Vision-language models (VLMs) can effectively transfer to various vision tasks via prompt
learning. Real-world scenarios often require adapting a model to multiple similar yet distinct …

This paper presents a novel approach to multiobjective algorithms aimed at modeling the
Pareto set using neural networks. Whereas previous methods mainly focused on identifying …

Previous multi-task dense prediction studies developed complex pipelines such as multi-
modal distillations in multiple stages or searching for task relational contexts for each task …

Graph neural architecture search (GraphNAS) has shown great potential for automatically
designing graph neural architectures for graph related tasks. However, multi-task GraphNAS …

While multi-task learning (MTL) has become an attractive topic, its training usually poses
more difficulties than the single-task case. How to successfully apply knowledge distillation …

We aim to train a multi-task model such that users can adjust the desired compute budget
and relative importance of task performances after deployment, without retraining. This …

In this paper, we present a new MTL framework that searches for structures optimized for
multiple tasks with diverse graph topologies and shares features among tasks. We design a …

Behavior of neural networks is irremediably determined by the specific loss and data used
during training. However it is often desirable to tune the model at inference time based on …

Multi-stage ranking pipelines have become widely used strategies in modern recommender
systems, where the final stage aims to return a ranked list of items that balances a number of …

The construction of machine learning models involves many bi-level multiobjective
optimization problems (BL-MOPs), where upper-level (UL) candidate solutions must be …