  In this work, we propose CARLS, a novel framework for augmenting the capacity
of existing deep learning frameworks by enabling multiple components -- model
trainers, knowledge makers and knowledge banks -- to concertedly work together
in an asynchronous fashion across hardware platforms. The proposed CARLS is
particularly suitable for learning paradigms where model training benefits from
additional knowledge inferred or discovered during training, such as node
embeddings for graph neural networks or reliable pseudo labels from model
predictions. We also describe three learning paradigms -- semi-supervised
learning, curriculum learning and multimodal learning -- as examples that can
be scaled up efficiently by CARLS. One version of CARLS has been open-sourced
and available for download at:
https://github.com/tensorflow/neural-structured-learning/tree/master/research/carls
