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Yining Ding

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2 papers

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ICRA Conference 2024 Conference Paper

CURL-MAP: Continuous Mapping and Positioning with CURL Representation †

Kaicheng Zhang
Yining Ding
Shida Xu
Ziyang Hong 0001
Xianwen Kong
Sen Wang 0002

Maps of LiDAR Simultaneous Localisation and Mapping (SLAM) are often represented as point clouds. They usually take up a huge amount of storage space for large-scale environments, otherwise much structural detail may not be kept. In this paper, a novel paradigm of LiDAR mapping and odometry is designed by leveraging the Continuous and Ultra-compact Representation of LiDAR (CURL) proposed in [1]. Termed CURL-MAP (Mapping and Positioning), the proposed approach can not only reconstruct 3D maps with a continuously varying density but also efficiently reduce map storage space by using CURL’s spherical harmonics implicit encoding. Different from the popular Iterative Closest Point (ICP) based LiDAR odometry techniques, CURL-MAP formulates LiDAR pose estimation as a unique optimisation problem tailored for CURL. Experiment evaluation shows that CURL-MAP achieves state-of-the-art 3D mapping results and competitive LiDAR odometry accuracy. We will release the CURL-MAP codes for the community.

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ICLR Conference 2021 Conference Paper

CPT: Efficient Deep Neural Network Training via Cyclic Precision

Yonggan Fu
Han Guo
Meng Li 0004
Xin Yang
Yining Ding
Vikas Chandra
Yingyan Celine Lin

Low-precision deep neural network (DNN) training has gained tremendous attention as reducing precision is one of the most effective knobs for boosting DNNs' training time/energy efficiency. In this paper, we attempt to explore low-precision training from a new perspective as inspired by recent findings in understanding DNN training: we conjecture that DNNs' precision might have a similar effect as the learning rate during DNN training, and advocate dynamic precision along the training trajectory for further boosting the time/energy efficiency of DNN training. Specifically, we propose Cyclic Precision Training (CPT) to cyclically vary the precision between two boundary values which can be identified using a simple precision range test within the first few training epochs. Extensive simulations and ablation studies on five datasets and eleven models demonstrate that CPT's effectiveness is consistent across various models/tasks (including classification and language modeling). Furthermore, through experiments and visualization we show that CPT helps to (1) converge to a wider minima with a lower generalization error and (2) reduce training variance which we believe opens up a new design knob for simultaneously improving the optimization and efficiency of DNN training.

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