Existing systems mostly compress features at pixel level and ignore the characteristics of feature structure, which could be further exploited for more efficient compression.

Despite diverse modeling techniques, existing models are black boxes and fail to provide insights and explanations about their representations.

Weight-sharing quantization has emerged as a technique to reduce energy expenditure during inference in large neural networks by constraining their weights to a limited set of values.

Bird's-eye-view (BEV) map layout estimation requires an accurate and full understanding of the semantics for the environmental elements around the ego car to

BERT, and GPT models show GRACE's state-of-the-art performance in making

We tackle the task of conditional music generation.