Search Results for author: Yi Pan

Found 29 papers, 2 papers with code

Review of Large Vision Models and Visual Prompt Engineering

no code implementations3 Jul 2023 Jiaqi Wang, Zhengliang Liu, Lin Zhao, Zihao Wu, Chong Ma, Sigang Yu, Haixing Dai, Qiushi Yang, Yiheng Liu, Songyao Zhang, Enze Shi, Yi Pan, Tuo Zhang, Dajiang Zhu, Xiang Li, Xi Jiang, Bao Ge, Yixuan Yuan, Dinggang Shen, Tianming Liu, Shu Zhang

This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering.

Prompt Engineering

Intelligent gradient amplification for deep neural networks

no code implementations29 May 2023 Sunitha Basodi, Krishna Pusuluri, Xueli Xiao, Yi Pan

Deep learning models offer superior performance compared to other machine learning techniques for a variety of tasks and domains, but pose their own challenges.

Brain Structure-Function Fusing Representation Learning using Adversarial Decomposed-VAE for Analyzing MCI

no code implementations23 May 2023 Qiankun Zuo, Baiying Lei, Ning Zhong, Yi Pan, Shuqiang Wang

Integrating the brain structural and functional connectivity features is of great significance in both exploring brain science and analyzing cognitive impairment clinically.

Representation Learning

Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT

no code implementations29 Apr 2023 Zhenxiang Xiao, Yuzhong Chen, Lu Zhang, Junjie Yao, Zihao Wu, Xiaowei Yu, Yi Pan, Lin Zhao, Chong Ma, Xinyu Liu, Wei Liu, Xiang Li, Yixuan Yuan, Dinggang Shen, Dajiang Zhu, Tianming Liu, Xi Jiang

Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks.

Image Classification

Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

no code implementations3 Mar 2023 Junbin Mao, Jin Liu, Hanhe Lin, Hulin Kuang, Shirui Pan, Yi Pan

To effectively offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs, we propose a novel method called MMKGL (Multi-modal Multi-Kernel Graph Learning).

Disease Prediction Graph Embedding +1

Meta-data Study in Autism Spectrum Disorder Classification Based on Structural MRI

no code implementations9 Jun 2022 Ruimin Ma, Yanlin Wang, Yanjie Wei, Yi Pan

Accurate diagnosis of autism spectrum disorder (ASD) based on neuroimaging data has significant implications, as extracting useful information from neuroimaging data for ASD detection is challenging.

Generative Adversarial Networks: A Survey Towards Private and Secure Applications

no code implementations7 Jun 2021 Zhipeng Cai, Zuobin Xiong, Honghui Xu, Peng Wang, Wei Li, Yi Pan

Generative Adversarial Networks (GAN) have promoted a variety of applications in computer vision, natural language processing, etc.

Covid-19 Detection from Chest X-ray and Patient Metadata using Graph Convolutional Neural Networks

no code implementations20 May 2021 Thosini Bamunu Mudiyanselage, Nipuna Senanayake, Chunyan Ji, Yi Pan, Yanqing Zhang

The novel corona virus (Covid-19) has introduced significant challenges due to its rapid spreading nature through respiratory transmission.

Transfer Learning

Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates

no code implementations10 May 2021 Laizhong Cui, Xiaoxin Su, Yipeng Zhou, Yi Pan

Then, we further propose the boosted MUCSC (B-MUCSC) algorithm, a biased compression algorithm that can achieve an extremely high compression rate by grouping insignificant model updates into a super cluster.

Federated Learning

Infant Vocal Tract Development Analysis and Diagnosis by Cry Signals with CNN Age Classification

no code implementations23 Apr 2021 Chunyan Ji, Yi Pan

In this paper, we propose a non-invasive fast method of using infant cry signals with convolutional neural network (CNN) based age classification to diagnose the abnormality of the vocal tract development as early as 4-month age.

Age Classification General Classification

Infant Cry Classification with Graph Convolutional Networks

no code implementations31 Jan 2021 Chunyan Ji, Ming Chen, Bin Li, Yi Pan

We propose an approach of graph convolutional networks for robust infant cry classification.

