Abstract: Feature selection is a pivotal step in machine learning, aimed at reducing feature dimensionality and improving model performance. Conventional feature selection methods, typically framed as ...

Two founders sat across from me, exhausted. They'd already worked with three coaches. Nothing changed. "We keep having good conversations," one told me, "but we're still stuck in the same place." This ...

Abstract: Scientific and data science applications are becoming more complex, with increasingly demanding computational and memory requirements during execution. Modern high performance computing (HPC ...

The person picked to replace Jerome H. Powell will be thrust into a credibility problem that will be difficult to escape. By Colby Smith Colby Smith covers the Federal Reserve. It was always going to ...

This camper was able to pass the tests but their algorithm didn't perform a swap of the smallest element and the first unsorted element. def selection_sort(items ...

The College Football Playoff selection committee is all over the place with its rankings. Week after week, it assembles the top 25 teams in college football, yet we’re left with ambiguity, ...

Bio-inspired computational methods have gained popularity recently. These methods mimic the seemingly complex behavior of organisms to tackle difficult and often overwhelming problems. For example, ...

High-dimensional data often contain noisy and redundant features, posing challenges for accurate and efficient feature selection. To address this, a dynamic multitask learning framework is proposed, ...

The original version of this story appeared in Quanta Magazine. If you want to solve a tricky problem, it often helps to get organized. You might, for example, break the problem into pieces and tackle ...