{tabular} | |
SE-23962 | \begin{tabular}{l|r|cccc}
\toprule
Model & \# outputs & 256 & 512 & 768 & 1024 \\
\midrule
\multirow{4}{*}{\texttt{CodeParrot-small}}
& 5,000 & 6,666 & 9,080 & 11,041 & 14,031 \\
& 10,000 & 10,627 & 14,655 & 17,664 & 22,243 \\
& 15,000 & 14,015 & 19,444 & 23,863 & 29,133 \\
& 20,000 & 16,966 & 23,574 & 29,2... | |
SE-22543 | \begin{tabular}{lrrr}
\toprule
\multirow{2}{*}{\bf Selection Rule} & \multicolumn{3}{c}{\bf Dataset} \\
\cmidrule{2-4}
& {\bf Spark} & {\bf Hadoop} & {\bf Kibana} \\
\midrule
None & 81 & 92 & 184 \\
Length & 33 & 25 & 77 \\
Length+Content & 59 & 57 & 114 \\
\bottomrule
\end{tabular} | |
AI-2635 | \begin{tabular}{||ccc||}
\hline
Cube no. & Edge length & Color \\ [0.5ex]
\hline\hline
1 & 5cm & Red \\
\hline
2 & 4cm & Red \\
\hline
3 & 3cm & Red \\
\hline
4 & 2cm & Red \\
\hline
5 & 10cm & Blue \\
\hline
6 & 8cm & Blue \\
\hline
7 & 6cm & Blue \\
\hline
8 & 2cm & Blue \\
\hline
\end{tabular} | |
CR-44786 | \begin{tabular}{ccccc}
\toprule
Method & Avg. of AUROC & Avg. of F1 score & Std. of AUROC & Std. of F1 score \\
\midrule
STRIP & 0.3930 & 0.5026 & 0.0997 & 0.0027 \\
FreqDetector & 0.7911 & 0.7671 & 0.2235 & 0.2027 \\
\rowcolor[rgb]{ .906, .902, .902} Ours & 0.7749 & 0.7856 & 0.0306 & 0.0336 \\
\bottomrule
\end{... | |
CR-36658 | \begin{tabular}{|c|l|l|}
\hline
No & Rule & Description \\
\hline
1 & Feature indifference & A value of a feature is indifferent at \\
& & bot and normal user \\
\hline
2 & Feature invariance & Summation of a feature is 0, and \\
& & standard deviation of a feature is 0 \\
& & at bot and normal user, respec... | |
CL-2666 | \begin{tabular}{>{\raggedright\arraybackslash}p{2.7cm}>{\raggedright\arraybackslash}p{2.7cm}|p{0.6cm}}
\hline
External representation & Internal representation & Test BLEU \\
\hline
Plain BPE & Plain BPE & 29.2 \\
Linearized derivation & Linearized derivation & 28.8 \\
\hline
Linearized tree & Plain BPE & 28.9 \... | |
CV-5329 | \begin{tabular}{|p{3.5cm}|p{0.8cm}|p{0.8cm}|p{0.8cm}|p{0.8cm}||p{0.8cm}|p{0.8cm}|p{0.8cm}|p{0.8cm}|}
\hline
\multirow{2}{*}{Method} & \multicolumn{4}{c|}{Market1501 $\rightarrow$ DukeMTMC-reID} & \multicolumn{4}{c|}{DukeMTMC-reID $\rightarrow$ Market1501 } \\
\cline{2-9}
\cline{2-9}
& R1 & R5 & R10 & mAP & R1 & R... | |
AI-11117 | \begin{tabular}{ccccccccc}
\hline
Dataset & level1 & level2 & level3 & level4 & level5 & level6 & level7 & level8 \\
\hline
RCV1 & 236334 & 20523 & 11850 & 23211 & - & - & - & - \\
NYT & 15161 & 2923 & 1160 & 842 & 1066 & 925 & 992 & 1460 \\
WOS & 6712 & 351 & - & - & - & - & - & - \\
\hline
\end{tabular} | |
CR-46823 | \begin{tabular}{|l||p{1.5cm}|p{1.5cm}|p{1.25cm}||p{1.5cm}|p{1.5cm}|p{1.25cm}|}
\hline
~ & \multicolumn{3}{c||}{TCP} & \multicolumn{3}{c|}{DCCP} \\
~ & Reported Attacks & Interesting \newline (Off-path) Attacks & Unique Attacks & Reported Attacks & Interesting \newline (Off-path) Attacks & Unique Attacks \\
\hline
... | |
CR-8746 | \begin{tabular}[c]{@{}l@{}}CopywritingTranslations,SocialMediaMarketingServices,\\OptimizationPromotionandAudit\end{tabular} | |
