DenoGrad refines noisy tabular and time-series data by optimizing inputs via gradients from a fixed model, yielding better downstream predictions on ten real-world datasets while preserving data statistics.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
Using full-recording acoustic features and wrapper selection, the Extreme Minimal Learning Machine provides competitive dementia classification accuracy at lower computational cost on ADReSS and Pitt datasets.
citing papers explorer
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DenoGrad: A Gradient-Based Framework for Data Refinement in Tabular and Time-Series Learning
DenoGrad refines noisy tabular and time-series data by optimizing inputs via gradients from a fixed model, yielding better downstream predictions on ten real-world datasets while preserving data statistics.
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Dementia classification from spontaneous speech using wrapper-based feature selection
Using full-recording acoustic features and wrapper selection, the Extreme Minimal Learning Machine provides competitive dementia classification accuracy at lower computational cost on ADReSS and Pitt datasets.