Classification General Classification +1

Deep Neural Networks with Short Circuits for Improved Gradient Learning

no code implementations23 Sep 2020 Ming Yan, Xueli Xiao, Joey Tianyi Zhou, Yi Pan

Deep neural networks have achieved great success both in computer vision and natural language processing tasks.

Graph Convolution Networks Using Message Passing and Multi-Source Similarity Features for Predicting circRNA-Disease Association

no code implementations15 Sep 2020 Thosini Bamunu Mudiyanselage, Xiujuan Lei, Nipuna Senanayake, Yanqing Zhang, Yi Pan

In this paper, we propose a novel graph convolution network framework to learn features from a graph built with multi-source similarity information to predict circRNA-disease associations.

A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News

no code implementations23 Jul 2020 Yang Li, Yi Pan

This paper proposes to use sentiment analysis to extract useful information from multiple textual data sources and a blending ensemble deep learning model to predict future stock movement.

Philosophy Sentiment Analysis +3

Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic Algorithm

1 code implementation23 Jun 2020 Xueli Xiao, Ming Yan, Sunitha Basodi, Chunyan Ji, Yi Pan

However, traditional genetic algorithms with fixed-length chromosomes may not be a good fit for optimizing deep learning hyperparameters, because deep learning models have variable number of hyperparameters depending on the model depth.

Hyperparameter Optimization

Gradient Amplification: An efficient way to train deep neural networks

no code implementations16 Jun 2020 Sunitha Basodi, Chunyan Ji, Haiping Zhang, Yi Pan

Our proposed approach improves performance of these deep learning models even at higher learning rates, thereby allowing these models to achieve higher performance with reduced training time.

Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize

no code implementations27 Dec 2018 Chandra Khatri, Behnam Hedayatnia, Anu Venkatesh, Jeff Nunn, Yi Pan, Qing Liu, Han Song, Anna Gottardi, Sanjeev Kwatra, Sanju Pancholi, Ming Cheng, Qinglang Chen, Lauren Stubel, Karthik Gopalakrishnan, Kate Bland, Raefer Gabriel, Arindam Mandal, Dilek Hakkani-Tur, Gene Hwang, Nate Michel, Eric King, Rohit Prasad

In the second iteration of the competition in 2018, university teams advanced the state of the art by using context in dialog models, leveraging knowledge graphs for language understanding, handling complex utterances, building statistical and hierarchical dialog managers, and leveraging model-driven signals from user responses.

Knowledge Graphs Management +4

Three-Stream Convolutional Networks for Video-based Person Re-Identification

no code implementations22 Nov 2017 Zeng Yu, Tianrui Li, Ning Yu, Xun Gong, Ke Chen, Yi Pan

This paper aims to develop a new architecture that can make full use of the feature maps of convolutional networks.

Video-Based Person Re-Identification

Reconstruction of Hidden Representation for Robust Feature Extraction

no code implementations8 Oct 2017 Zeng Yu, Tianrui Li, Ning Yu, Yi Pan, Hongmei Chen, Bing Liu

We believe that minimizing the reconstruction error of the hidden representation is more robust than minimizing the Frobenius norm of the Jacobian matrix of the hidden representation.

Denoising Representation Learning

Parallel Large-Scale Attribute Reduction on Cloud Systems

no code implementations6 Oct 2016 Junbo Zhang, Tianrui Li, Yi Pan

The rapid growth of emerging information technologies and application patterns in modern society, e. g., Internet, Internet of Things, Cloud Computing and Tri-network Convergence, has caused the advent of the era of big data.

Cloud Computing feature selection

Effective-one-body model for black-hole binaries with generic mass ratios and spins

no code implementations11 Nov 2013 Andrea Taracchini, Alessandra Buonanno, Yi Pan, Tanja Hinderer, Michael Boyle, Daniel A. Hemberger, Lawrence E. Kidder, Geoffrey Lovelace, Abdul H. Mroue, Harald P. Pfeiffer, Mark A. Scheel, Bela Szilagyi, Nicholas W. Taylor, Anil Zenginoglu

Gravitational waves emitted by black-hole binary systems have the highest signal-to-noise ratio in LIGO and Virgo detectors when black-hole spins are aligned with the orbital angular momentum and extremal.

General Relativity and Quantum Cosmology

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