The MHHTOF framework uses momentum-constrained heuristic optimization and residual DRL to achieve faster convergence, lower stable costs, and safer velocity profiles than baselines in visually impaired navigation scenarios.
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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.
LightGBM outperforms other models for electricity price forecasting across Norway's bidding zones, with lagged prices and calendar features often sufficient but external features key in stressed regimes.
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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.