Search Results for author: Kai Zeng

Found 11 papers, 4 papers with code

Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation

1 code implementation13 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.

A Unified Transferable Model for ML-Enhanced DBMS

no code implementations6 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.

A Pluggable Learned Index Method via Sampling and Gap Insertion

no code implementations4 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.

BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation

1 code implementation29 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.

Probabilistic Programming

FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation

1 code implementation18 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.

FSPN: A New Class of Probabilistic Graphical Model

no code implementations18 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).

MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions

no code implementations12 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.

Recommendation Systems

Intelligent Policing Strategy for Traffic Violation Prevention

no code implementations20 Sep 2019 Monireh Dabaghchian, Amir Alipour-Fanid, Kai Zeng

Police officer presence at an intersection discourages a potential traffic violator from violating the law.

Multi-stage Deep Classifier Cascades for Open World Recognition

1 code implementation26 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.

Object Recognition

Who is Smarter? Intelligence Measure of Learning-based Cognitive Radios

no code implementations26 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.

Online Learning with Randomized Feedback Graphs for Optimal PUE Attacks in Cognitive Radio Networks

no code implementations28 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.

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