1 code implementation • 18 May 2023 • Zeyuan Tan, Xiulong Yuan, Congjie He, Man-Kit Sit, Guo Li, Xiaoze Liu, Baole Ai, Kai Zeng, Peter Pietzuch, Luo Mai
Quiver's key idea is to exploit workload metrics for predicting the irregular computation of GNN requests, and governing the use of GPUs for graph sampling and feature aggregation: (1) for graph sampling, Quiver calculates the probabilistic sampled graph size, a metric that predicts the degree of parallelism in graph sampling.
no code implementations • 29 Mar 2023 • Osoro B Ogutu, Edward J Oughton, Kai Zeng, Brian L. Mark
Consequently, this paper presents two open-source simulation models for assessing the socio-economic impacts of operating in untrusted non-cooperative networks.
no code implementations • 29 Oct 2022 • Yue Wang, Zhi Tian, Xin Fan, Yan Huo, Cameron Nowzari, Kai Zeng
With the proliferation of versatile Internet of Things (IoT) services, smart IoT devices are increasingly deployed at the edge of wireless networks to perform collaborative machine learning tasks using locally collected data, giving rise to the edge learning paradigm.
no code implementations • 29 Sep 2021 • Daoyuan Chen, Wuchao Li, Yaliang Li, Bolin Ding, Kai Zeng, Defu Lian, Jingren Zhou
We theoretically analyze prediction error bounds that link $\epsilon$ with data characteristics for an illustrative learned index method.
1 code implementation • 13 Sep 2021 • Yuxing Han, Ziniu Wu, Peizhi Wu, Rong Zhu, Jingyi Yang, Liang Wei Tan, Kai Zeng, Gao Cong, Yanzhao Qin, Andreas Pfadler, Zhengping Qian, Jingren Zhou, Jiangneng Li, Bin Cui
Therefore, we propose a new metric P-Error to evaluate the performance of CardEst methods, which overcomes the limitation of Q-Error and is able to reflect the overall end-to-end performance of CardEst methods.
1 code implementation • 6 May 2021 • Ziniu Wu, Pei Yu, Peilun Yang, Rong Zhu, Yuxing Han, Yaliang Li, Defu Lian, Kai Zeng, Jingren Zhou
We propose to explore the transferabilities of the ML methods both across tasks and across DBs to tackle these fundamental drawbacks.
no code implementations • 4 Jan 2021 • Yaliang Li, Daoyuan Chen, Bolin Ding, Kai Zeng, Jingren Zhou
In this paper, we propose a formal machine learning based framework to quantify the index learning objective, and study two general and pluggable techniques to enhance the learning efficiency and learning effectiveness for learned indexes.
1 code implementation • 29 Dec 2020 • Ziniu Wu, Amir Shaikhha, Rong Zhu, Kai Zeng, Yuxing Han, Jingren Zhou
Recently proposed deep learning based methods largely improve the estimation accuracy but their performance can be greatly affected by data and often difficult for system deployment.
no code implementations • 18 Nov 2020 • Ziniu Wu, Rong Zhu, Andreas Pfadler, Yuxing Han, Jiangneng Li, Zhengping Qian, Kai Zeng, Jingren Zhou
We introduce factorize sum split product networks (FSPNs), a new class of probabilistic graphical models (PGMs).
1 code implementation • 18 Nov 2020 • Rong Zhu, Ziniu Wu, Yuxing Han, Kai Zeng, Andreas Pfadler, Zhengping Qian, Jingren Zhou, Bin Cui
Despite decades of research, existing methods either over simplify the models only using independent factorization which leads to inaccurate estimates, or over complicate them by lossless conditional factorization without any independent assumption which results in slow probability computation.
no code implementations • 12 Oct 2020 • Wenqi Jiang, Zhenhao He, Shuai Zhang, Thomas B. Preußer, Kai Zeng, Liang Feng, Jiansong Zhang, Tongxuan Liu, Yong Li, Jingren Zhou, Ce Zhang, Gustavo Alonso
MicroRec accelerates recommendation inference by (1) redesigning the data structures involved in the embeddings to reduce the number of lookups needed and (2) taking advantage of the availability of High-Bandwidth Memory (HBM) in FPGA accelerators to tackle the latency by enabling parallel lookups.
no code implementations • 20 Sep 2019 • Monireh Dabaghchian, Amir Alipour-Fanid, Kai Zeng
Police officer presence at an intersection discourages a potential traffic violator from violating the law.
1 code implementation • 26 Aug 2019 • Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng, Liang Zhao
At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase.
no code implementations • 26 Dec 2017 • Monireh Dabaghchian, Amir Alipour-Fanid, Songsong Liu, Kai Zeng, Xiaohua LI, Yu Chen
Then we apply factor analysis on the performance data to identify and quantize the intelligence factors and cognitive capabilities of the CR.
no code implementations • 28 Sep 2017 • Monireh Dabaghchian, Amir Alipour-Fanid, Kai Zeng, Qingsi Wang, Peter Auer
In this paper, for the first time, we study optimal PUE attack strategies by formulating an online learning problem where the attacker needs to dynamically decide the attacking channel in each time slot based on its attacking experience.