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\begin{algorithmic}[1]
\State Input: Two multi-sets of $n$ points $R,B$ in $Q_d$.
\State Output: A matching from $R$ to $B$.
\State$\triangleright$ The set B is shared across all threads
\Procedure{WeightedMatch}{$R,B$}
\For {$r \in R$}\Comment{All for loop statements run in parallel}
\State $b\gets\mathrm{BreadthFirst... | \begin{algorithmic}
[1]
\State Input: Two multi-sets of $n$ points $R,B$ in $Q_d$.
\State Output: A matching from $R$ to $B$.
\State$\triangleright$ The set B is shared across all threads
\Procedure{WeightedMatch}{$R,B$}
\For {$r \in R$}\Comment{All for loop statements run in parallel}
\State $b\gets\mathrm{BreadthFirs... | "https://arxiv.org/src/2401.11562" | "2401.11562.tar.gz" | "2024-01-21" | {
"title": "enhancing selectivity using wasserstein distance based reweighing",
"id": "2401.11562",
"abstract": "given two labeled data-sets $\\mathcal{s}$ and $\\mathcal{t}$, we design a simple and efficient greedy algorithm to reweigh the loss function such that the limiting distribution of the neural net... | "2024-03-15T07:03:20.799704" | {
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} | [] | "algorithm" | "6198b44e-7ef3-4092-abff-15603e4a2143" | 681 | easy | |
\begin{algorithmic}[1]
\Require current node $u$, candidate node $v$, Walker $W$, {\sf HuGE} parameter $\mu$
\Ensure walker state updates
{\flushleft{{\bf sendStateQuery($u$, $v$, $W$)}}} %//{submit the walker-to-vertex query messages and process the state queries}
\State{$P(u,v) = Z\left(\frac{1}{deg(u)-Cm(u, v)}\cdot... | \begin{algorithmic}
[1]
\Require current node $u$, candidate node $v$, Walker $W$, {\sf HuGE} parameter $\mu$
\Ensure walker state updates
{\flushleft{{\bf sendStateQuery($u$, $v$, $W$)}}} %//{submit the walker-to-vertex query messages and process the state queries}
\State{$P(u,v) = Z\left(\frac{1}{deg(u)-Cm(u, v)}\cdo... | "https://arxiv.org/src/2303.15702" | "2303.15702.tar.gz" | "2024-02-25" | {
"title": "distributed graph embedding with information-oriented random walks",
"id": "2303.15702",
"abstract": "graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. the increasing availability of billion-edge graphs underscores the importance of lea... | "2024-03-15T03:43:03.810720" | {
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} | [] | "algorithm" | "495ba26c-9d13-4916-8708-fb41c8065401" | 1121 | medium | |
\begin{algorithmic}[1]
\Procedure{ModifiedGD}{$\nabla F, \hat{L}_{i, 0}, \delta_{(i, \cdot)}, R, \theta_{i, 0}, \kappa_{(i,\cdot)}, T^*, \ell, \gamma$}
\State $ \Delta \leftarrow 0 $ \Comment{Measure of predicted decrease of objective}
\For{$j = 0, 1, ..., T^*-1$}
\State $\alpha_{i,... | \begin{algorithmic}
[1]
\Procedure{ModifiedGD}{$\nabla F, \hat{L}_{i, 0}, \delta_{(i, \cdot)}, R, \theta_{i, 0}, \kappa_{(i,\cdot)}, T^*, \ell, \gamma$}
\State $ \Delta \leftarrow 0 $ \Comment{Measure of predicted decrease of objective}
\For{$j = 0, 1, ..., T^*-1$}
\State $\alpha_{i,j} \leftarrow \delta_{(i,\cdot)} (2|... | "https://arxiv.org/src/2309.10894" | "2309.10894.tar.gz" | "2024-02-15" | {
"title": "a novel gradient methodology with economical objective function evaluations for data science applications",
"id": "2309.10894",
"abstract": "gradient methods are experiencing a growth in methodological and theoretical developments owing to the challenges of optimization problems arising in dat... | "2024-03-15T05:23:50.845023" | {
