Publications
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Encoding global expertise in self & active-learning machines: Improve the quality of decisions made by machines Vlerick Business School
A hybrid heuristic for the machine scheduling problem with parallel machines Ghent University
We consider the unrelated parallel machine scheduling problem with a makespan objective. We discuss two heuristic approaches and the hybridization of these heuristics with a truncated branch-and-bound procedure. We compare the performances of these heuristics on standard data available in literature and examine the influence of the different heuristic parameters. The computational experiments reveal that the hybrid heuristics are able to compete ...
Ultrafast Boltzmann Sampling using Photonic Ising Machines for Machine Learning Vrije Universiteit Brussel
Ising machines have emerged as a promising computational concept that specializes in solving resource intensive optimization problems. Their operation is based on mapping a problem’s cost function to a network of coupled Ising spins whose energy is described by the Ising Hamiltonian. Such a spin system can be emulated on a network of bistable analog oscillators and the tendency of this system to evolve to the lowest energy configuration is then ...
Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning Vrije Universiteit Brussel
Ising machines are a promising non-von-Neumann computational concept for neural network training and combinatorial optimization. However, while various neural networks can be implemented with Ising machines, their inability to perform fast statistical sampling makes them inefficient for training neural networks compared to digital computers. Here, we introduce a universal concept to achieve ultrafast statistical sampling with analog Ising ...
Uncertainty quantification and sensitivity analysis in electrical machines with stochastically varying machine parameters KU Leuven
© 2015 IEEE. Electrical machines that are produced in mass production suffer from stochastic deviations introduced during the production process. These variations can cause undesired and unanticipated side-effects. Until now, only worst case analysis and Monte Carlo simulation have been used to predict such stochastic effects and to reduce their influence on the machine behavior. However, these methods have proven to be either inaccurate or very ...
Augmented Reality Based Machine Monitoring for Legacy Machines: a retrofitting use case KU Leuven
Monitoring a machine and the insight it provides for appropriate maintenance is of prime importance for the modern industry. While this is fully supported in Industry 4.0, many manufacturing units today are unable to use its technologies. One of the major reasons behind that is the lack of onboard sensors to capture and communicate appropriate data from a legacy machine. Retrofitting is considered an efficient solution to include legacy machines ...
Improving the performance of machine learning models for biotechnology : the quest for deus ex machina Ghent University
Machine learning is becoming an integral part of the Design-Build-Test-Learn cycle in biotechnology. Machine learning models learn from collected datasets such as omics data and predict a defined outcome, which has led to both production improvements and predictive tools in the field. Robust prediction of the behavior of microbial cell factories and production processes not only greatly increases our understanding of the function of such ...