Search Results for author: Haoming Zhang

Found 8 papers, 4 papers with code

FR-NAS: Forward-and-Reverse Graph Predictor for Efficient Neural Architecture Search

1 code implementation24 Apr 2024 Haoming Zhang, Ran Cheng

Additionally, we incorporate a customized training loss within the GNN predictor to ensure efficient utilization of both types of representations.

Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding

no code implementations9 Jan 2024 Yatong Bai, Utsav Garg, Apaar Shanker, Haoming Zhang, Samyak Parajuli, Erhan Bas, Isidora Filipovic, Amelia N. Chu, Eugenia D Fomitcheva, Elliot Branson, Aerin Kim, Somayeh Sojoudi, Kyunghyun Cho

Vision and vision-language applications of neural networks, such as image classification and captioning, rely on large-scale annotated datasets that require non-trivial data-collecting processes.

Image Captioning Image Classification +3

Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM Network

3 code implementations1 Sep 2023 Haoming Zhang, Zhanxin Wang, Heike Vallery

This work proposes a deep-learning-based method to detect NLOS receptions and predict GNSS pseudorange errors by analyzing GNSS observations as a spatio-temporal modeling problem.

Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation

no code implementations14 Aug 2022 Zhichao Lu, Ran Cheng, Shihua Huang, Haoming Zhang, Changxiao Qiu, Fan Yang

The main challenges of applying NAS to semantic segmentation arise from two aspects: (i) high-resolution images to be processed; (ii) additional requirement of real-time inference speed (i. e., real-time semantic segmentation) for applications such as autonomous driving.

Autonomous Driving Image Classification +3

Discriminative Supervised Subspace Learning for Cross-modal Retrieval

no code implementations26 Jan 2022 Haoming Zhang, Xiao-Jun Wu, Tianyang Xu, Donglin Zhang

Thirdly, we introduce a similarity preservation term, thus our model can compensate for the shortcomings of insufficient use of discriminative data and better preserve the semantically structural information within each modality.

Cross-Modal Retrieval Retrieval +2

Predicting Basin Stability of Power Grids using Graph Neural Networks

1 code implementation18 Aug 2021 Christian Nauck, Michael Lindner, Konstantin Schürholt, Haoming Zhang, Paul Schultz, Jürgen Kurths, Ingrid Isenhardt, Frank Hellmann

We investigate the feasibility of applying graph neural networks (GNN) to predict dynamic stability of synchronisation in complex power grids using the single-node basin stability (SNBS) as a measure.

EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising

2 code implementations24 Sep 2020 Haoming Zhang, Mingqi Zhao, Chen Wei, Dante Mantini, Zherui Li, Quanying Liu

Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing deep learning-based denoising models, as well as for performance comparisons across models.

Denoising EEG +1

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