Search Results for author: Yixuan Liu

Found 12 papers, 4 papers with code

PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance

no code implementations6 Dec 2023 Haichao Sha, Ruixuan Liu, Yixuan Liu, Hong Chen

We prove that pre-projection enhances the convergence of DP-SGD by reducing the dependence of clipping error and bias to a fraction of the top gradient eigenspace, and in theory, limits cross-client variance to improve the convergence under heterogeneous federation.

Federated Learning

DARWIN Series: Domain Specific Large Language Models for Natural Science

2 code implementations25 Aug 2023 Tong Xie, Yuwei Wan, Wei Huang, Zhenyu Yin, Yixuan Liu, Shaozhou Wang, Qingyuan Linghu, Chunyu Kit, Clara Grazian, Wenjie Zhang, Imran Razzak, Bram Hoex

To add new capabilities in natural science, enabling the acceleration and enrichment of automation of the discovery process, we present DARWIN, a series of tailored LLMs for natural science, mainly in physics, chemistry, and material science.

Knowledge Graphs

Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

no code implementations11 Apr 2023 Yixuan Liu, Suyun Zhao, Li Xiong, YuHan Liu, Hong Chen

In this work, a general framework (APES) is built up to strengthen model privacy under personalized local privacy by leveraging the privacy amplification effect of the shuffle model.

Federated Learning

Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT

no code implementations5 Apr 2023 Tong Xie, Yuwei Wan, Wei Huang, Yufei Zhou, Yixuan Liu, Qingyuan Linghu, Shaozhou Wang, Chunyu Kit, Clara Grazian, Wenjie Zhang, Bram Hoex

The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches.

feature selection Language Modelling

ESCL: Equivariant Self-Contrastive Learning for Sentence Representations

no code implementations9 Mar 2023 Jie Liu, Yixuan Liu, Xue Han, Chao Deng, Junlan Feng

Previous contrastive learning methods for sentence representations often focus on insensitive transformations to produce positive pairs, but neglect the role of sensitive transformations that are harmful to semantic representations.

Contrastive Learning Multi-Task Learning +2

Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking

1 code implementation ACL 2022 Jinyu Guo, Kai Shuang, Jijie Li, Zihan Wang, Yixuan Liu

However, no matter how the dialogue history is used, each existing model uses its own consistent dialogue history during the entire state tracking process, regardless of which slot is updated.

Dialogue State Tracking

Extremal Analysis of Flooding Risk and Management

no code implementations1 Dec 2021 Chengxiu Ling, Jiayi Li, Yixuan Liu, Zhiyan Cai

Catastrophic losses caused by natural disasters receive a growing concern about the severe rise in magnitude and frequency.

Management

Solving reward-collecting problems with UAVs: a comparison of online optimization and Q-learning

1 code implementation30 Nov 2021 Yixuan Liu, Chrysafis Vogiatzis, Ruriko Yoshida, Erich Morman

Uncrewed autonomous vehicles (UAVs) have made significant contributions to reconnaissance and surveillance missions in past US military campaigns.

Autonomous Vehicles Q-Learning

Delayed Rewards Calibration via Reward Empirical Sufficiency

no code implementations21 Feb 2021 Yixuan Liu, Hu Wang, Xiaowei Wang, Xiaoyue Sun, Liuyue Jiang, Minhui Xue

Therefore, a purify-trained classifier is designed to obtain the distribution and generate the calibrated rewards.

CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions

no code implementations ECCV 2020 Zhongdao Wang, Jingwei Zhang, Liang Zheng, Yixuan Liu, Yifan Sun, Ya-Li Li, Shengjin Wang

This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as those from video tracklets or clustering.

Clustering Multi-Object Tracking +2

Towards Real-Time Multi-Object Tracking

12 code implementations ECCV 2020 Zhongdao Wang, Liang Zheng, Yixuan Liu, Ya-Li Li, Shengjin Wang

In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.

Multiple Object Tracking Multi-Task Learning +2

Adversarial View-Consistent Learning for Monocular Depth Estimation

no code implementations4 Aug 2019 Yixuan Liu, Yuwang Wang, Shengjin Wang

To this end, we first design a differentiable depth map warping operation, which is end-to-end trainable, and then propose a pose generator to generate novel views for a given image in an adversarial manner.

Monocular Depth Estimation

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