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Found 9 papers, 6 papers with code
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks
Similar to large language models, PFNs make use of pretraining and in-context learning to achieve strong performance on new tasks in a single forward pass.
Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data Fitted Networks
Tabular classification has traditionally relied on supervised algorithms, which estimate the parameters of a prediction model using its training data.
Exploring Dataset-Scale Indicators of Data Quality
Modern computer vision foundation models are trained on massive amounts of data, incurring large economic and environmental costs.
ArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models
We introduce ArcheType, a simple, practical method for context sampling, prompt serialization, model querying, and label remapping, which enables large language models to solve CTA problems in a fully zero-shot manner.
Ranked #1 on
Column Type Annotation
on WDC SOTAB
(Weighted F1 metric)
Distributionally Robust Classification on a Data Budget
To our knowledge, this is the first result showing (near) state-of-the-art distributional robustness on limited data budgets.
When Do Neural Nets Outperform Boosted Trees on Tabular Data?
To this end, we conduct the largest tabular data analysis to date, comparing 19 algorithms across 176 datasets, and we find that the 'NN vs. GBDT' debate is overemphasized: for a surprisingly high number of datasets, either the performance difference between GBDTs and NNs is negligible, or light hyperparameter tuning on a GBDT is more important than choosing between NNs and GBDTs.
LiT Tuned Models for Efficient Species Detection
Recent advances in training vision-language models have demonstrated unprecedented robustness and transfer learning effectiveness; however, standard computer vision datasets are image-only, and therefore not well adapted to such training methods.
Caption supervision enables robust learners
Vision language (VL) models like CLIP are robust to natural distribution shifts, in part because CLIP learns on unstructured data using a technique called caption supervision; the model inteprets image-linked texts as ground-truth labels.
A Meta-Analysis of Distributionally-Robust Models
State-of-the-art image classifiers trained on massive datasets (such as ImageNet) have been shown to be vulnerable to a range of both intentional and incidental distribution shifts.