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} | [] | "algorithm" | "d5824813-2875-4568-bb05-f16712f12f47" | 1447 | hard | |
\begin{algorithmic}[1]
\State \textbf{Input:} Stock data for two assets $S_1$ and $S_2$, buy threshold, sell threshold
\State \textbf{Output:} Trade signals for pairs trading
\State
\Procedure{Compute Hedge Ratio}{data1, data2}
\State model $\gets$ perform OLS regression (data1, data2)
\State \Return model.params$[1]$
... | \begin{algorithmic}
[1]
\State \textbf{Input:} Stock data for two assets $S_1$ and $S_2$, buy threshold, sell threshold
\State \textbf{Output:} Trade signals for pairs trading
\State
\Procedure{Compute Hedge Ratio}{data1, data2}
\State model $\gets$ perform OLS regression (data1, data2)
\State \Return model.params$[1]$... | "https://arxiv.org/src/2401.14761" | "2401.14761.tar.gz" | "2024-01-26" | {
"title": "esg driven pairs algorithm for sustainable trading: analysis from the indian market",
"id": "2401.14761",
"abstract": "this paper proposes an algorithmic trading framework integrating environmental, social, and governance (esg) ratings with a pairs trading strategy. it addresses the demand for... | "2024-03-15T05:30:05.430403" | {
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} | [] | "algorithm" | "d4f074dd-00b8-4ebd-9eff-3126db854c98" | 1105 | medium | |
\begin{algorithm}
GPB(N) Algorithm
\end{algorithm}
| \begin{algorithm}
GPB(N) Algorithm
\end{algorithm} | "https://arxiv.org/src/2402.08051" | "2402.08051.tar.gz" | "2024-02-12" | {
"title": "on bayesian filtering for markov regime switching models",
"id": "2402.08051",
"abstract": "this paper presents a framework for empirical analysis of dynamic macroeconomic models using bayesian filtering, with a specific focus on the state-space formulation of dynamic stochastic general equilibr... | "2024-03-15T04:21:14.605813" | {
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} | [] | "algorithm" | "75bee723-8648-4b9d-82cd-341a3bad5259" | 50 | easy | |
\begin{algorithmic}[1]
\State Initialize an empty list $l$
\For{\textbf{each} $i$ \textbf{in} $producers$}
\For{$t$ \textbf{in} $[500, 900)$}
\If{$\forall x \in \{p_{it},p_{i(t+1)},\dots,p_{i1000}\}(x < \epsilon)$}
\State $l$\textbf{.push}(True)
\State \textbf{break}
\EndIf
\E... | \begin{algorithmic}
[1]
\State Initialize an empty list $l$
\For{\textbf{each} $i$ \textbf{in} $producers$}
\For{$t$ \textbf{in} $[500, 900)$}
\If{$\forall x \in \{p_{it},p_{i(t+1)},\dots,p_{i1000}\}(x < \epsilon)$}
\State $l$\textbf{.push}(True)
\State \textbf{break}
\EndIf
\EndFor
\State $l$\textbf{.push}(False)
\End... | "https://arxiv.org/src/2401.07070" | "2401.07070.tar.gz" | "2024-01-13" | {
"title": "a dynamic agent based model of the real economy with monopolistic competition, perfect product differentiation, heterogeneous agents, increasing returns to scale and trade in disequilibrium",
"id": "2401.07070",
"abstract": "we have used agent-based modeling as our numerical method to artifi... | "2024-03-15T06:13:08.276479" | {
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} | [] | "algorithm" | "596d1a2a-5955-45c6-abc2-8a6ffce88a00" | 481 | easy | |
\begin{algorithm}[!ht]
\caption{Discrete empirical interpolation method (DEIM)}\label{alg:DEIM}
\begin{algorithmic}[1]
\State \textbf{Input:} matrix $M \in \mathbb{R}^{n\times m}$ with orthonormal columns
\State \textbf{Output:} index set $I$ if cardinality $m$
\State $I = \{\mathsf{argmax}\ |M(:,1)|\}$
