Search Results for author: Teng Zhang

Found 49 papers, 8 papers with code

Harnessing Knowledge Retrieval with Large Language Models for Clinical Report Error Correction

no code implementations21 Jun 2024 Jinge Wu, Zhaolong Wu, Abul Hasan, Yunsoo Kim, Jason P. Y. Cheung, Teng Zhang, Honghan Wu

This study proposes an approach for error correction in clinical radiology reports, leveraging large language models (LLMs) and retrieval-augmented generation (RAG) techniques.

RAG Retrieval

Chain-of-Though (CoT) prompting strategies for medical error detection and correction

no code implementations13 Jun 2024 Zhaolong Wu, Abul Hasan, Jinge Wu, Yunsoo Kim, Jason P. Y. Cheung, Teng Zhang, Honghan Wu

We report results for three methods of few-shot In-Context Learning (ICL) augmented with Chain-of-Thought (CoT) and reason prompts using a large language model (LLM).

In-Context Learning Language Modelling +1

Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation

1 code implementation23 May 2024 Hongxu Jiang, Muhammad Imran, Linhai Ma, Teng Zhang, Yuyin Zhou, Muxuan Liang, Kuang Gong, Wei Shao

This is primarily due to the high computational cost associated with (1) the use of large number of time steps (e. g., 1, 000) in diffusion processes and (2) the increased dimensionality of medical images, which are often 3D or 4D.

Image Denoising Image Super-Resolution +1

Weakly supervised alignment and registration of MR-CT for cervical cancer radiotherapy

no code implementations21 May 2024 Jjahao Zhang, Yin Gu, Deyu Sun, Yuhua Gao, Ming Gao, Ming Cui, Teng Zhang, He Ma

The fusion of the information characteristics of both computed tomography (CT) and magnetic resonance imaging(MRI) modalities may be useful in achieving a precise outline of the extent of paracervical tissue invasion.

Computed Tomography (CT) Image Registration +1

A Subspace-Constrained Tyler's Estimator and its Applications to Structure from Motion

1 code implementation CVPR 2024 Feng Yu, Teng Zhang, Gilad Lerman

We present the subspace-constrained Tyler's estimator (STE) designed for recovering a low-dimensional subspace within a dataset that may be highly corrupted with outliers.

3D Reconstruction

Theoretical Guarantees for the Subspace-Constrained Tyler's Estimator

no code implementations27 Mar 2024 Gilad Lerman, Feng Yu, Teng Zhang

It further shows that under the generalized haystack model, STE initialized by the Tyler's M-estimator (TME), can recover the subspace when the fraction of iniliers is too small for TME to handle.

Differentially Private Pre-Trained Model Fusion using Decentralized Federated Graph Matching

no code implementations5 Nov 2023 Qian Chen, Yiqiang Chen, Xinlong Jiang, Teng Zhang, Weiwei Dai, Wuliang Huang, Zhen Yan, Bo Ye

Model fusion is becoming a crucial component in the context of model-as-a-service scenarios, enabling the delivery of high-quality model services to local users.

Graph Matching Privacy Preserving

Improved Convergence Rates of Windowed Anderson Acceleration for Symmetric Fixed-Point Iterations

no code implementations4 Nov 2023 Casey Garner, Gilad Lerman, Teng Zhang

This paper studies the commonly utilized windowed Anderson acceleration (AA) algorithm for fixed-point methods, $x^{(k+1)}=q(x^{(k)})$.

STGIN: Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation

no code implementations5 Sep 2023 Shaohua Liu, Yu Qi, Gen Li, Mingjian Chen, Teng Zhang, Jia Cheng, Jun Lei

Specifically, we construct subgraphs of spatial, temporal, spatial-temporal, and global views respectively to precisely characterize the user's interests in various contexts.

graph construction Graph Sampling

FedBone: Towards Large-Scale Federated Multi-Task Learning

no code implementations30 Jun 2023 Yiqiang Chen, Teng Zhang, Xinlong Jiang, Qian Chen, Chenlong Gao, Wuliang Huang

The conflicting gradient projection technique is used to enhance the generalization of the large-scale general model between different tasks.

