tabular data
PulseAugur coverage of tabular data — every cluster mentioning tabular data across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
-
pTNAS accelerates neural architecture search for tabular data
Researchers have developed pTNAS, a novel approach for progressive neural architecture search specifically designed for tabular data. This method efficiently identifies optimal neural network architectures, significantl…
-
New framework offers recourse for LLM tabular data decisions
Researchers have developed a new framework, ASR-ICL, for generating algorithmic recourse in tabular data when using in-context learning (ICL) with large language models. This framework addresses the gap in providing act…
-
New difficulty score enhances tabular data learning reliability
Researchers have developed a new method called Trajectory-based Difficulty Score (TDS) to estimate the difficulty of individual instances in tabular data learning. This score is derived from the cumulative prediction tr…
-
New RTTAD method enhances anomaly detection with risk-aware adaptation
Researchers have developed a new method called RTTAD to improve unsupervised anomaly detection in tabular data, particularly when the definition of 'normal' data shifts over time. The approach uses a dual-task learning …
-
Data Language Models offer native tabular data understanding, outperforming existing methods
Researchers have introduced Data Language Models (DLMs), a new class of foundation models designed to natively understand tabular data without requiring preprocessing. The first DLM, Schema-1, a 140M parameter model tra…