VIP-COP is a black-box method that optimizes context for tabular foundation models by ranking and selecting high-value samples and features via online KernelSHAP regression, outperforming baselines on large high-dimensional data.
MIT press Cambridge, MA, USA
2 Pith papers cite this work. Polarity classification is still indexing.
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GourNet is a CNN with 683,656 parameters that achieves 97% accuracy classifying mango leaf images into eight disease and healthy classes on the MangoLeafBD dataset.
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VIP-COP: Context Optimization for Tabular Foundation Models
VIP-COP is a black-box method that optimizes context for tabular foundation models by ranking and selecting high-value samples and features via online KernelSHAP regression, outperforming baselines on large high-dimensional data.
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GourNet: A CNN-Based Model for Mango Leaf Disease Detection
GourNet is a CNN with 683,656 parameters that achieves 97% accuracy classifying mango leaf images into eight disease and healthy classes on the MangoLeafBD dataset.