4 accepted papers
High-definition (HD) map construction plays a critical role in providing precise and comprehensive static environmental information for autonomous driving systems. However, the performance of existing methods degrades significantly under adverse conditions such as nighttime or rainy weather, where t
Autonomous exploration in an unknown environment is a critical task for quadruped robot applications. Traditional approaches based on stacked decision rules often suffer from inefficiency due to the greedy behavior. In this work, we propose a hybrid architecture that jointly employs a graph neural n
Standard cells form the building blocks of digital circuits, so their delay and power critically influence chip-level performance; yet characterization (can be understood as evaluation of cell delay and power) still relies on slow simulation sweeps, and many fast predictors ignore layout geometry, m…
For speech recognition in noisy environments, we propose a multi-task autoencoder which estimates not only clean speech features but also noise features from noisy speech. We introduce the deSpeeching autoencoder, which excludes speech signals from noisy speech, and combine it with the conventional…