Search Results for author: Gang Fu

Found 11 papers, 5 papers with code

Learning from Aggregated Data: Curated Bags versus Random Bags

no code implementations16 May 2023 Lin Chen, Gang Fu, Amin Karbasi, Vahab Mirrokni

Our method is based on the observation that the sum of the gradients of the loss function on individual data examples in a curated bag can be computed from the aggregate label without the need for individual labels.

Approximately Optimal Core Shapes for Tensor Decompositions

no code implementations8 Feb 2023 Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni

This work studies the combinatorial optimization problem of finding an optimal core tensor shape, also called multilinear rank, for a size-constrained Tucker decomposition.

Combinatorial Optimization

Sequential Attention for Feature Selection

1 code implementation29 Sep 2022 Taisuke Yasuda, Mohammadhossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni

Feature selection is the problem of selecting a subset of features for a machine learning model that maximizes model quality subject to a budget constraint.

Feature Importance feature selection

Deep Image-based Illumination Harmonization

no code implementations CVPR 2022 Zhongyun Bao, Chengjiang Long, Gang Fu, Daquan Liu, Yuanzhen Li, Jiaming Wu, Chunxia Xiao

Specifically, we firstly apply a physically-based rendering method to construct a large-scale, high-quality dataset (named IH) for our task, which contains various types of foreground objects and background scenes with different lighting conditions.

Feature Cross Search via Submodular Optimization

no code implementations5 Jul 2021 Lin Chen, Hossein Esfandiari, Gang Fu, Vahab S. Mirrokni, Qian Yu

First, we show that it is not possible to provide an $n^{1/\log\log n}$-approximation algorithm for this problem unless the exponential time hypothesis fails.

Feature Engineering

CRPN-SFNet: A High-Performance Object Detector on Large-Scale Remote Sensing Images

no code implementations28 Oct 2020 QiFeng Lin, Jianhui Zhao, Gang Fu, and Zhiyong Yuan, Member, IEEE

Extensive experiments on the public Dataset for Object deTection in Aerial images data set indicate that our CRPN can help our detector deal the larger image faster with the limited GPU memory; meanwhile, the SFNet is beneficial to achieve more accurate detection of geospatial objects with wide-scale range.

object-detection Object Detection In Aerial Images +1

Learning to Detect Specular Highlights from Real-world Images

1 code implementation10 Oct 2020 Gang Fu, Qing Zhang, QiFeng Lin, Lei Zhu, and Chunaxia Xiao

Specular highlight detection is a challenging problem, and has many applications such as shiny object detection and light source estimation.

Highlight Detection object-detection +1

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

1 code implementation7 Sep 2018 Zheng Gao, Gang Fu, Chunping Ouyang, Satoshi Tsutsui, Xiaozhong Liu, Jeremy Yang, Christopher Gessner, Brian Foote, David Wild, Qi Yu, Ying Ding

We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.

Biomedical Information Retrieval Information Retrieval +3

On Reasoning with RDF Statements about Statements using Singleton Property Triples

no code implementations15 Sep 2015 Vinh Nguyen, Olivier Bodenreider, Krishnaprasad Thirunarayan, Gang Fu, Evan Bolton, Núria Queralt Rosinach, Laura I. Furlong, Michel Dumontier, Amit Sheth

If the singleton property triples describe a data triple, then how can a reasoner infer this data triple from the singleton property triples?

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