OpenML CC18
PulseAugur coverage of OpenML CC18 — every cluster mentioning OpenML CC18 across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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Table2Image model offers lightweight tabular learning with proxy representations
Researchers have introduced Table2Image, a novel lightweight model designed for tabular data learning. This model generates intermediate proxy representations to balance predictive performance, parameter efficiency, and…
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New CoCo loss function enhances embedding structure and convergence
Researchers have developed a new loss function called CoCo, designed to create normalized and well-structured data representations. CoCo encourages classes to collapse internally while contrasting with other classes, ai…
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New CoCo loss function enhances embedding quality and training speed
Researchers have introduced CoCo, a novel loss function designed to create normalized and well-structured data representations. This function promotes intra-class collapse and inter-class contrast, enabling neural netwo…
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New research compares ensemble methods for tabular classification · arXiv paper
A new research paper published on arXiv details a comparison of parallel heterogeneous ensemble methods for tabular classification tasks. The study analyzed 56 small-to-medium tabular classification tasks from OpenML CC…
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New LUCoS method improves tabular foundation model context selection
A new research paper introduces LUCoS, a method for unsupervised context selection in tabular foundation models. LUCoS addresses the challenge of selecting instances for labeling in low-label tabular learning by utilizi…
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Ternary decision trees add uncertainty zones to improve accuracy
Researchers have introduced ternary decision trees, which enhance standard binary decision trees by incorporating an uncertainty zone around decision boundaries. This zone allows for weighted blending of predictions fro…
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Researchers distill large AI models into faster CPU-ready gradient-boosted trees
Researchers have developed a method to distill large tabular foundation models (TFMs) into smaller, faster gradient-boosted tree models that can run on CPUs. This technique addresses the latency issue of TFMs, which are…