1 code implementation • 17 Jun 2024 • Jieyu Zhang, Weikai Huang, Zixian Ma, Oscar Michel, Dong He, Tanmay Gupta, Wei-Chiu Ma, Ali Farhadi, Aniruddha Kembhavi, Ranjay Krishna
As a result, when a developer wants to identify which models to use for their application, they are overwhelmed by the number of benchmarks and remain uncertain about which benchmark's results are most reflective of their specific use case.
no code implementations • 9 Apr 2024 • Lindsey Linxi Wei, Chung Yik Edward Yeung, Hongjian Yu, Jingchuan Zhou, Dong He, Magdalena Balazinska
We demonstrate MaskSearch, a system designed to accelerate queries over databases of image masks generated by machine learning models.
no code implementations • 3 May 2023 • Dong He, Jieyu Zhang, Maureen Daum, Alexander Ratner, Magdalena Balazinska
Machine learning tasks over image databases often generate masks that annotate image content (e. g., saliency maps, segmentation maps, depth maps) and enable a variety of applications (e. g., determine if a model is learning spurious correlations or if an image was maliciously modified to mislead a model).
no code implementations • 7 Mar 2023 • Maureen Daum, Enhao Zhang, Dong He, Stephen Mussmann, Brandon Haynes, Ranjay Krishna, Magdalena Balazinska
We introduce VOCALExplore, a system designed to support users in building domain-specific models over video datasets.
no code implementations • 3 Mar 2022 • Dong He, Supun Nakandala, Dalitso Banda, Rathijit Sen, Karla Saur, Kwanghyun Park, Carlo Curino, Jesús Camacho-Rodríguez, Konstantinos Karanasos, Matteo Interlandi
Finally, TQP can accelerate queries mixing ML predictions and SQL end-to-end, and deliver up to 9$\times$ speedup over CPU baselines.
1 code implementation • 6 Apr 2021 • Dong He, Maureen Daum, Walter Cai, Magdalena Balazinska
We design, implement, and evaluate DeepEverest, a system for the efficient execution of interpretation by example queries over the activation values of a deep neural network.
no code implementations • 5 Apr 2021 • Dong He, Jie Cheng, Jong-Hwan Kim
This paper proposes the GSECnet - Ground Segmentation network for Edge Computing, an efficient ground segmentation framework of point clouds specifically designed to be deployable on a low-power edge computing unit.
no code implementations • 10 Dec 2019 • Xuewen Yao, Dong He, Tiancheng Jing, Kaya de Barbaro
It has been suggested in developmental psychology literature that the communication of affect between mothers and their infants correlates with the socioemotional and cognitive development of infants.