Abstract: With the increasing complexity of tasks in the legal domain, traditional methods struggle to meet the demands of multi-task scenarios and face significant bottlenecks in task accuracy and ...

Evolutionary algorithms (EAs) have long provided a flexible framework for solving challenging optimisation problems by mimicking natural evolutionary processes. When combined with multitask ...

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, ...

Herein, we have offered the code of our APMTO and the reproduced codes of AEMTO and OTMTO. The codes of the three algorithms are all organized with the same structure. By running the main.py file, the ...

Purdue faculty dedicate countless hours to exploring the frontiers of their respective fields, pushing the boundaries of knowledge and contributing to the ever-evolving landscape of academia. To ...