Federated Learning Multi-Task Learning

Scalable Optimal Margin Distribution Machine

2 code implementations8 May 2023 Yilin Wang, Nan Cao, Teng Zhang, Xuanhua Shi, Hai Jin

Optimal margin Distribution Machine (ODM) is a newly proposed statistical learning framework rooting in the novel margin theory, which demonstrates better generalization performance than the traditional large margin based counterparts.

Understanding Overfitting in Adversarial Training via Kernel Regression

no code implementations13 Apr 2023 Teng Zhang, Kang Li

Adversarial training and data augmentation with noise are widely adopted techniques to enhance the performance of neural networks.

Data Augmentation regression

Theoretical Guarantees for Sparse Principal Component Analysis based on the Elastic Net

no code implementations29 Dec 2022 Teng Zhang, Haoyi Yang, Lingzhou Xue

Sparse principal component analysis (SPCA) is widely used for dimensionality reduction and feature extraction in high-dimensional data analysis.

Dimensionality Reduction

Robust Regularized Low-Rank Matrix Models for Regression and Classification

no code implementations14 May 2022 Hsin-Hsiung Huang, Feng Yu, Xing Fan, Teng Zhang

While matrix variate regression models have been studied in many existing works, classical statistical and computational methods for the analysis of the regression coefficient estimation are highly affected by high dimensional and noisy matrix-valued predictors.

Classification regression

On the Optimization of Margin Distribution

no code implementations29 Apr 2022 Meng-Zhang Qian, Zheng Ai, Teng Zhang, Wei Gao

Margin has played an important role on the design and analysis of learning algorithms during the past years, mostly working with the maximization of the minimum margin.

The Design and Implementation of a Broadly Applicable Algorithm for Optimizing Intra-Day Surgical Scheduling

no code implementations14 Mar 2022 Jin Xie, Teng Zhang, Jose Blanchet, Peter Glynn, Matthew Randolph, David Scheinker

In order for an algorithm to see sustained use, it must be compatible with changes to hospital capacity, patient volumes, and scheduling practices.

Scheduling

Distributed Optimal Margin Distribution Machine

no code implementations29 Sep 2021 Yilin Wang, Nan Cao, Teng Zhang, Hai Jin

Optimal margin Distribution Machine (ODM), a newly proposed statistical learning framework rooting in the novel margin theory, demonstrates better generalization performance than the traditional large margin based counterparts.

Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers

no code implementations22 Sep 2021 Honggang Yu, Shihfeng Zeng, Teng Zhang, Ing-Chao Lin, Yier Jin

As a result, our theoretical proofs provide support to more efficient active learning methods with the help of adversarial examples, contrary to previous works where adversarial examples are often used as destructive solutions.

Active Learning Adversarial Attack +1

FaceCook: Face Generation Based on Linear Scaling Factors

no code implementations8 Sep 2021 Tianren Wang, Can Peng, Teng Zhang, Brian Lovell

With the excellent disentanglement properties of state-of-the-art generative models, image editing has been the dominant approach to control the attributes of synthesised face images.

Disentanglement Diversity +1

Understanding and controlling hexagonal patterns of wrinkles in neo-Hookean elastic bilayer structures

no code implementations23 Feb 2021 Teng Zhang

We employ large-scale finite element simulations of a bilayer neo-Hookean solid (e. g., a film bonded on a substrate) to explore mechanical principles that govern the formation of hexagonal wrinkling patterns and strategies for making nearly perfect hexagonal patterns.

Soft Condensed Matter

ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network

no code implementations20 Feb 2021 Xing Fan, Marianna Pensky, Feng Yu, Teng Zhang

The paper considers a Mixture Multilayer Stochastic Block Model (MMLSBM), where layers can be partitioned into groups of similar networks, and networks in each group are equipped with a distinct Stochastic Block Model.

Clustering Stochastic Block Model +1

Applications of BERT Based Sequence Tagging Models on Chinese Medical Text Attributes Extraction

no code implementations22 Aug 2020 Gang Zhao, Teng Zhang, Chenxiao Wang, Ping Lv, Ji Wu

We convert the Chinese medical text attributes extraction task into a sequence tagging or machine reading comprehension task.

