From the EIC: Robust Machine Learning

From the EIC: Manufacturing test woes

WITH THE MANUFACTURING CAPACITY of thousands of wafers per week, and thousands of dies per wafer, today's silicon comes out of fab really fast—and furious—sporting device I/Os in the gigabit-per-second range. But it is increasingly going into high-volume and low-cost consumer electronic parts, exhausting the available time and money to test these parts. Little wonder that the 2003 International...

Simulation of Scour Pattern Around Cross-Vane Structures Using Outlier Robust Extreme Learning Machine

In this research, the scour hole depth at the downstream of cross-vane structures with different shapes (i.e., J, I, U, and W) was simulated utilizing a modern artificial intelligence method entitled "Outlier Robust Extreme Learning Machine (ORELM)". The observational data were divided into two groups: training (70%) and test (30%). Then, using the input parameters including the ratio of the st...

A Robust and Regularized Extreme Learning Machine

In a moment when the study of outlier robustness within Extreme Learning Machine is still in its infancy, we propose a method that combines maximization of the hidden layer’s information transmission, through Batch Intrinsic Plasticity (BIP), with robust estimation of the output weights. This method named R-ELM/BIP generates a reliable solution in the presence of corrupted data with a good gene...

Efficient and Robust Automated Machine Learning

The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand, and also set their respective hyperparameters. Recent work has started to t...

A Robust Online Sequential Extreme Learning Machine