Transformers
Merging parameter-efficient task experts has recently gained growing attention
as a way to build modular architectures that can be …
Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). The performance of such models …
Merging parameter-efficient task experts has recently gained growing attention as a way to build modular architectures that can be …
We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including …
We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including …
Generating high-quality summaries for chat dialogs often requires large labeled datasets. We propose a method to efficiently use …
We study the utility of incorporating entity type abstractions into pre-trained Transformers and test these methods on four NLP tasks …
The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. …
Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A major challenge is to efficiently …
Recent research suggests that systematic generalization in natural language understanding remains a challenge for state-of-the-art …