Search Results for author: Hao Yan

Found 35 papers, 5 papers with code

VimTS: A Unified Video and Image Text Spotter for Enhancing the Cross-domain Generalization

1 code implementation30 Apr 2024 Yuliang Liu, Mingxin Huang, Hao Yan, Linger Deng, Weijia Wu, Hao Lu, Chunhua Shen, Lianwen Jin, Xiang Bai

Typically, we propose a Prompt Queries Generation Module and a Tasks-aware Adapter to effectively convert the original single-task model into a multi-task model suitable for both image and video scenarios with minimal additional parameters.

Domain Generalization Text Spotting

Lightweight Unsupervised Federated Learning with Pretrained Vision Language Model

no code implementations17 Apr 2024 Hao Yan, Yuhong Guo

To address these two inherent challenges in supervised federated learning, we propose a novel lightweight unsupervised federated learning approach that leverages unlabeled data on each client to perform lightweight model training and communication by harnessing pretrained vision-language models, such as CLIP.

Federated Learning Language Modelling

Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling

no code implementations5 Apr 2024 Jiuyun Hu, Ziyue Li, Chen Zhang, Fugee Tsung, Hao Yan

Moreover, a case study in the station clustering based on real passenger flow data is conducted, with quite valuable insights discovered.

Clustering Dimensionality Reduction

Image-based Novel Fault Detection with Deep Learning Classifiers using Hierarchical Labels

no code implementations26 Mar 2024 Nurettin Sergin, Jiayu Huang, Tzyy-Shuh Chang, Hao Yan

One important characteristic of modern fault classification systems is the ability to flag the system when faced with previously unseen fault types.

Fault Detection

DS-NeRV: Implicit Neural Video Representation with Decomposed Static and Dynamic Codes

no code implementations23 Mar 2024 Hao Yan, Zhihui Ke, Xiaobo Zhou, Tie Qiu, Xidong Shi, Dadong Jiang

Implicit neural representations for video (NeRV) have recently become a novel way for high-quality video representation.

Optical Flow Estimation

Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework

no code implementations17 Mar 2024 Kaiyan Chang, Kun Wang, Nan Yang, Ying Wang, Dantong Jin, Wenlong Zhu, Zhirong Chen, Cangyuan Li, Hao Yan, Yunhao Zhou, Zhuoliang Zhao, Yuan Cheng, Yudong Pan, Yiqi Liu, Mengdi Wang, Shengwen Liang, Yinhe Han, Huawei Li, Xiaowei Li

Our 13B model (ChipGPT-FT) has a pass rate improvement compared with GPT-3. 5 in Verilog generation and outperforms in EDA script (i. e., SiliconCompiler) generation with only 200 EDA script data.

Data Augmentation

Learning-driven Physically-aware Large-scale Circuit Gate Sizing

no code implementations13 Mar 2024 Yuyang Ye, Peng Xu, Lizheng Ren, Tinghuan Chen, Hao Yan, Bei Yu, Longxing Shi

Gate sizing plays an important role in timing optimization after physical design.

A Crosstalk-Aware Timing Prediction Method in Routing

no code implementations7 Mar 2024 Leilei Jin, Jiajie Xu, Wenjie Fu, Hao Yan, Longxing Shi

With shrinking interconnect spacing in advanced technology nodes, existing timing predictions become less precise due to the challenging quantification of crosstalk-induced delay.

Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering

no code implementations31 Oct 2023 Ziyue Li, Hao Yan, Chen Zhang, Lijun Sun, Wolfgang Ketter, Fugee Tsung

In this paper, we propose a novel tensor Dirichlet Process Multinomial Mixture model with graphs, which can preserve the hierarchical structure of the multi-dimensional trip information and cluster them in a unified one-step manner with the ability to determine the number of clusters automatically.

Clustering Community Detection +1

Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data

no code implementations7 Sep 2023 Jiuyun Hu, Naichen Shi, Raed Al Kontar, Hao Yan

We propose personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets.

Anomaly Detection Classification +2

Tensor Dirichlet Process Multinomial Mixture Model for Passenger Trajectory Clustering

no code implementations23 Jun 2023 Ziyue Li, Hao Yan, Chen Zhang, Andi Wang, Wolfgang Ketter, Lijun Sun, Fugee Tsung

In this paper, we propose a novel Tensor Dirichlet Process Multinomial Mixture model (Tensor-DPMM), which is designed to preserve the multi-mode and hierarchical structure of the multi-dimensional trip information via tensor, and cluster them in a unified one-step manner.

Clustering Trajectory Clustering

Dual Moving Average Pseudo-Labeling for Source-Free Inductive Domain Adaptation

no code implementations15 Dec 2022 Hao Yan, Yuhong Guo

We first split the unlabeled training set in the target domain into a pseudo-labeled confident subset and an unlabeled less-confident subset according to the prediction confidence scores from the pre-trained source model.

Source-Free Domain Adaptation Unsupervised Domain Adaptation

Learning on Large-scale Text-attributed Graphs via Variational Inference

2 code implementations26 Oct 2022 Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, Jian Tang

In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language learning with a variational Expectation-Maximization (EM) framework, called GLEM.

Variational Inference

Posterior Regularized Bayesian Neural Network Incorporating Soft and Hard Knowledge Constraints

no code implementations16 Oct 2022 Jiayu Huang, Yutian Pang, Yongming Liu, Hao Yan

Neural Networks (NNs) have been widely {used in supervised learning} due to their ability to model complex nonlinear patterns, often presented in high-dimensional data such as images and text.

