Search Results for author: H. V. Jagadish

Found 17 papers, 8 papers with code

A Generic Inverted Index Framework for Similarity Search on the GPU - Technical Report

1 code implementation28 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.

ARM-Net: Adaptive Relation Modeling Network for Structured Data

1 code implementation5 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.

Attribute Decision Making +1

Observatory: Characterizing Embeddings of Relational Tables

1 code implementation5 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.

CompactIE: Compact Facts in Open Information Extraction

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.

Open Information Extraction

Pylon: Semantic Table Union Search in Data Lakes

1 code implementation12 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.

Contrastive Learning Representation Learning +1

Duoquest: A Dual-Specification System for Expressive SQL Queries

1 code implementation16 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

Reinforcement Learning Enhanced Weighted Sampling for Accurate Subgraph Counting on Fully Dynamic Graph Streams

1 code implementation13 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.

Subgraph Counting

PANDA: Facilitating Usable AI Development

no code implementations26 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.

Autonomous Driving

Database Meets Deep Learning: Challenges and Opportunities

no code implementations21 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.

Image Classification speech-recognition +1

Responsible Scoring Mechanisms Through Function Sampling

no code implementations22 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.

Representation Bias in Data: A Survey on Identification and Resolution Techniques

no code implementations22 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.

Decision Making Fairness

Detection of Groups with Biased Representation in Ranking

no code implementations30 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.

Decision Making Fairness

Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities

1 code implementation2 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.

Data Augmentation Fairness

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