AI RESEARCH
DenoGrad: A Gradient-Based Framework for Data Refinement in Tabular and Time-Series Learning
arXiv CS.LG
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ArXi:2511.10161v2 Announce Type: replace-cross In the Data-Centric Artificial Intelligence (AI) paradigm, improving data quality is essential for robust machine learning. However, many denoising methods rely on rigid statistical assumptions or require clean reference data, which limits their applicability in real-world scenarios. In this work, we propose DenoGrad, a gradient-based framework for data refinement that leverages a pretrained neural network to iteratively correct noisy observations by optimizing the input space while keeping the model fixed.