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Found 4 papers, 2 papers with code
Loss Shaping Constraints for Long-Term Time Series Forecasting
We propose a practical Primal-Dual algorithm to tackle it, and demonstrate that the proposed approach exhibits competitive average performance in time series forecasting benchmarks, while shaping the distribution of errors across the predicted window.
Resilient Constrained Learning
This paper presents a constrained learning approach that adapts the requirements while simultaneously solving the learning task.
Neural Networks with Quantization Constraints
Furthermore, we show that dual variables indicate the sensitivity of the objective with respect to constraint perturbations.
Automatic Data Augmentation via Invariance-Constrained Learning
Despite its ubiquity, its effectiveness depends on the choices of which transformations to apply, when to do so, and how often.
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