no code implementations • 30 Apr 2024 • Longlong Jing, Ruichi Yu, Xu Chen, Zhengli Zhao, Shiwei Sheng, Colin Graber, Qi Chen, Qinru Li, Shangxuan Wu, Han Deng, Sangjin Lee, Chris Sweeney, Qiurui He, Wei-Chih Hung, Tong He, Xingyi Zhou, Farshid Moussavi, Zijian Guo, Yin Zhou, Mingxing Tan, Weilong Yang, CongCong Li
In this paper, we propose STT, a Stateful Tracking model built with Transformers, that can consistently track objects in the scenes while also predicting their states accurately.
no code implementations • 12 Jul 2023 • Xuewei Wang, Qiang Jin, Shengyu Huang, Min Zhang, Xi Liu, Zhengli Zhao, Yukun Chen, Zhengyu Zhang, Jiyan Yang, Ellie Wen, Sagar Chordia, Wenlin Chen, Qin Huang
In order to pass better ads from the early to the final stage ranking, we propose a multi-task learning framework for early stage ranking to capture multiple final stage ranking components (i. e. ads clicks and ads quality events) and their task relations.
1 code implementation • 22 Oct 2020 • Murphy Yuezhen Niu, Andrew M. Dai, Li Li, Augustus Odena, Zhengli Zhao, Vadim Smelyanskyi, Hartmut Neven, Sergio Boixo
Given a quantum circuit, a quantum computer can sample the output distribution exponentially faster in the number of bits than classical computers.
no code implementations • 4 Jun 2020 • Zhengli Zhao, Zizhao Zhang, Ting Chen, Sameer Singh, Han Zhang
We provide new state-of-the-art results for conditional generation on CIFAR-10 with both consistency loss and contrastive loss as additional regularizations.
2 code implementations • NeurIPS 2020 • Samarth Sinha, Zhengli Zhao, Anirudh Goyal, Colin Raffel, Augustus Odena
We introduce a simple (one line of code) modification to the Generative Adversarial Network (GAN) training algorithm that materially improves results with no increase in computational cost: When updating the generator parameters, we simply zero out the gradient contributions from the elements of the batch that the critic scores as `least realistic'.
no code implementations • 11 Feb 2020 • Zhengli Zhao, Sameer Singh, Honglak Lee, Zizhao Zhang, Augustus Odena, Han Zhang
Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator.
no code implementations • 2 Oct 2019 • Zhengli Zhao, Nicolas Papernot, Sameer Singh, Neoklis Polyzotis, Augustus Odena
Broad adoption of machine learning techniques has increased privacy concerns for models trained on sensitive data such as medical records.
1 code implementation • ICLR 2018 • Zhengli Zhao, Dheeru Dua, Sameer Singh
Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed.