no code implementations • 21 Apr 2024 • Zhihang Li, Zhao Song, Weixin Wang, Junze Yin, Zheng Yu
Leverage score is a fundamental problem in machine learning and theoretical computer science.
no code implementations • 26 Nov 2023 • Zhihang Li, Zhao Song, Zifan Wang, Junze Yin
Our main results involve analyzing the convergence properties of an approximate Newton method used to minimize the regularized training loss.
no code implementations • 17 Jul 2023 • Yichuan Deng, Zhihang Li, Sridhar Mahadevan, Zhao Song
We demonstrate the convergence of our algorithm, highlighting its effectiveness in efficiently computing gradients for large-scale LLMs.
no code implementations • 20 Apr 2023 • Yichuan Deng, Zhihang Li, Zhao Song
One of the key computation in LLMs is the softmax unit.
no code implementations • 28 Mar 2023 • Zhihang Li, Zhao Song, Tianyi Zhou
In this paper, we make use of the input sparsity and purpose an algorithm that use $\log ( \|x_0 - x^*\|_2 / \epsilon)$ iterations and $\widetilde{O}(\mathrm{nnz}(A) + d^{\omega} )$ per iteration time to solve the problem.
1 code implementation • 12 Dec 2021 • Lin Wan, Qianyan Jing, Zongyuan Sun, Chuang Zhang, Zhihang Li, Yehansen Chen
Much of that is due to the notorious modality bias training issue brought by the single-modality ImageNet pre-training, which might yield RGB-biased representations that severely hinder the cross-modality image retrieval.
Contrastive Learning Cross-Modality Person Re-identification +3
no code implementations • 15 Jun 2021 • Lin Wan, Zongyuan Sun, Qianyan Jing, Yehansen Chen, Lijing Lu, Zhihang Li
Specifically, we propose to build a semantic-aligned complete graph into which all cross-modality images can be mapped via a pose-adaptive graph construction mechanism.
no code implementations • CVPR 2021 • Yehansen Chen, Lin Wan, Zhihang Li, Qianyan Jing, Zongyuan Sun
RGB-Infrared person re-identification (RGB-IR ReID) is a challenging cross-modality retrieval problem, which aims at matching the person-of-interest over visible and infrared camera views.
no code implementations • CVPR 2020 • Zhihang Li, Teng Xi, Jiankang Deng, Gang Zhang, Shengzhao Wen, Ran He
(3) How to learn these correlations with a small number of samples?
1 code implementation • ICCV 2019 • Fan Zhang, Yanqin Chen, Zhihang Li, Zhibin Hong, Jingtuo Liu, Feifei Ma, Junyu Han, Errui Ding
Recent works have made great progress in semantic segmentation by exploiting richer context, most of which are designed from a spatial perspective.
4 code implementations • 31 Mar 2019 • Zhihang Li, Xu Tang, Junyu Han, Jingtuo Liu, Ran He
With the rapid development of deep convolutional neural network, face detection has made great progress in recent years.
3 code implementations • NeurIPS 2018 • Huaibo Huang, Zhihang Li, Ran He, Zhenan Sun, Tieniu Tan
On the other hand, the inference model is encouraged to classify between the generated and real samples while the generator tries to fool it as GANs.
no code implementations • 13 Dec 2017 • Zhihang Li, Yibo Hu, Ran He
We treat the face completion and corruption as disentangling and fusing processes of clean faces and occlusions, and propose a jointly disentangling and fusing Generative Adversarial Network (DF-GAN).
no code implementations • 15 Jun 2017 • Zhihe Lu, Zhihang Li, Jie Cao, Ran He, Zhenan Sun
Face synthesis has been a fascinating yet challenging problem in computer vision and machine learning.