\For $k = 2,\do... | \begin{algorithm}
[!ht]
\caption{Discrete empirical interpolation method (DEIM)}\begin{algorithmic}
[1]
\State \textbf{Input:} matrix $M \in \mathbb{R}^{n\times m}$ with orthonormal columns
\State \textbf{Output:} index set $I$ if cardinality $m$
\State $I = \{\mathsf{argmax}\ |M(:,1)|\}$
\For $k = 2,\dots,m$
\State $c... | "https://arxiv.org/src/2211.11338" | "2211.11338.tar.gz" | "2024-02-25" | {
"title": "approximation in the extended functional tensor train format",
"id": "2211.11338",
"abstract": "this work proposes the extended functional tensor train (eftt) format for compressing and working with multivariate functions on tensor product domains. our compression algorithm combines tensorized c... | "2024-03-15T03:21:44.181934" | {
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} | [] | "algorithm" | "080b212f-f820-4ed0-803b-0cd7ba5e82d4" | 467 | easy | |
\begin{algorithm}
\caption{Multi-Period Transition Framework}\label{alg:MPTF}
\begin{algorithmic}[H]
\Require $P_0, \mathcal{T}, Y_{-L:0}, \mathbf{X}_{-L:0}, V_0, \mathbf{T}, C_0$, OPT, Forecaster, MarketObserver
\Ensure $Y_0^T\mathcal{T} + C_0 \leq V_0$
\State $t \gets 0$
\State $P_t \gets P_0$
\State $C_t \gets C_0... | \begin{algorithm}
\caption{Multi-Period Transition Framework}\begin{algorithmic}
[H]
\Require $P_0, \mathcal{T}, Y_{-L:0}, \mathbf{X}_{-L:0}, V_0, \mathbf{T}, C_0$, OPT, Forecaster, MarketObserver
\Ensure $Y_0^T\mathcal{T} + C_0 \leq V_0$
\State $t \gets 0$
\State $P_t \gets P_0$
\State $C_t \gets C_0$
\State $Y \gets ... | "https://arxiv.org/src/2401.13126" | "2401.13126.tar.gz" | "2024-01-24" | {
"title": "optimizing transition strategies for small to medium sized portfolios",
"id": "2401.13126",
"abstract": "this work discusses the benefits of constrained portfolio turnover strategies for small to medium-sized portfolios. we propose a dynamic multi-period model that aims to minimize transaction c... | "2024-03-15T05:38:12.261498" | {
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} | [] | "algorithm" | "9d6c0f2d-70af-4e25-9cee-9bec2c661226" | 1119 | medium | |
\begin{algorithm}
\caption{getNextStage(candidates, k)}\label{alg:te2rules_stagek}
\begin{algorithmic}
\State $newCandidates \gets []$
\\
\For{$r_1 \gets candidates$}
\For{$r_2 \gets candidates$}
\State $nodes_1 = r_1.sourceNodes$
\State $nodes_2 = r_2.sourceNodes$
\If{$|nodes_1 \ca... | \begin{algorithm}
\caption{getNextStage(candidates, k)}\begin{algorithmic}
\State $newCandidates \gets []$
\\
\For{$r_1 \gets candidates$}
\For{$r_2 \gets candidates$}
\State $nodes_1 = r_1.sourceNodes$
\State $nodes_2 = r_2.sourceNodes$
\If{$|nodes_1 \cap nodes_2| = k - 2$}
\State $r \gets r_1 \& r_2$
\State $r.source... | "https://arxiv.org/src/2206.14359" | "2206.14359.tar.gz" | "2024-01-23" | {
"title": "te2rules: explaining tree ensembles using rules",
"id": "2206.14359",
"abstract": "tree ensemble (te) models, such as gradient boosted trees, often achieve optimal performance on tabular datasets, yet their lack of transparency poses challenges for comprehending their decision logic. this paper ... | "2024-03-15T09:04:28.850184" | {
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} | [] | "algorithm" | "4838572e-3197-4b9d-96d2-c4263ddd219d" | 496 | easy | |
\begin{algorithmic}[1]
\State Simulate $N$ random variables from $\text{Uniform}(0,1)$; Denote $\tilde{U}=(\tilde{U}_{(1)},...,\tilde{U}_{(N)})$ as the vector of $N$ simulated Uniformly distributed random variables