Diversity Machine Reading Comprehension

Faces à la Carte: Text-to-Face Generation via Attribute Disentanglement

no code implementations13 Jun 2020 Tianren Wang, Teng Zhang, Brian Lovell

Text-to-Face (TTF) synthesis is a challenging task with great potential for diverse computer vision applications.

Attribute Disentanglement +3

Memory Augmented Generative Adversarial Networks for Anomaly Detection

no code implementations7 Feb 2020 Ziyi Yang, Teng Zhang, Iman Soltani Bozchalooi, Eric Darve

Decoded memory units in MEMGAN are more interpretable and disentangled than previous methods, which further demonstrates the effectiveness of the memory mechanism.

Anomaly Detection

To What Extent Does Downsampling, Compression, and Data Scarcity Impact Renal Image Analysis?

no code implementations22 Sep 2019 Can Peng, Kun Zhao, Arnold Wiliem, Teng Zhang, Peter Hobson, Anthony Jennings, Brian C. Lovell

Critical findings are observed: (1) The best balance between detection accuracy, detection speed and file size is achieved at 8 times downsampling captured with a $40\times$ objective; (2) compression which reduces the file size dramatically, does not necessarily have an adverse effect on overall accuracy; (3) reducing the amount of training data to some extents causes a drop in precision but has a negligible impact on the recall; (4) in most cases, Faster R-CNN achieves the best accuracy in the glomerulus detection task.

Image Compression

An Algorithm for Graph-Fused Lasso Based on Graph Decomposition

1 code implementation6 Aug 2019 Feng Yu, Yi Yang, Teng Zhang

In comparison, this work proposes to decompose the objective function into two components, where one component is the loss function plus part of the total variation penalty, and the other component is the remaining total variation penalty.

Optimization and Control Computation

Deep Instance-Level Hard Negative Mining Model for Histopathology Images

1 code implementation24 Jun 2019 Meng Li, Lin Wu, Arnold Wiliem, Kun Zhao, Teng Zhang, Brian C. Lovell

Histopathology image analysis can be considered as a Multiple instance learning (MIL) problem, where the whole slide histopathology image (WSI) is regarded as a bag of instances (i. e, patches) and the task is to predict a single class label to the WSI.

General Classification Multiple Instance Learning

CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels

no code implementations24 Jun 2019 Sam Maksoud, Arnold Wiliem, Kun Zhao, Teng Zhang, Lin Wu, Brian C. Lovell

This is because the system can ignore the attention mechanism by assigning equal weights for all members.

regression

SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks

no code implementations20 Mar 2018 Teng Zhang, Johanna Carvajal, Daniel F. Smith, Kun Zhao, Arnold Wiliem, Peter Hobson, Anthony Jennings, Brian C. Lovell

In order to address the quality assessment problem, we propose a deep neural network based framework to automatically assess the slide quality in a semantic way.

CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions

1 code implementation ICML 2018 Kevin Tian, Teng Zhang, James Zou

However, in addition to the text data itself, we often have additional covariates associated with individual corpus documents---e. g. the demographic of the author, time and venue of publication---and we would like the embedding to naturally capture this information.

Natural Questions Tensor Decomposition

Learning Covariate-Specific Embeddings with Tensor Decompositions

no code implementations ICLR 2018 Kevin Tian, Teng Zhang, James Zou

In addition to the text data itself, we often have additional covariates associated with individual documents in the corpus---e. g. the demographic of the author, time and venue of publication, etc.---and we would like the embedding to naturally capture the information of the covariates.

Natural Questions Tensor Decomposition +1

TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition

2 code implementations7 Dec 2017 Teng Zhang, Arnold Wiliem, Siqi Yang, Brian C. Lovell

While it can greatly increase the scope and benefits of the current security surveillance systems, performing such a task using thermal images is a challenging problem compared to face recognition task in the Visible Light Domain (VLD).

Face Recognition Generative Adversarial Network

Exact Camera Location Recovery by Least Unsquared Deviations

no code implementations27 Sep 2017 Gilad Lerman, Yunpeng Shi, Teng Zhang

We establish exact recovery for the Least Unsquared Deviations (LUD) algorithm of Ozyesil and Singer.