Uncertainty Quantification

Adaptive Partially-Observed Sequential Change Detection and Isolation

no code implementations9 Aug 2022 Xinyu Zhao, Jiuyun Hu, Yajun Mei, Hao Yan

High-dimensional data has become popular due to the easy accessibility of sensors in modern industrial applications.

Change Detection Change Point Detection

Adaptive Resources Allocation CUSUM for Binomial Count Data Monitoring with Application to COVID-19 Hotspot Detection

no code implementations9 Aug 2022 Jiuyun Hu, Yajun Mei, Sarah Holte, Hao Yan

In this paper, we present an efficient statistical method (denoted as "Adaptive Resources Allocation CUSUM") to robustly and efficiently detect the hotspot with limited sampling resources.

Change Point Detection

GAN-Based Multi-View Video Coding with Spatio-Temporal EPI Reconstruction

no code implementations7 May 2022 Chengdong Lan, Hao Yan, Cheng Luo, Tiesong Zhao

At the decoder side, we combine the SI and adjacent viewpoints to reconstruct intermediate views using the GAN generator.

Decoder Generative Adversarial Network +1

ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data

no code implementations25 Feb 2022 Michael Biehler, Hao Yan, Jianjun Shi

Unstructured point clouds with varying sizes are increasingly acquired in a variety of environments through laser triangulation or Light Detection and Ranging (LiDAR).

Dimensionality Reduction Quantization +1

Deep Spatio-temporal Sparse Decomposition for Trend Prediction and Anomaly Detection in Cardiac Electrical Conduction

no code implementations20 Sep 2021 Xinyu Zhao, Hao Yan, Zhiyong Hu, Dongping Du

Electrical conduction among cardiac tissue is commonly modeled with partial differential equations, i. e., reaction-diffusion equation, where the reaction term describes cellular stimulation and diffusion term describes electrical propagation.

Anomaly Detection

Filters for ISI Suppression in Molecular Communication via Diffusion

no code implementations29 Apr 2021 Ruifeng Zheng, Lin Lin, Hao Yan

The extent that ISI and noise are suppressed in an MCvD system determines its effectiveness, especially at a high data rate.

Covariance Distributions in Single Particle Tracking

no code implementations6 Oct 2020 Mary Lou P Bailey, Hao Yan, Ivan Surovtsev, Jessica F Williams, Megan C King, Simon G J Mochrie

This suggests that the origin of the theory-experiment discrepancy is associated with localization noise, which influences only the first two covariances.

Biological Physics

Partially Observable Online Change Detection via Smooth-Sparse Decomposition

no code implementations22 Sep 2020 Jie Guo, Hao Yan, Chen Zhang, Steven Hoi

We consider online change detection of high dimensional data streams with sparse changes, where only a subset of data streams can be observed at each sensing time point due to limited sensing capacities.

Bayesian Inference Change Detection +1

Automatic Storage Structure Selection for hybrid Workload

no code implementations15 Aug 2020 Hongzhi Wang, Yan Wei, Hao Yan

Therefore, the users of the database need to select the storage engine and design data model according to the workload encountered.

Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile

no code implementations23 Apr 2020 Ziyue Li, Hao Yan, Chen Zhang, Fugee Tsung

Spatiotemporal data is very common in many applications, such as manufacturing systems and transportation systems.

Clustering Tensor Decomposition

Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction

1 code implementation11 Dec 2019 Ziyue Li, Nurettin Dorukhan Sergin, Hao Yan, Chen Zhang, Fugee Tsung

Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data.

Tensor Decomposition

Toward a Better Monitoring Statistic for Profile Monitoring via Variational Autoencoders

no code implementations1 Nov 2019 Nurettin Sergin, Hao Yan

To do so, we formulate nonlinear and probabilistic extensions of the monitoring statistics from classical approaches as the expected reconstruction error (ERE) and the KL-divergence (KLD) based monitoring statistics.

AKM$^2$D : An Adaptive Framework for Online Sensing and Anomaly Quantification

no code implementations4 Oct 2019 Hao Yan, Kamran Paynabar, Jianjun Shi

In point-based sensing systems such as coordinate measuring machines (CMM) and laser ultrasonics where complete sensing is impractical due to the high sensing time and cost, adaptive sensing through a systematic exploration is vital for online inspection and anomaly quantification.

Anomaly Detection

Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling

1 code implementation9 Jun 2019 Hao Peng, Jian-Xin Li, Hao Yan, Qiran Gong, Senzhang Wang, Lin Liu, Lihong Wang, Xiang Ren

Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario.

Link Prediction Multi-Label Classification +1

Multiple profiles sensor-based monitoring and anomaly detection

no code implementations JOURNAL OF QUALITY TECHNOLOGY 2018 Chen Zhang, Hao Yan, Seungho Lee, Jianjun Shi

However, there are several challenges in developing an effective process monitoring system: (i) data streams generated by multiple sensors are high-dimensional profiles; (ii) sensor signals are affected by noise due to system-inherent variations; (iii) signals of different sensors have cluster-wise features; and (iv) an anomaly may cause only sparse changes of sensor signals.

Anomaly Detection

Structured Point Cloud Data Analysis via Regularized Tensor Regression for Process Modeling and Optimization

no code implementations26 Jul 2018 Hao Yan, Kamran Paynabar, Massimo Pacella

Advanced 3D metrology technologies such as Coordinate Measuring Machine (CMM) and laser 3D scanners have facilitated the collection of massive point cloud data, beneficial for process monitoring, control and optimization.


Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning

no code implementations11 Apr 2018 Chen Zhang, Hao Yan, Seungho Lee, Jianjun Shi

Multivariate functional data from a complex system are naturally high-dimensional and have complex cross-correlation structure.


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