Papers › Mambular: A Sequential Model for Tabular Deep Learning

Mambular: A Sequential Model for Tabular Deep Learning

12 Aug 2024arXiv:2408.06291archive 2025-07-28

Anton Frederik Thielmann, Manish Kumar, Christoph Weisser, Arik Reuter, Benjamin Säfken, Soheila Samiee

The analysis of tabular data has traditionally been dominated by gradient-boosted decision trees (GBDTs), known for their proficiency with mixed categorical and numerical features. However, recent deep learning innovations are challenging this dominance. This paper investigates the use of autoregressive state-space models for tabular data and compares their performance against established benchmark models. Additionally, we explore various adaptations of these models, including different pooling strategies, feature interaction mechanisms, and bi-directional processing techniques to understand their effectiveness for tabular data. Our findings indicate that interpreting features as a sequence and processing them and their interactions through structured state-space layers can lead to significant performance improvement. This research underscores the versatility of autoregressive models in tabular data analysis, positioning them as a promising alternative that could substantially enhance deep learning capabilities in this traditionally challenging area. The source code is available at https://github.com/basf/mamba-tabular.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2408.06291")

Code

Syntology Ran 4 of 8 code samples harvested from 1 repository linked to this paper; 4 have no recorded run. Of those that ran: 4 ran with no contract checked.

By repository: official repository: 8 samples from 1 repository, 4 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

basf/mamba-tabular officialmentioned in papermentioned on GitHubpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

8 samples harvested; 4 ran; 0 honoured the contract we drafted; 4 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

4ran
4unverified

Licence: 0 of the 8 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from basf/mamba-tabular. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

column_count_error basf/mamba-tabular/deeptab/core/exceptions.py official repository ran MIT (permissive) · 2a39145fb11d3caa · report
column_dtype_error basf/mamba-tabular/deeptab/core/exceptions.py official repository ran MIT (permissive) · b73ad8847b0a093b · report
get_feature_dimensions basf/mamba-tabular/deeptab/core/inspection.py official repository ran MIT (permissive) · 719e9519bdf633c2 · report
get_hardware_info basf/mamba-tabular/deeptab/core/hardware.py official repository ran MIT (permissive) · 72b7dcfaa786cdff · report
build_structlog_logger basf/mamba-tabular/deeptab/core/observability.py official repository unverified MIT (permissive) · c3b09b852e474607 · report
column_name_error basf/mamba-tabular/deeptab/core/exceptions.py official repository unverified MIT (permissive) · 07cf79394c8014b0 · report
create_run_dir basf/mamba-tabular/deeptab/core/observability.py official repository unverified MIT (permissive) · 039ee243ea0b93be · report
write_run_config basf/mamba-tabular/deeptab/core/observability.py official repository unverified MIT (permissive) · f08fd4f5902ebb5e · report

Tasks

Deep LearningMambaState Space Modelsmodel

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

Mamba

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections