Compositional Generalization
Merging parameter-efficient task experts has recently gained growing attention
as a way to build modular architectures that can be …
Merging parameter-efficient task experts has recently gained growing attention as a way to build modular architectures that can be …
Embodied agents face significant challenges when tasked with performing actions in diverse environments, particularly in generalizing …
Pretrained large generative language models have shown great performance on many tasks, but exhibit low compositional generalization …
A popular approach for sequential decision-making is to perform simulator-based search
guided with Machine Learning (ML) methods like …
The ALFRED environment features embodied instruction following tasks in simulated home environments. However, end-to-end deep learning …
A popular approach for sequential decision-making is to perform simulator-based search
guided with Machine Learning (ML) methods like …
Compositionality, or the ability to combine familiar units like words into novel phrases and sentences, has been the focus of intense …
Like humans devoid of imagination, current machine learning systems lack the ability to adapt to new, unexpected situations by …
Modularity is a compelling solution to continual learning (CL), the problem of modeling sequences of related tasks. Learning and then …