CR-7160 | \begin{tabular}{cccccccccc}
\toprule
\multirow{2}{*}{Data} & \multirow{2}{*}{Measures} & \multicolumn{3}{c}{\texttt{CFD}} & \multicolumn{3}{c}{\texttt{CFD LRT}} \\
\cmidrule(lr){3-8}
& {} & \texttt{SCFE} & \texttt{GS} & \texttt{CCHVAE} & \texttt{SCFE} & \texttt{GS} & \texttt{CCHVAE} \\
\midrule
\multirow{4}{*}{A... | |
SE-15978 | \begin{tabular}{lllr}
\hline
\textbf{ID} & \textbf{Mistake Type} & \textbf{Associated Mistake Class} & \textbf{Occurance} \\ \hline
1 & Lack of preparation & \textit{Teamwork and Planning} & 4 \\
2 & Lack of planning & \textit{Teamwork and Planning} & 3 \\ \hline
3 & Not identifying stakeholders & \textit{Question... | |
PL-2065 | \begin{tabular}{l|lll}
\hline
\multirow{2}{*}{$b_0$} & \texttt{int i = 0;} & & \\
& & \verb|Update|: $\phi(b_1)$ & \verb|Goto| $b_1$ \\
\cline{1-1}\cline{3-4}
\multirow{3}{*}{$b_1$} & & Skip to $b_2$ unless $SAT(\phi(b_1))$ & \\
& \texttt{while (i < b) \{} & & \\
& & \verb|Update|: $\phi(b_2)$, $\phi(b_5)$ & ... | |
CV-8720 | \begin{tabular}{ccccccccc}
\hline
Model & 0.25 & 0.5 & 1 & 2 & 4 & 8 & 16 & 32 \\[0.5ex]
\hline
Chained & 4.76 & 8.33 & 15.55 & 28.23 & 44.69 & 58.62 & 65.89 & 67.49 \\
2-SHG & 5.59 & 10.87 & 22.25 & 41.62 & 61.78 & 73.9 & 79.21 & 79.78 \\
DeepPose & 3.3 & 4.86 & 7.99 & 12.98 & 18.26 & 21.33 & 22.79 & 23.12 \\
\... | |
SE-1008 | \begin{tabular}{l|c|c|}
\cline{2-3}
\multicolumn{1}{c|}{} & Description & Artifact Type \\ \hline
\multicolumn{1}{|l|}{{CR01}} & \makecell{Every lifeline must have \\ a corresponding class.} & uml:Lifeline \\ \hline
\multicolumn{1}{|l|}{{CR02}} & \makecell{Every transition has to have \\ a corresponding message.} &... | |
CR-52556 | \begin{tabular}{||cccc||}
\hline
\textbf{Datasets} & \textbf{Nodes (N)} & \textbf{Dimension (d)} & \textbf{Classes (c)} \\ [0.5ex]
\hline\hline
Iris & 150 & 4 & 3 \\
\hline
Glass & 214 & 9 & 6 \\
\hline
Wine & 178 & 13 & 3 \\
\hline
Control Chart & 600 & 60 & 6 \\
\hline
Parkinsons & 195 & 22 & 2 \\
\hline... | |
CR-29421 | \begin{tabular}{p{3cm}<{\raggedright}p{5cm}<{\raggedright}p{9cm}<{\raggedright}}
\hline
\textbf{Type} & \textbf{Approach} & \textbf{Brief Introduction} \\
\hline
\multirow{4}{3cm}{Original Approaches with complete process frameworks} & E-Safety Vehicle Intrusion Protected Applications (EVITA) & EVITA approach consi... | |
CV-13976 | \begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|}
\hline
Methods & Land & Forest & Residential & Haystack & Road & Church & Car & Water & Sky & Hill & Person & Fence & Overall \\
\hline
w/ Base Train & .495 & .496 & .774 & .000 & .252 & .166 & .000 & .006 & .952 & .371 & .000 & .060 & .298 \\
w/ SegProp Train & \text... | |
CV-24411 | \begin{tabular}{l|cc|cc}
\specialrule{1.2pt}{1pt}{1pt}
\multirow{2}{*}{\hspace{0.08cm} Method} & \multicolumn{2}{c|}{Segmentation} &
\multicolumn{2}{c}{Robustness} \\
\cline{2-5}
& \textbf{B} & \textbf{W} & \textbf{B} & \textbf{W} \\
\specialrule{1.2pt}{1pt}{1pt}
DeepLabv3-Res50 & 73.9 & 74.1 & 53.7 & \textbf{5... | |
