3 accepted papers
In multi-robot systems operating under uncertainty, maintaining safe inter-robot distances while avoiding collisions with obstacles is crucial. Although chance-constrained methods have been widely adopted to handle such uncertainties, existing approaches often exhibit conservatism due to their relia…
Using deep neural networks (DNNs) in machine learning tasks is promising in delivering high-quality results but challenging to meet stringent latency requirements and energy constraints because of the memory-bound and the compute-bound execution pattern of DNNs. We propose a big-little dual-module i…
This paper presents a hardness-aware deep metric learning (HDML) framework. Most previous deep metric learning methods employ the hard negative mining strategy to alleviate the lack of informative samples for training. However, this mining strategy only utilizes a subset of training data, which may…