Spline encodings for numerical features show task-dependent performance in tabular deep learning, with piecewise-linear encoding robust for classification and variable results for regression depending on spline family, knot strategy, and backbone.
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DDAP is a controlled agentic framework that guides non-experts via four LLM-assisted stages to construct competitive AI pipelines for business, biology, and health domains.
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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From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning
Spline encodings for numerical features show task-dependent performance in tabular deep learning, with piecewise-linear encoding robust for classification and variable results for regression depending on spline family, knot strategy, and backbone.
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From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists
DDAP is a controlled agentic framework that guides non-experts via four LLM-assisted stages to construct competitive AI pipelines for business, biology, and health domains.
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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.