Recent advances in deep learning have allowed artificial intelligence (AI) to reach near
human-level performance in many sensory, perceptual, linguistic, and cognitive tasks. There …

A well-performed deep learning model in image segmentation relies on a large number of
labeled data. However, it is hard to obtain sufficient high-quality raw data in industrial …

Changing how pre-trained models behave--eg, improving their performance on a
downstream task or mitigating biases learned during pre-training--is a common practice …

In this work, we explore" prompt tuning", a simple yet effective mechanism for learning" soft
prompts" to condition frozen language models to perform specific downstream tasks. Unlike …

Multi-task learning can leverage information learned by one task to benefit the training of
other tasks. Despite this capacity, naively training all tasks together in one model often …

Multi-task learning (MTL) is a subfield of machine learning in which multiple tasks are
simultaneously learned by a shared model. Such approaches offer advantages like …

There has been growing interest in parameter-efficient methods to apply pre-trained
language models to downstream tasks. Building on the Prompt Tuning approach of Lester et …

Averaging the parameters of models that have the same architecture and initialization can
provide a means of combining their respective capabilities. In this paper, we take the …

Abstract Model-agnostic meta learning (MAML) is currently one of the dominating
approaches for few-shot meta-learning. Albeit its effectiveness, the optimization of MAML …

Many computer vision applications require solving multiple tasks in real-time. A neural
network can be trained to solve multiple tasks simultaneously using multi-task learning. This …