Search Results for author: Yu Huang

Found 25 papers, 4 papers with code

Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)

no code implementations23 Mar 2022 Yu Huang, Junyang Lin, Chang Zhou, Hongxia Yang, Longbo Huang

Recently, it has been observed that the best uni-modal network outperforms the jointly trained multi-modal network, which is counter-intuitive since multiple signals generally bring more information.

A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification

no code implementations23 Mar 2022 Yu Xiang, Yu Huang, Haodong Xu, Guangbo Zhang, Wenyong Wang

The identification of pedestrians using radar micro-Doppler signatures has become a hot topic in recent years.

Testability-Aware Low Power Controller Design with Evolutionary Learning

1 code implementation26 Nov 2021 Min Li, Zhengyuan Shi, Zezhong Wang, Weiwei Zhang, Yu Huang, Qiang Xu

The proposed GA-guided XORNets also allows reducing the number of control bits, and the total testing time decreases by 20. 78% on average and up to 47. 09% compared to the existing design without sacrificing test coverage.

DeepGate: Learning Neural Representations of Logic Gates

no code implementations26 Nov 2021 Min Li, Sadaf Khan, Zhengyuan Shi, Naixing Wang, Yu Huang, Qiang Xu

We propose DeepGate, a novel representation learning solution that effectively embeds both logic function and structural information of a circuit as vectors on each gate.

Representation Learning

GraSSNet: Graph Soft Sensing Neural Networks

no code implementations12 Nov 2021 Yu Huang, Chao Zhang, Jaswanth Yella, Sergei Petrov, Xiaoye Qian, Yufei Tang, Xingquan Zhu, Sthitie Bom

In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection.

Time Series Time Series Classification

Soft-Sensing ConFormer: A Curriculum Learning-based Convolutional Transformer

no code implementations12 Nov 2021 Jaswanth Yella, Chao Zhang, Sergei Petrov, Yu Huang, Xiaoye Qian, Ali A. Minai, Sthitie Bom

Over the last few decades, modern industrial processes have investigated several cost-effective methodologies to improve the productivity and yield of semiconductor manufacturing.

Soft Sensing Transformer: Hundreds of Sensors are Worth a Single Word

1 code implementation10 Nov 2021 Chao Zhang, Jaswanth Yella, Yu Huang, Xiaoye Qian, Sergei Petrov, Andrey Rzhetsky, Sthitie Bom

We demonstrate the challenges and effectiveness of modeling industrial big data by a Soft Sensing Transformer model on these data sets.

Time Series

IEEE BigData 2021 Cup: Soft Sensing at Scale

no code implementations7 Sep 2021 Sergei Petrov, Chao Zhang, Jaswanth Yella, Yu Huang, Xiaoye Qian, Sthitie Bom

The scope of this challenge is to tackle the task of classifying soft sensing data with machine learning techniques.

ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting

no code implementations12 Aug 2021 Yu Huang, James Li, Min Shi, Hanqi Zhuang, Xingquan Zhu, Laurent Chérubin, James VanZwieten, Yufei Tang

A spatio-temporal physics-coupled neural network (ST-PCNN) model is proposed to achieve three goals: (1) learning the underlying physics parameters, (2) transition of local information between spatio-temporal regions, and (3) forecasting future values for the dynamical system.

Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems

no code implementations11 Aug 2021 Yu Huang, Yufei Tang, Xingquan Zhu, Min Shi, Ali Muhamed Ali, Hanqi Zhuang, Laurent Cherubin

To tackle these challenges, we advocate a spatio-temporal physics-coupled neural networks (ST-PCNN) model to learn the underlying physics of the dynamical system and further couple the learned physics to assist the learning of the recurring dynamics.

Active Learning Spatio-Temporal Forecasting

Snippet Policy Network for Multi-class Varied-length ECG Early Classification

no code implementations28 Jul 2021 Yu Huang, Gary G. Yen, Vincent S. Tseng

To the best of our knowledge, this is the first work focusing on solving the cardiovascular early classification problem based on varied-length ECG data.