\State Denote $\tilde{Y}^{*}_{ij}=(\tilde{Y}^{*}_{ij,(1)},...,\tilde{Y}^{*}_{ij,(N)})$ as the vec... | \begin{algorithmic}
[1]
\State Simulate $N$ random variables from $\text{Uniform}(0,1)$; Denote $\tilde{U}=(\tilde{U}_{(1)},...,\tilde{U}_{(N)})$ as the vector of $N$ simulated Uniformly distributed random variables
\State Denote $\tilde{Y}^{*}_{ij}=(\tilde{Y}^{*}_{ij,(1)},...,\tilde{Y}^{*}_{ij,(N)})$ as the vector of ... | "https://arxiv.org/src/2206.08541" | "2206.08541.tar.gz" | "2024-02-19" | {
"title": "ensemble distributional forecasting for insurance loss reserving",
"id": "2206.08541",
"abstract": "loss reserving generally focuses on identifying a single model that can generate superior predictive performance. however, different loss reserving models specialise in capturing different aspects... | "2024-03-15T03:14:29.396980" | {
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} | [] | "algorithm" | "1d289ead-1c69-4045-8011-bd2568d3d304" | 1151 | medium | |
\begin{algorithmic}
\vspace{1mm}
\State{// \texttt{Iterate over all the haloes in catalogue}}
\For{ halo in catalogue }
\vspace{3mm}
\State{// \texttt{Compute probability of central}}
\State{$p_\text{cen} \leftarrow \text{model.}N_\text{cen}( $ halo.mass $... | \begin{algorithmic}
\vspace{1mm}
\State{// \texttt{Iterate over all the haloes in catalogue}}
\For{ halo in catalogue }
\vspace{3mm}
\State{// \texttt{Compute probability of central}}
\State{$p_\text{cen} \leftarrow \text{model.}N_\text{cen}( $ halo.mass $ )$ }
\vspace{3mm}
\State{// \texttt{Define a binomial random va... | "https://arxiv.org/src/2002.07179" | "2002.07179.tar.gz" | "2024-02-14" | {
"title": "scampy -- a sub-halo clustering & abundance matching based python interface for painting galaxies on the dark matter halo/sub-halo hierarchy",
"id": "2002.07179",
"abstract": "we present a computational framework for \"painting\" galaxies on top of the dark matter halo/sub-halo hierarchy obtai... | "2024-03-15T04:34:46.153353" | {
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} | [] | "algorithm" | "845af096-d4f2-4f54-96bd-5d99fe1e642a" | 861 | medium | |
\begin{algorithmic}[1]
\Require $n \geq 0 \vee x \neq 0$
\Ensure $y = x^n$
\State $y \Leftarrow 1$
\If{$n < 0$}\label{algln2}
\State $X \Leftarrow 1 / x$
\State $N \Leftarrow -n$
\Else
\State $X \Leftarrow x$
\State $N \Leftarrow n$
\EndIf
\While{$N \neq 0$}
\If{$N$ is even}
... | \begin{algorithmic}
[1]
\Require $n \geq 0 \vee x \neq 0$
\Ensure $y = x^n$
\State $y \Leftarrow 1$
\If{$n < 0$} \State $X \Leftarrow 1 / x$
\State $N \Leftarrow -n$
\Else
\State $X \Leftarrow x$
\State $N \Leftarrow n$
\EndIf
\While{$N \neq 0$}
\If{$N$ is even}
\State $X \Leftarrow X \times X$
\State $N \Leftarrow N /... | "https://arxiv.org/src/2312.05063" | "2312.05063.tar.gz" | "2024-02-25" | {
"title": "individualizing glioma radiotherapy planning by optimization of data and physics-informed discrete loss",
"id": "2312.05063",
"abstract": "brain tumor growth is unique to each patient and extends beyond what is visible in imaging scans, infiltrating surrounding brain tissue. understanding thes... | "2024-03-15T03:04:49.138400" | {
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} | [] | "algorithm" | "37f9ec91-081a-4799-9d19-7a6595228dc9" | 437 | easy | |
\begin{algorithmic}[1]
\State Compute the set
\begin{equation*}
I(\mathbf{x}) = \left\{i \in \{1, \dots, N\} : \mathbf{x} \in \mathcal{B}_i\right\}.
