no code implementations • 1 Apr 2024 • Tianyang Li, Chao Wang, Hong Zhang
The interaction of image-level and prompt-level features is utilized to address the clutter interference.
1 code implementation • CVPR 2022 • Tianyang Li, Xin Wen, Yu-Shen Liu, Hua Su, Zhizhong Han
However, the local codes are constrained at discrete and regular positions like grid points, which makes the code positions difficult to be optimized and limits their representation ability.
no code implementations • CVPR 2020 • Xin Wen, Tianyang Li, Zhizhong Han, Yu-Shen Liu
Point cloud completion aims to infer the complete geometries for missing regions of 3D objects from incomplete ones.
no code implementations • 27 Apr 2020 • Tianyang Li, Zhongyi Han, Benzheng Wei, Yuanjie Zheng, Yanfei Hong, Jinyu Cong
However, robust and accurate screening of COVID-19 from chest X-rays is still a globally recognized challenge because of two bottlenecks: 1) imaging features of COVID-19 share some similarities with other pneumonia on chest X-rays, and 2) the misdiagnosis rate of COVID-19 is very high, and the misdiagnosis cost is expensive.
no code implementations • 24 Jan 2019 • Liu Liu, Tianyang Li, Constantine Caramanis
We define a natural condition we call the Robust Descent Condition (RDC), and show that if a gradient estimator satisfies the RDC, then Robust Hard Thresholding (IHT using this gradient estimator), is guaranteed to obtain good statistical rates.
no code implementations • 29 May 2018 • Liu Liu, Yanyao Shen, Tianyang Li, Constantine Caramanis
Our algorithm recovers the true sparse parameters with sub-linear sample complexity, in the presence of a constant fraction of arbitrary corruptions.
no code implementations • 23 May 2018 • Tianyang Li, Anastasios Kyrillidis, Liu Liu, Constantine Caramanis
We present a novel statistical inference framework for convex empirical risk minimization, using approximate stochastic Newton steps.
no code implementations • 21 May 2017 • Tianyang Li, Liu Liu, Anastasios Kyrillidis, Constantine Caramanis
We present a novel method for frequentist statistical inference in $M$-estimation problems, based on stochastic gradient descent (SGD) with a fixed step size: we demonstrate that the average of such SGD sequences can be used for statistical inference, after proper scaling.
no code implementations • NeurIPS 2015 • Tianyang Li, Adarsh Prasad, Pradeep K. Ravikumar
We consider the problem of binary classification when the covariates conditioned on the each of the response values follow multivariate Gaussian distributions.