Minimize experimentation overhead through dataset selection, ensemble feature attention, and feature selection with reduced subset sizes

In large-scale ML, data size becomes a critical variable, especially in the context of large companies, where models already exist and are hard to change and fine-tune. Time to market and model quality are essential metrics, thus looking for ways to select, prune and augment the input data while treating the model as a black box can speed up the...

Link adaptation and equalization for underwater acoustic communication using machine learning

The underwater acoustic environment is amongst the most challenging mediums for wireless communications. The three distinct challenges of underwater acoustic communication are the low and nonuniform propagation speed, frequency-dependent attenuation and time-varying multipath propagation.<br/>To cope with these challenges, physical layer...

High-dimensional numerical optimization of fiber reinforced polymers with variational autoencoders and Bayesian optimization

Designing engineering structures relies on accurate numerical simulations to predict the behaviour of a structure before its realization. In the design process, many variables influence the final structure. There is an incentive for optimizing the design based on the desire for cheaper structures and less material usage. Although a wide variety...