1 code implementation • 28 Mar 2016 • Jingbo Zhou, Qi Guo, H. V. Jagadish, Luboš Krčál, Siyuan Liu, Wenhao Luan, Anthony K. H. Tung, Yueji Yang, Yuxin Zheng
We propose a novel generic inverted index framework on the GPU (called GENIE), aiming to reduce the programming complexity of the GPU for parallel similarity search of different data types.
1 code implementation • 5 Jul 2021 • Shaofeng Cai, Kaiping Zheng, Gang Chen, H. V. Jagadish, Beng Chin Ooi, Meihui Zhang
The key idea is to model feature interactions with cross features selectively and dynamically, by first transforming the input features into exponential space, and then determining the interaction order and interaction weights adaptively for each cross feature.
1 code implementation • 5 Oct 2023 • Tianji Cong, Madelon Hulsebos, Zhenjie Sun, Paul Groth, H. V. Jagadish
Based on these properties, we define an extensible framework to evaluate language and table embedding models.
1 code implementation • NAACL 2022 • Farima Fatahi Bayat, Nikita Bhutani, H. V. Jagadish
Our experiments on CaRB and Wire57 datasets indicate that CompactIE finds 1. 5x-2x more compact extractions than previous systems, with high precision, establishing a new state-of-the-art performance in OpenIE.
Ranked #3 on Open Information Extraction on BenchIE
1 code implementation • 12 Jan 2023 • Tianji Cong, Fatemeh Nargesian, H. V. Jagadish
The large size and fast growth of data repositories, such as data lakes, has spurred the need for data discovery to help analysts find related data.
1 code implementation • 16 Mar 2020 • Christopher Baik, Zhongjun Jin, Michael Cafarella, H. V. Jagadish
We present results from user studies in which Duoquest demonstrates a 62. 5% absolute increase in query construction accuracy over a state-of-the-art NLI and comparable accuracy to a PBE system on a more limited workload supported by the PBE system.
Databases
1 code implementation • 13 Nov 2022 • Kaixin Wang, Cheng Long, Da Yan, Jie Zhang, H. V. Jagadish
Specifically, we propose a weighted sampling algorithm called WSD for estimating the subgraph count in a fully dynamic graph stream, which samples the edges based on their weights that indicate their importance and reflect their properties.
no code implementations • 26 Apr 2018 • Jinyang Gao, Wei Wang, Meihui Zhang, Gang Chen, H. V. Jagadish, Guoliang Li, Teck Khim Ng, Beng Chin Ooi, Sheng Wang, Jingren Zhou
In many complex applications such as healthcare, subject matter experts (e. g. Clinicians) are the ones who appreciate the importance of features that affect health, and their knowledge together with existing knowledge bases are critical to the end results.
no code implementations • 12 Dec 2015 • Jinyang Gao, H. V. Jagadish, Beng Chin Ooi
Recent years have witnessed amazing outcomes from "Big Models" trained by "Big Data".
no code implementations • COLING 2018 • Nikita Bhutani, Kun Qian, Yunyao Li, H. V. Jagadish, Hern, Mauricio ez, Mitesh Vasa
We show that programs for mapping entity mentions to their structures can be automatically generated using human-comprehensible labels.
no code implementations • NAACL 2019 • Nikita Bhutani, Yoshihiko Suhara, Wang-Chiew Tan, Alon Halevy, H. V. Jagadish
We describe NeurON, a system for extracting tuples from question-answer pairs.
no code implementations • 21 Jun 2019 • Wei Wang, Meihui Zhang, Gang Chen, H. V. Jagadish, Beng Chin Ooi, Kian-Lee Tan
Deep learning has recently become very popular on account of its incredible success in many complex data-driven applications, such as image classification and speech recognition.
no code implementations • 22 Nov 2019 • Abolfazl Asudeh, H. V. Jagadish
We provide unbiased samplers for the entire function space, as well as a $\theta$-vicinity around a given function.
no code implementations • 22 Mar 2022 • Nima Shahbazi, Yin Lin, Abolfazl Asudeh, H. V. Jagadish
Data-driven algorithms are only as good as the data they work with, while data sets, especially social data, often fail to represent minorities adequately.
no code implementations • 30 Dec 2022 • Jinyang Li, Yuval Moskovitch, H. V. Jagadish
We propose efficient search algorithms for two different fairness measures: global representation bounds, and proportional representation.
1 code implementation • 2 Feb 2024 • Mahdi Erfanian, H. V. Jagadish, Abolfazl Asudeh
The potential harms of the under-representation of minorities in training data, particularly in multi-modal settings, is a well-recognized concern.