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.
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Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios
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: 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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