CL-1656 | \begin{tabular}{lrr}
\toprule
$K$ & Successor surprisal & Total entropy \\
\midrule
5 & 0.212 & 0.541 \\
50 & 0.335 & 0.820 \\
500 & 0.397 & 0.947 \\
5000 & 0.434 & 0.992 \\
50000 & 0.454 & 1 \\
\bottomrule
\end{tabular} | |
CR-49011 | \begin{tabular}{cc}
\toprule
Component & Types considered \\
\midrule
Trend & linear model, local level, local linear \\
Seasonal & hourly, daily \\
Error & Gaussian, AR(p): autoregressive model of order p=1,2 \\
\bottomrule
\end{tabular} | |
CR-2892 | \begin{tabular}{cccccc}
\toprule
& CIFAR10 & CIFAR100 & Purchase100 & Texas100 & Location \\
\midrule
$p^*$ & 0.13 & 0.11 & 0.015 & 0.005 & 0.015 \\
\bottomrule
\end{tabular} | |
CR-39239 | \begin{tabular}{lccc}
\textbf{Dataset} & \textbf{Classes} & \textbf{Instances/Class} & \textbf{Total} \\
\hline
Undefended & 95 & 1000 & 95,000 \\
WTF-PAD & 95 & 1000 & 95,000 \\
Walkie-Talkie (sim.) & 100 & 900 & 90,000 \\
Walkie-Talkie (real) & 100 & 750 & 75,000 \\
Onion Sites & 538 & 77 & 41,426 \\
\hline
\... | |
AI-37528 | \begin{tabular}{c|c|c|c}
\hline
$\pi_b$ & 20$\times$20 & 50$\times$20 & 100$\times$20 \\
\hline
\hline
MWKR & \textbf{1803.1} & \textbf{3147.3} & \textbf{5676.0} \\
MOR & 1831.7 & 3229.8 & 5728.3 \\
SPT & 1813.8 & 3201.7 & 5718.7 \\
FIFO & 1826.4 & 3177.6 & 5692.9 \\
\hline
\end{tabular} | |
AI-18721 | \begin{tabular}{r|r|r|r}
& \textbf{Wikipedia} & \textbf{Wikinews} & \textbf{Science} \\ \hline
\textbf{Sentences} & 15,000 & 14,682 & 46,715 \\
\textbf{Verbs} & 32,758 & 34,026 & 66,653 \\
\textbf{Questions} & 75,867 & 80,081 & 143,388 \\
\textbf{Valid Qs} & 67,146 & 70,555 & 127,455
\end{tabular} |
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LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement
Dataset artifact for paper, LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement (AAAI 2025)
Tab2Latex: a Latex table recognition dataset, with 87,513 training, 5,000 validation, and 5,000 test instances.
The LaTeX sources are collected from academic papers within these six distinct sub-fields of computer science—Artificial Intelligence, Computation and Language, Computer Vision and Pattern Recognition, Cryptography and Security, Programming Languages, and Software Engineering—from the arXiv repository, covering the years 2018 to 2023.
Once the paper sources are downloaded, tables are identified and extracted from the LaTeX source code by matching \begin{tabular} and \end{tabular} and removing the comments. Then, the LaTeX table source scripts are rendered to PDF format and converted to PNG format at 160 dpi.
Citation
@article{jiang2025latte,
title = {LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement},
author = {Jiang, Nan and Liang, Shanchao and Wang, Chengxiao and Wang, Jiannan and Tan, Lin},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {39},
number = {4},
pages = {4030--4038},
year = {2025},
month = {Apr.},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/32422},
doi = {10.1609/aaai.v39i4.32422},
}
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