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Found 11 papers, 3 papers with code
UNIFY: a Unified Policy Designing Framework for Solving Constrained Optimization Problems with Machine Learning
Our approach relies on a clever decomposition of the policy in two stages, namely an unconstrained ML model and a CO problem, to take advantage of the strength of each approach while compensating for its weaknesses.
Deep Learning for Virus-Spreading Forecasting: a Brief Survey
The advent of the coronavirus pandemic has sparked the interest in predictive models capable of forecasting virus-spreading, especially for boosting and supporting decision-making processes.
An Analysis of Regularized Approaches for Constrained Machine Learning
Regularization-based approaches for injecting constraints in Machine Learning (ML) were introduced to improve a predictive model via expert knowledge.
Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey
Deep Learning (DL) models proved themselves to perform extremely well on a wide variety of learning tasks, as they can learn useful patterns from large data sets.
Teaching the Old Dog New Tricks: Supervised Learning with Constraints
Adding constraint support in Machine Learning has the potential to address outstanding issues in data-driven AI systems, such as safety and fairness.
Injecting Domain Knowledge in Neural Networks: a Controlled Experiment on a Constrained Problem
Given enough data, Deep Neural Networks (DNNs) are capable of learning complex input-output relations with high accuracy.
Injective Domain Knowledge in Neural Networks for Transprecision Computing
Machine Learning (ML) models are very effective in many learning tasks, due to the capability to extract meaningful information from large data sets.
Combining Learning and Optimization for Transprecision Computing
The ML model learns the relation between variables precision and the output error; this information is then embedded in the MP focused on minimizing the number of bits.
Distributed, Parallel, and Cluster Computing
Anomaly Detection using Autoencoders in High Performance Computing Systems
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components.
Boosting Combinatorial Problem Modeling with Machine Learning
The three pillars of constraint satisfaction and optimization problem solving, i. e., modeling, search, and optimization, can exploit ML techniques to boost their accuracy, efficiency and effectiveness.
Multi-Criteria Optimal Planning for Energy Policies in CLP
In the policy making process a number of disparate and diverse issues such as economic development, environmental aspects, as well as the social acceptance of the policy, need to be considered.
