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Found 7 papers, 2 papers with code
The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyond
These competitions focus on accurate energy consumption forecasting and the importance of interpretability in understanding the underlying factors.
Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy
As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area.
Physics-informed neural networks for pathloss prediction
This paper introduces a physics-informed machine learning approach for pathloss prediction.
Automatic Preference Based Multi-objective Evolutionary Algorithm on Vehicle Fleet Maintenance Scheduling Optimization
A preference based multi-objective evolutionary algorithm is proposed for generating solutions in an automatically detected knee point region.
Interpretable Control by Reinforcement Learning
In this paper, three recently introduced reinforcement learning (RL) methods are used to generate human-interpretable policies for the cart-pole balancing benchmark.
Optimal deep neural networks for sparse recovery via Laplace techniques
Owing to the specific structure, it is shown that the centroid can be computed analytically by extending a recent result that facilitates the volume computation of polytopes via Laplace transformations.
Towards optimal nonlinearities for sparse recovery using higher-order statistics
In this context, we analyze the Bayesian mean-square-error (MSE) for two types of estimators: (i) a linear estimator and (ii) a structured estimator composed of a linear operator followed by a Cartesian product of univariate nonlinear mappings.