\end{equation*}
\If{$|I(\mathbf{x})|=1$}
\State Assign $C(\mathbf{x})$ the unique element of $I(\mathbf{x})$.
\EndIf
\If{$|I(\mathbf{x})| = ... | \begin{algorithmic}
[1]
\State Compute the set
\begin{equation*}
I(\mathbf{x}) = \left\{i \in \{1, \dots, N\} : \mathbf{x} \in \mathcal{B}_i\right\}.
\end{equation*}
\If{$|I(\mathbf{x})|=1$}
\State Assign $C(\mathbf{x})$ the unique element of $I(\mathbf{x})$.
\EndIf
\If{$|I(\mathbf{x})| = 0$}
\State $C(\mathbf{x}) = \a... | "https://arxiv.org/src/2301.09734" | "2301.09734.tar.gz" | "2024-02-08" | {
"title": "topological learning in multi-class data sets",
"id": "2301.09734",
"abstract": "we specialize techniques from topological data analysis to the problem of characterizing the topological complexity (as defined in the body of the paper) of a multi-class data set. as a by-product, a topological cla... | "2024-03-15T05:24:48.442243" | {
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} | [] | "algorithm" | "be38228b-6714-4f2a-987e-5364b1359ad6" | 490 | easy | |
\begin{algorithmic}
\State Initialize \(k=1, \mathbf{w}\sim Uniform(|\Phi|)\)
\While{\(k<k_{max}\)}
\State \(\hat{J}=-\infty\)
\For{\(\sigma_{\text{test}} \in [0,1]\)}
\If{\(J(\mathbf{w}, \sigma_{\text{test}}) > \hat{J}\)}
\State \(\sigma \gets \sigma_{\text{test}... | \begin{algorithmic}
\State Initialize \(k=1, \mathbf{w}\sim Uniform(|\Phi|)\)
\While{\(k<k_{max}\)}
\State \(\hat{J}=-\infty\)
\For{\(\sigma_{\text{test}} \in [0,1]\)}
\If{\(J(\mathbf{w}, \sigma_{\text{test}}) > \hat{J}\)}
\State \(\sigma \gets \sigma_{\text{test}}\)
\State \(\hat{J} \gets J(\mathbf{w}, \sigma_{\text{t... | "https://arxiv.org/src/2207.06392" | "2207.06392.tar.gz" | "2024-01-25" | {
"title": "relationship design for socially-aware behavior in static games",
"id": "2207.06392",
"abstract": "autonomous agents can adopt socially-aware behaviors to reduce social costs, mimicking the way animals interact in nature and humans in society. we present a new approach to model socially-aware de... | "2024-03-15T08:38:27.674079" | {
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"sections/3_preliminaries.tex": {
"toxicity_score": 0.007382561,
"severe_toxicity_score": 0.00079631805,
"identity_attack_score": 0.0020071338,
"insult_score": 0.0063918694,
... | {
"num_done": {
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} | [] | "algorithm" | "b6fcbba0-8428-4ca6-90b8-c0ab8babb1b1" | 775 | medium | |
\begin{algorithm}[!ht]\caption{LSH, private procedures}\label{alg:LSH_private_app}
\begin{algorithmic}[1]
\State {\bf data structure} \textsc{LSH}
\State
\State {\bf private}
\Procedure{\textsc{ChooseHashFunc}}{$k,L\in \mathbb{N}_+$}\label{lin:choose_hash_func}
\For{$l \in [L]$}
\State \Comment{Amplify hash... | \begin{algorithm}[!ht]
\caption{LSH, private procedures}\begin{algorithmic}
[1]
\State {\bf data structure} \textsc{LSH}
\State
\State {\bf private}
\Procedure{\textsc{ChooseHashFunc}}{$k,L\in \mathbb{N}_+$} \For{$l \in [L]$}
\State \Comment{Amplify hash functions by concatenating}
