Search Results for author: Haoyu Ren

Found 13 papers, 3 papers with code

TinyMetaFed: Efficient Federated Meta-Learning for TinyML

no code implementations13 Jul 2023 Haoyu Ren, Xue Li, Darko Anicic, Thomas A. Runkler

The field of Tiny Machine Learning (TinyML) has made substantial advancements in democratizing machine learning on low-footprint devices, such as microcontrollers.

Computational Efficiency Few-Shot Learning

TinyReptile: TinyML with Federated Meta-Learning

no code implementations11 Apr 2023 Haoyu Ren, Darko Anicic, Thomas A. Runkler

Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs).

Federated Learning Meta-Learning

SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT

1 code implementation18 Jul 2022 Haoyu Ren, Kirill Dorofeev, Darko Anicic, Youssef Hammad, Roland Eckl, Thomas A. Runkler

Therefore, this paper presents a framework called Semantic Low-Code Engineering for ML Applications (SeLoC-ML), built on a low-code platform to support the rapid development of ML applications in IIoT by leveraging Semantic Web technologies.

BIG-bench Machine Learning

How to Manage Tiny Machine Learning at Scale: An Industrial Perspective

1 code implementation18 Feb 2022 Haoyu Ren, Darko Anicic, Thomas Runkler

Tiny machine learning (TinyML) has gained widespread popularity where machine learning (ML) is democratized on ubiquitous microcontrollers, processing sensor data everywhere in real-time.

Benchmarking BIG-bench Machine Learning +1

The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT

no code implementations4 May 2021 Haoyu Ren, Darko Anicic, Thomas Runkler

Focusing on comprehensive networking, big data, and artificial intelligence, the Industrial Internet-of-Things (IIoT) facilitates efficiency and robustness in factory operations.

BIG-bench Machine Learning

TinyOL: TinyML with Online-Learning on Microcontrollers

no code implementations15 Mar 2021 Haoyu Ren, Darko Anicic, Thomas Runkler

The neural network is first trained using a large amount of pre-collected data on a powerful machine and then flashed to MCUs.

TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching

no code implementations11 Jun 2019 Mostafa El-Khamy, Haoyu Ren, Xianzhi Du, Jungwon Lee

In this paper, we introduce the problem of estimating the real world depth of elements in a scene captured by two cameras with different field of views, where the first field of view (FOV) is a Wide FOV (WFOV) captured by a wide angle lens, and the second FOV is contained in the first FOV and is captured by a tele zoom lens.

Depth Estimation Disparity Estimation +2

Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding

no code implementations7 Jun 2019 Haoyu Ren, Mostafa El-Khamy, Jungwon Lee

We introduce two different scene understanding modules based on scene classification and coarse depth estimation respectively.

Depth Estimation General Classification +2

DN-ResNet: Efficient Deep Residual Network for Image Denoising

no code implementations16 Oct 2018 Haoyu Ren, Mostafa El-Khamy, Jungwon Lee

The results show that DN-ResNets are more efficient, robust, and perform better denoising than current state of art deep learning methods, as well as the popular variants of the BM3D algorithm, in cases of blind and non-blind denoising of images corrupted with Poisson, Gaussian or Poisson-Gaussian noise.

Computational Efficiency Image Denoising +2

CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution

no code implementations11 Nov 2017 Haoyu Ren, Mostafa El-Khamy, Jungwon Lee

We propose methodologies to train highly accurate and efficient deep convolutional neural networks (CNNs) for image super resolution (SR).

Image Super-Resolution

Basis Mapping Based Boosting for Object Detection

no code implementations CVPR 2015 Haoyu Ren, Ze-Nian Li

We show that the basis mapping based weak classifier is an approximation of kernel weak classifiers while keeping the same computation cost as linear weak classifiers.

Object object-detection +2

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