This content explores architecture learning for multi-task models, leveraging a latent approach. It focuses on optimizing designs that can efficiently handle multiple objectives simultaneously.
This research explores the application of deep neural networks in survival analysis, employing a multi-task framework. The approach aims to enhance the prediction and modeling of time-to-event data by leveraging complex neural network architectures.
O LiME (Lightweight Mixture of Experts) propõe uma nova abordagem para MoE-PEFT, utilizando modulação leve de um único módulo PEFT compartilhado em vez de adaptadores separados por especialista. Isso reduz significativamente os parâmetros, introduz roteamento de parâmetros zero e generaliza para qualquer método PEFT, superando as limitações de escalabilidade e aplicabilidade.
This paper introduces PEML, a method for parameter-efficient multi-task learning with optimized continuous prompts for Large Language Models. It addresses the shortcomings of existing PEFT methods like LoRA and Prefix Tuning by enabling more efficient fine-tuning across multiple tasks and facilitating resource consolidation.
Machine unlearning typically focuses on single-task settings, but modern AI models often operate in multi-task environments with shared backbones, leading to unintended interference when data is removed. This paper introduces multi-task unlearning, proposing an interference-aware framework that uses task-aware gradient projection to address task-level and instance-level interference.