A curated set of 11 influential, general-purpose tabular deep-learning papers published 2019–2023 — not an exhaustive census. Impact is measured as Semantic Scholar citation counts retrieved 2026-10-28, with annual counts for 2020–2026 (2026 is partial, through 2026-10-28). Labs and teams are inferred from author affiliations in OpenAlex and paper metadata.
Click a legend entry to hide or show a paper; hover a line for yearly values. The five most-cited papers start highlighted; the rest are drawn thin and grey until selected. Units: citations received per calendar year, Semantic Scholar.
Team impact is inferred from the curated papers associated with each group — it is a sum over flagship papers, not a formal institutional citation census. Each paper counts once, under its principal affiliation.
| Paper | Year | Authors | Lab / team | Citations | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026* | Momentum | Source |
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*2026 is partial, through 2026-10-28. Momentum sparkbars show the 2020–2026 annual shape per paper.
Method: 11 curated influential tabular deep-learning papers (2019–2023); current totals and annual citation counts (2020–2026, 2026 partial through 2026-10-28) from Semantic Scholar, retrieved 2026-10-28. Lab/team attribution cross-checked against OpenAlex author affiliations; ambiguous affiliations left unresolved. Citation databases merge paper versions differently, so counts are comparable within Semantic Scholar rather than universal totals. False-match search results in auxiliary sets were omitted. Lab totals sum each paper once under its principal affiliation.