FORGE reformulates molecular optimization as context-aware fragment ranking and replacement using mined low-to-high edit pairs, outperforming larger language models and graph methods on standard benchmarks.
hub
Tabpfn: A transformer that solves small tabu- lar classification problems in a second
18 Pith papers cite this work. Polarity classification is still indexing.
18
Pith papers citing it
hub tools
years
2026 18representative citing papers
Forecast loss differentials are reframed as returns and assessed with risk-adjusted finance metrics, showing professional forecasters are harder to beat on risk-adjusted performance than on raw accuracy in US macro forecasting.
Schema-1 is the first Data Language Model that natively understands raw tabular data and outperforms gradient-boosted ensembles, AutoML, and prior tabular foundation models on row-level prediction and imputation tasks.
TFM-Retouche is an architecture-agnostic input-space residual adapter that improves tabular foundation model accuracy on 51 datasets by learning input corrections through the frozen backbone, with an identity guard to fall back to the original model.
PHBench shows Product Hunt launch signals predict Series A funding with an ensemble model reaching AP 0.037 and F0.5 0.097 on blind test data, outperforming logistic regression and zero-shot LLMs.
Time series foundation models match the performance of specialized models for day-ahead load forecasting while providing explanations that match domain knowledge on weather and calendar effects.
TabDistill distills feature interactions from tabular foundation models via post-hoc attribution and inserts them into GAMs, yielding consistent predictive gains.
The authors release the first Slovene ESG sentiment dataset from news and report that large language models lead on environmental and social classification while fine-tuned SloBERTa performs best on governance.
LGB+ improves macroeconomic forecasts by letting linear basis functions compete with or alternate against tree updates inside gradient boosting, yielding native linear/nonlinear decomposition of predictions.
CarCrashNet releases a large-scale open benchmark dataset of structural crash simulations and a hierarchical neural solver for data-driven full-vehicle crash prediction.
ModelLens learns a performance-aware latent space from 1.62M leaderboard records to rank unseen models on unseen datasets without forward passes on the target.
Decoupled PFNs use controllable synthetic priors to train separate latent-signal and noise heads, making epistemic-aleatoric decomposition identifiable and improving acquisition in noisy settings.
Tabular foundation models achieve high accuracy in molecular property prediction through in-context learning, with up to 100% win rates on MoleculeACE tasks when paired with CheMeleon embeddings.
ReSS uses decision-tree scaffolds to fine-tune LLMs for faithful tabular reasoning, reporting up to 10% gains over baselines on medical and financial data.
Spline encodings for numerical features show task-dependent performance in tabular deep learning, with piecewise-linear encoding robust for classification and variable results for regression depending on spline family, knot strategy, and backbone.
TabPFN reaches AUC 0.892 for 3-year MCI-to-AD conversion on TADPOLE data and holds performance at N=50 training samples where XGBoost, Random Forest, LightGBM, and logistic regression degrade.
TabPFNv2.5 delivers 40x faster inference than Random Forest at 97% binary accuracy on TON IoT data, enabling a hybrid pipeline for real-time IoT threat screening in smart cities.
TabPFN maintains high ROC-AUC and structured attention under controlled additions of irrelevant features, nonlinear correlations, and mislabeled targets in binary classification.
citing papers explorer
-
Quantifying the Risk-Return Tradeoff in Forecasting
Forecast loss differentials are reframed as returns and assessed with risk-adjusted finance metrics, showing professional forecasters are harder to beat on risk-adjusted performance than on raw accuracy in US macro forecasting.
-
LGB+: A Macroeconomic Forecasting Road Test
LGB+ improves macroeconomic forecasts by letting linear basis functions compete with or alternate against tree updates inside gradient boosting, yielding native linear/nonlinear decomposition of predictions.