AI Briefing#tabular-data
The latest AI and developer news about #tabular-data, with the original source and a short summary.
The latest AI and developer news about #tabular-data, with the original source and a short summary.
The method corrects posterior probabilities at test time without retraining, maintaining performance on balanced data while fixing recall collapse in imbalanced scenarios.
Kumo Tabular achieves state-of-the-art accuracy on TabArena with ELO 1950 while being 17x faster than LimiX-2, trained entirely on synthetic data.
Prior Labs has released TabPFN-3.5, a foundation model that significantly improves performance in tabular data processing.
LG AI Research has released EXAONE Tabular, a lightweight tabular foundation model pre-trained without real data.
LG AI Research has released 'EXAONE Tabular', a foundation model for tabular data pre-trained exclusively on synthetic data without using any real-world data.
LG AI Research presented a new training methodology at NeurIPS 2023 to improve deep learning performance on Tabular Data.
LG AI Research has unveiled a self-supervised learning technique using Binning to overcome the limitations of Tabular Data.
LG AI Research has proposed a new technique that uses B-spline to dynamically normalize tabular data according to its complex distributions.
LG AI Research proposed a Tabular data normalization technique using B-splines that dynamically transforms according to data characteristics.
LG AI Research has developed AGATa technology that improves training performance while preserving the structure of tabular data.
LG AI Research unveiled AGATa, a new augmentation technique that maximizes training performance while preserving the structural characteristics of tabular data.
LG AI Research presented RaTab, a technology that improves the self-supervised learning efficiency of tabular data, at AAAI-25.
LG AI Research presented RaTab, a technique that maximizes learning effectiveness while preserving core information in tabular data, at AAAI-25.
Google Research has unveiled TabFM, a model that processes tabular data without separate fine-tuning.
Google has released TabFM, a model that can make predictions for classification and regression tasks on tabular data without separate training.
A study revealed the mathematical correspondence between decision trees and diffusion models, unifying the two models.
TabPFN has released a foundation model for tabular data along with extension features.
Predicted 58 body params from 8 survey inputs, putting the forward pass into the loss.
TabPFN is a pretrained Transformer that produces fast baselines on small tabular data.
8Percent's CSS team experimented with credit scoring using CB data, XGBoost, and Transformer.