Arrhythmia Detection Classification +2

Regions of Attraction Estimation using Level SetMethod for Complex Network System

no code implementations25 Jan 2021 Mengbang Zou, Yu Huang, Weisi Guo

This approach is insufficient for dynamic networks with changing equilibrium and estimating the Region of Attraction(ROA) is needed.

Social and Information Networks

Physical rendering of synthetic spaces for topological sound transport

no code implementations22 Dec 2020 Hui Chen, Hongkuan Zhang, Qian Wu, Yu Huang, Huy Nguyen, Emil Prodan, Xiaoming Zhou, Guoliang Huang

Synthetic dimensions can be rendered in the physical space and this has been achieved with photonics and cold atomic gases, however, little to no work has been succeeded in acoustics because acoustic wave-guides cannot be weakly coupled in a continuous fashion.

Mesoscale and Nanoscale Physics Classical Physics

BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis

no code implementations2 Dec 2020 Weijie He, Xiaohao Mao, Chao Ma, Yu Huang, José Miguel Hernández-Lobato, Ting Chen

To address the challenge, we propose a non-RL Bipartite Scalable framework for Online Disease diAgnosis, called BSODA.

Disease Prediction

Evolutionary Architecture Search for Graph Neural Networks

1 code implementation21 Sep 2020 Min Shi, David A. Wilson, Xingquan Zhu, Yu Huang, Yuan Zhuang, Jianxun Liu, Yufei Tang

In particular, Neural Architecture Search (NAS) has seen significant attention throughout the AutoML research community, and has pushed forward the state-of-the-art in a number of neural models to address grid-like data such as texts and images.

Neural Architecture Search Representation Learning

Physics-informed Tensor-train ConvLSTM for Volumetric Velocity Forecasting of Loop Current

no code implementations4 Aug 2020 Yu Huang, Yufei Tang, Hanqi Zhuang, James VanZwieten, Laurent Cherubin

According to the National Academies, a weekly forecast of velocity, vertical structure, and duration of the Loop Current (LC) and its eddies is critical for understanding the oceanography and ecosystem, and for mitigating outcomes of anthropogenic and natural disasters in the Gulf of Mexico (GoM).

Frame Video Prediction

Genetic Improvement @ ICSE 2020

no code implementations31 Jul 2020 William B. Langdon, Westley Weimer, Justyna Petke, Erik Fredericks, Seongmin Lee, Emily Winter, Michail Basios, Myra B. Cohen, Aymeric Blot, Markus Wagner, Bobby R. Bruce, Shin Yoo, Simos Gerasimou, Oliver Krauss, Yu Huang, Michael Gerten

Following Prof. Mark Harman of Facebook's keynote and formal presentations (which are recorded in the proceedings) there was a wide ranging discussion at the eighth international Genetic Improvement workshop, GI-2020 @ ICSE (held as part of the 42nd ACM/IEEE International Conference on Software Engineering on Friday 3rd July 2020).

Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies

no code implementations10 Jun 2020 Yu Huang, Yue Chen

Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications.

3D Object Detection Autonomous Driving +1

Attributed Rhetorical Structure Grammar for Domain Text Summarization

no code implementations3 Sep 2019 Ruqian Lu, Shengluan Hou, Chuanqing Wang, Yu Huang, Chaoqun Fei, Songmao Zhang

We have also shown that the knowledge based approach may be made more powerful by introducing grammar parsing and RST as inference engine.

Text Summarization

The State and Future of Genetic Improvement

no code implementations27 Jun 2019 William B. Langdon, Westley Weimer, Christopher Timperley, Oliver Krauss, Zhen Yu Ding, Yiwei Lyu, Nicolas Chausseau, Eric Schulte, Shin Hwei Tan, Kevin Leach, Yu Huang, Gabin An

We report the discussion session at the sixth international Genetic Improvement workshop, GI-2019 @ ICSE, which was held as part of the 41st ACM/IEEE International Conference on Software Engineering on Tuesday 28th May 2019.

Segmentation of MRI head anatomy using deep volumetric networks and multiple spatial priors

1 code implementation24 May 2019 Lukas Hirsch, Yu Huang, Lucas C. Parra

The benefit of adding a TPM is generic in that it can boost the performance of established segmentation networks such as the DeepMedic and a UNet.

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