These embodied agents are typically trainedtabula rasain isolated worlds with limited complexity and diversity. Although highly performant, theyare specialist models that do not generalize beyond a narrowsetoftasks.

Many machine learning tasks that involve predicting an output response can be solved by training a weighted regression model.

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Text-driven image synthesis has made significant advancements with the development ofdiffusion models, transforming howvisual content isgenerated from text prompts. Despite these advances, text-driven image editing, a key area in computer graphics, faces unique challenges. A major challenge is making simultaneous edits across multiple objects or attributes.

The definition of agnostic learning above is classical.

Sequence-to-Sequence (Seq2Seq) neural text generation models, especially the pre-trained ones (e.g., BART and T5), have exhibited compelling performance on various natural language generation tasks. However,the black-box nature of these models limits their application in tasks where specific rules (e.g., controllable constraints, prior knowledge) need to be executed.

However, most existing meta-learning approaches only can learn from tasks without any constraint.