View PDF ↗
PDF Viewer
Open Full PDF

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (18)

[1]
TabArena: A Living Benchmark for Machine Learning on Tabular Data
2025Nick Erickson, Lennart Purucker et al.
[2]
Can a Deep Learning Model be a Sure Bet for Tabular Prediction?
2024Jintai Chen, Jiahuan Yan et al.
[3]
rLLM: Relational Table Learning with LLMs
2024Weichen Li, Xiaotong Huang et al.
[4]
LakeBench: A Benchmark for Discovering Joinable and Unionable Tables in Data Lakes
2024Yuhao Deng, Chengliang Chai et al.
[5]
Benchmarking Distribution Shift in Tabular Data with TableShift
2023Josh Gardner, Zoran Popovic et al.
[6]
LakeBench: Benchmarks for Data Discovery over Data Lakes
2023Kavitha Srinivas, Julian Dolby et al.
[7]
Trompt: Towards a Better Deep Neural Network for Tabular Data
2023Kuan-Yu Chen, Ping-Han Chiang et al.
[8]
XTab: Cross-table Pretraining for Tabular Transformers
2023Bingzhao Zhu, Xingjian Shi et al.
[9]
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
2022Noah Hollmann, Samuel G. Müller et al.
[10]
Revisiting Deep Learning Models for Tabular Data
2021Yu. V. Gorishniy, Ivan Rubachev et al.
[11]
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
2021Gowthami Somepalli, Micah Goldblum et al.
[12]
TabTransformer: Tabular Data Modeling Using Contextual Embeddings
2020Xin Huang, A. Khetan et al.
[13]
On data lake architectures and metadata management
2020Pegdwendé N. Sawadogo, J. Darmont
[14]
JOSIE: Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes
2019Erkang Zhu, Dong Deng et al.
[15]
Table Union Search on Open Data
2018Fatemeh Nargesian, Erkang Zhu et al.
[16]
CatBoost: unbiased boosting with categorical features
2017L. Ostroumova, Gleb Gusev et al.
[17]
Constance: An Intelligent Data Lake System
2016Rihan Hai, Sandra Geisler et al.
[18]
XGBoost: A Scalable Tree Boosting System
2016Tianqi Chen, Carlos Guestrin

Founder's Pitch

"LakeMLB is a standardized benchmark for evaluating machine learning performance in data lake environments, featuring multi-table scenarios."

BenchmarkingScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

4/4 signals

10

Series A Potential

1/4 signals

2.5

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/11/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.