A growing body of recent research and industry commentary suggests that a shift in how organisations approach site ...

Abstract: Current distributed processing of site landscape images suffers from uneven sample distribution and multi-task gradient conflicts, which can easily lead to poor detection performance in ...

In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...

Most robot headlines follow a familiar script: a machine masters one narrow trick in a controlled lab, then comes the bold promise that everything is about to change. I usually tune those stories out.

These days, large language models can handle increasingly complex tasks, writing complex code and engaging in sophisticated ...

Abstract: Multi-task learning (MTL) is a standard learning paradigm in machine learning. The central idea of MTL is to capture the shared knowledge among multiple tasks for mitigating the problem of ...

Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.

Keeley tells PEOPLE he was only able to understand "the cadence and the rhythm" of the specific "Delco" dialect after arriving in Philadelphia Tabitha Parent is a writer at PEOPLE covering ...

Deep learning’s (DL’s) promise and appeal is algorithmic amalgamation of all available data to achieve model generalization and prediction of complex systems. Thus, there is a need to design ...