\State $\mathcal{H}_{l} \leftarrow$ s... | "https://arxiv.org/src/2208.03915" | "2208.03915.tar.gz" | "2024-02-13" | {
"title": "dynamic maintenance of kernel density estimation data structure: from practice to theory",
"id": "2208.03915",
"abstract": "kernel density estimation (kde) stands out as a challenging task in machine learning. the problem is defined in the following way: given a kernel function $f(x,y)$ and a ... | "2024-03-15T05:43:39.046127" | {
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"insult_score": 0.0062493687,
"profani... | {
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} | [] | "algorithm" | "df723daa-5bbd-4dcf-a0b8-28a7b34143bd" | 808 | medium |
End of preview. Expand in Data Studio
Image2Struct - Latex
Paper | Website | Datasets (Webpages, Latex, Music sheets) | Leaderboard | HELM repo | Image2Struct repo
License: Apache License Version 2.0, January 2004
Dataset description
Image2struct is a benchmark for evaluating vision-language models in practical tasks of extracting structured information from images.
This subdataset focuses on LaTeX code. The model is given an image of the expected output with the prompt:
Please provide the LaTex code used to generate this image. Only generate the code relevant to what you see. Your code will be surrounded by all the imports necessary as well as the begin and end document delimiters.
The subjects were collected on ArXiv and are: eess, cs, stat, math, physics, econ, q-bio, q-fin.
The dataset is divided into 5 categories. There are 4 categories that are collected automatically using the Image2Struct repo:
- equations
- tables
- algorithms
- code
The last category: wild, was collected by taking screenshots of equations in the Wikipedia page of "equation" and its related pages.
Uses
To load the subset equation of the dataset to be sent to the model under evaluation in Python:
import datasets
datasets.load_dataset("stanford-crfm/i2s-latex", "equation", split="validation")
To evaluate a model on Image2Latex (equation) using HELM, run the following command-line commands:
pip install crfm-helm
helm-run --run-entries image2latex:subset=equation,model=vlm --models-to-run google/gemini-pro-vision --suite my-suite-i2s --max-eval-instances 10
You can also run the evaluation for only a specific subset and difficulty:
helm-run --run-entries image2latex:subset=equation,difficulty=hard,model=vlm --models-to-run google/gemini-pro-vision --suite my-suite-i2s --max-eval-instances 10
For more information on running Image2Struct using HELM, refer to the HELM documentation and the article on reproducing leaderboards.
Citation
BibTeX:
@misc{roberts2024image2struct,
title={Image2Struct: A Benchmark for Evaluating Vision-Language Models in Extracting Structured Information from Images},
author={Josselin Somerville Roberts and Tony Lee and Chi Heem Wong and Michihiro Yasunaga and Yifan Mai and Percy Liang},
year={2024},
eprint={TBD},
archivePrefix={arXiv},
primaryClass={TBD}
}
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