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Creators/Authors contains: "Wang, Z."

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  1. ; ; ; ( , ACM-SIAM Symposium on Discrete Algorithms (SODA))
    Free, publicly-accessible full text available April 1, 2023
  2. ; ; ; ( , Journal of fluid mechanics)
    Free, publicly-accessible full text available February 24, 2023
  3. ; ( , International Conference on Machine Learning)
    Free, publicly-accessible full text available January 1, 2023
  4. ; ; ; ( , 2021 IEEE Symposium Series on Computational Intelligence (SSCI))
    Free, publicly-accessible full text available January 24, 2023
  5. ; ; ; ; ; ; ; ( , IEEE transactions on computers)
    Free, publicly-accessible full text available March 1, 2023
  6. ; ; ( , Proceedings of the 39th International Conference on Machine Learning (ICML-2022))
    Free, publicly-accessible full text available January 1, 2023
  7. ; ; ( , IEEE International Conference on Computer Vision workshops)
    Monocular estimation of 3d human pose has attracted in- creased attention with the availability of large ground-truth motion capture datasets. However, the diversity of training data available is limited and it is not clear to what extent methods generalize outside the specific datasets they are trained on. In this work we carry out a systematic study of the diversity and biases present in specific datasets and its e↵ect on cross-dataset generalization across a compendium of 5 pose datasets. We specifically focus on systematic di↵erences in the distri- bution of camera viewpoints relative to a body-centered coordinate frame. Based on thismore »observation, we propose an auxiliary task of predicting the camera viewpoint in addition to pose. We find that models trained to jointly predict viewpoint and pose systematically show significantly improved cross-dataset generalization.« less
    Free, publicly-accessible full text available October 11, 2022
  8. ; ; ; ; ( , IEEE transactions on knowledge and data engineering)
    Free, publicly-accessible full text available November 1, 2022
  9. ; ; ; ; ; ; ( , IEEE Big Data Conference)
    Free, publicly-accessible full text available December 1, 2022
  10. ; ; ; ; ; ( , Proceedings of the Symposium on Operating Systems Principles)
    This paper presents Basil, the first transactional, leaderless Byzantine Fault Tolerant key-value store. Basil leverages ACID transactions to scalably implement the abstraction of a trusted shared log in the presence of Byzantine actors. Unlike traditional BFT approaches, Basil executes non-conflicting operations in parallel and commits transactions in a sin- gle round-trip during fault-free executions. Basil improves throughput over traditional BFT systems by four to five times, and is only four times slower than TAPIR, a non-Byzantine replicated system. Basil’s novel recovery mechanism further minimizes the impact of failures: with 30% Byzantine clients, throughput drops by less than 25% in themore »worst-case.« less
    Free, publicly-accessible full text available October 1, 2022