no code implementations • 6 May 2024 • Jiang Zhang, Yahya H Ezzeldin, Ahmed Roushdy Elkordy, Konstantinos Psounis, Salman Avestimehr
However, we further demonstrate that in practice, these conditions are almost unlikely to hold and hence additional noise added in model updates is still required in order for SA in FL to achieve DP.
no code implementations • 13 Dec 2023 • Jiang Zhang, Qiong Wu, Yiming Xu, Cheng Cao, Zheng Du, Konstantinos Psounis
Furthermore, student LMs fine-tuned with rationales extracted via DToT outperform baselines on all datasets with up to 16. 9\% accuracy improvement, while being more than 60x smaller than conventional LLMs.
1 code implementation • 21 Nov 2023 • Zhang Zhang, Ruyi Tao, Jiang Zhang
The rapid increase in the parameters of deep learning models has led to significant costs, challenging computational efficiency and model interpretability.
1 code implementation • 12 Oct 2023 • Ruyi Tao, Ningning Tao, Yi-Zhuang You, Jiang Zhang
For deterministic dynamics, our framework can discern whether the dynamics are self-similar.
no code implementations • 19 Aug 2023 • Mingzhe Yang, Zhipeng Wang, Kaiwei Liu, Yingqi Rong, Bing Yuan, Jiang Zhang
Quantifying emergence and modeling emergent dynamics in a data-driven manner for complex dynamical systems is challenging due to the lack of direct observations at the micro-level.
no code implementations • 20 Feb 2023 • Jing Xu, Shuo Wang, Na Ying, Xiao Xiao, Jiang Zhang, Yun Cheng, Zhiling Jin, Gangfeng Zhang
Previous GCNs-based methods usually require providing spatial correlation graph structure of observation sites in advance.
no code implementations • 27 Oct 2022 • Jiabao Sheng, Yuanpeng Zhang, Jing Cai, Sai-Kit Lam, Zhe Li, Jiang Zhang, Xinzhi Teng
To improve the discriminative ability of the loss function, we incorporate a margin into the contrastive learning.
no code implementations • 3 Aug 2022 • Ahmed Roushdy Elkordy, Jiang Zhang, Yahya H. Ezzeldin, Konstantinos Psounis, Salman Avestimehr
While SA ensures no additional information is leaked about the individual model update beyond the aggregated model update, there are no formal guarantees on how much privacy FL with SA can actually offer; as information about the individual dataset can still potentially leak through the aggregated model computed at the server.
1 code implementation • 25 Apr 2022 • Zhang Zhang, Ruyi Tao, Yongzai Tao, Mingze Qi, Jiang Zhang
And experiments show that our model perform better on a network with higher Reachable CC.
1 code implementation • 25 Jan 2022 • Jiang Zhang, Kaiwei Liu
We also show how our framework can extract the dynamics on different levels and identify causal emergence from the data on several exampled systems.
no code implementations • 13 Jan 2022 • Jiang Zhang, Lillian Clark, Matthew Clark, Konstantinos Psounis, Peter Kairouz
Cellular providers and data aggregating companies crowdsource celluar signal strength measurements from user devices to generate signal maps, which can be used to improve network performance.
no code implementations • 7 Dec 2021 • Evita Bakopoulou, Mengwei Yang, Jiang Zhang, Konstantinos Psounis, Athina Markopoulou
We consider the problem of predicting cellular network performance (signal maps) from measurements collected by several mobile devices.
no code implementations • 9 Nov 2021 • Jiang Zhang, Konstantinos Psounis, Muhammad Haroon, Zubair Shafiq
Online behavioral advertising, and the associated tracking paraphernalia, poses a real privacy threat.
1 code implementation • 24 Jun 2021 • Jing Liu, Sujie Li, Jiang Zhang, Pan Zhang
Despite the great potential, however, existing tensor network models for unsupervised machine learning only work as a proof of principle, as their performance is much worse than the standard models such as restricted Boltzmann machines and neural networks.
no code implementations • 6 Jun 2021 • Lun Du, Fei Gao, Xu Chen, Ran Jia, Junshan Wang, Jiang Zhang, Shi Han, Dongmei Zhang
To simultaneously extract spatial and relational information from tables, we propose a novel neural network architecture, TabularNet.
no code implementations • 22 Nov 2020 • Jiang Zhang, Ivan Beschastnikh, Sergey Mechtaev, Abhik Roychoudhury
Data-driven decision making is gaining prominence with the popularity of various machine learning models.
no code implementations • 14 Apr 2020 • Yaoxin Li, Jing Liu, Guozheng Lin, Yueyuan Hou, Muyun Mou, Jiang Zhang
In computer science, there exist a large number of optimization problems defined on graphs, that is to find a best node state configuration or a network structure such that the designed objective function is optimized under some constraints.
2 code implementations • ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2020 • Shuo Wang, Yan-ran Li, Jiang Zhang, Qingye Meng, Lingwei Meng, Fei Gao
When predicting PM2. 5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period.
no code implementations • 18 Jan 2020 • Mengyuan Chen, Jiang Zhang, Zhang Zhang, Lun Du, Qiao Hu, Shuo Wang, Jiaqi Zhu
We carried out experiments on discrete and continuous time series data.
1 code implementation • 2 Jan 2020 • Lei Dong, Zhou Huang, Jiang Zhang, Yu Liu
Understanding quantitative relationships between urban elements is crucial for a wide range of applications.
Physics and Society
no code implementations • 10 Oct 2019 • Weiwei Gu, Fei Gao, Xiaodan Lou, Jiang Zhang
Link prediction aims to infer missing links or predicting the future ones based on currently observed partial networks, it is a fundamental problem in network science with tremendous real-world applications.
no code implementations • 16 Sep 2019 • Jing Liu, Fei Gao, Jiang Zhang
Many problems in real life can be converted to combinatorial optimization problems (COPs) on graphs, that is to find a best node state configuration or a network structure such that the designed objective function is optimized under some constraints.
1 code implementation • 30 Dec 2018 • Zhang Zhang, Yi Zhao, Jing Liu, Shuo Wang, Ruyi Tao, Ruyue Xin, Jiang Zhang
We exhibit the universality of our framework on different kinds of time-series data: with the same structure, our model can be trained to accurately recover the network structure and predict future states on continuous, discrete, and binary dynamics, and outperforms competing network reconstruction methods.
1 code implementation • 23 Nov 2018 • Jiang Zhang, Yuanqing Xia, Ganghui Shen
Autonomous path planning algorithms are significant to planetary exploration rovers, since relying on commands from Earth will heavily reduce their efficiency of executing exploration missions.
no code implementations • 25 Aug 2018 • Jiang Zhang, Yuanqing Xia, Ganghui Shen
In this paper, emerging deep learning techniques are leveraged to deal with Mars visual navigation problem.
no code implementations • 2 Feb 2018 • Ruyue Xin, Jiang Zhang, Yitong Shao
Classifying large scale networks into several categories and distinguishing them according to their fine structures is of great importance with several applications in real life.