Stochastic Primal-Dual Proximal ExtraGradient Descent for Compositely Regularized Optimization

no code implementations20 Aug 2017 Tianyi Lin, Linbo Qiao, Teng Zhang, Jiashi Feng, Bofeng Zhang

This optimization model abstracts a number of important applications in artificial intelligence and machine learning, such as fused Lasso, fused logistic regression, and a class of graph-guided regularized minimization.

regression

Multi-Class Optimal Margin Distribution Machine

no code implementations ICML 2017 Teng Zhang, Zhi-Hua Zhou

It still remains open for multi-class classification, and due to the complexity of margin for multi-class classification, optimizing its distribution by mean and variance can also be difficult.

Binary Classification Classification +2

Robust PCA by Manifold Optimization

no code implementations1 Aug 2017 Teng Zhang, Yi Yang

Robust PCA is a widely used statistical procedure to recover a underlying low-rank matrix with grossly corrupted observations.

The Monkeytyping Solution to the YouTube-8M Video Understanding Challenge

1 code implementation16 Jun 2017 He-Da Wang, Teng Zhang, Ji Wu

This article describes the final solution of team monkeytyping, who finished in second place in the YouTube-8M video understanding challenge.

General Classification Video Classification +1

A Well-Tempered Landscape for Non-convex Robust Subspace Recovery

no code implementations13 Jun 2017 Tyler Maunu, Teng Zhang, Gilad Lerman

The practicality of the deterministic condition is demonstrated on some statistical models of data, and the method achieves almost state-of-the-art recovery guarantees on the Haystack Model for different regimes of sample size and ambient dimension.

Spectral clustering in the dynamic stochastic block model

no code implementations2 May 2017 Marianna Pensky, Teng Zhang

We estimate the edge probability tensor by a kernel-type procedure and extract the group memberships of the nodes by spectral clustering.

Clustering Stochastic Block Model

Anisotropic twicing for single particle reconstruction using autocorrelation analysis

no code implementations26 Apr 2017 Tejal Bhamre, Teng Zhang, Amit Singer

The missing phase problem in X-ray crystallography is commonly solved using the technique of molecular replacement, which borrows phases from a previously solved homologous structure, and appends them to the measured Fourier magnitudes of the diffraction patterns of the unknown structure.

Optimal Margin Distribution Machine

no code implementations12 Apr 2016 Teng Zhang, Zhi-Hua Zhou

Support vector machine (SVM) has been one of the most popular learning algorithms, with the central idea of maximizing the minimum margin, i. e., the smallest distance from the instances to the classification boundary.

Denoising and Covariance Estimation of Single Particle Cryo-EM Images

no code implementations22 Feb 2016 Tejal Bhamre, Teng Zhang, Amit Singer

In CWF, the covariance matrix of the projection images is used within the classical Wiener filtering framework for solving the image restoration deconvolution problem.

Denoising Image Restoration

Orthogonal Matrix Retrieval in Cryo-Electron Microscopy

no code implementations1 Dec 2014 Tejal Bhamre, Teng Zhang, Amit Singer

In single particle reconstruction (SPR) from cryo-electron microscopy (cryo-EM), the 3D structure of a molecule needs to be determined from its 2D projection images taken at unknown viewing directions.

Retrieval

Large Margin Distribution Machine

no code implementations5 Nov 2013 Teng Zhang, Zhi-Hua Zhou

In this paper, we propose the Large margin Distribution Machine (LDM), which tries to achieve a better generalization performance by optimizing the margin distribution.

Robust subspace recovery by Tyler's M-estimator

no code implementations7 Jun 2012 Teng Zhang

This paper considers the problem of robust subspace recovery: given a set of $N$ points in $\mathbb{R}^D$, if many lie in a $d$-dimensional subspace, then can we recover the underlying subspace?

Position

Robust computation of linear models by convex relaxation

no code implementations18 Feb 2012 Gilad Lerman, Michael McCoy, Joel A. Tropp, Teng Zhang

Consider a dataset of vector-valued observations that consists of noisy inliers, which are explained well by a low-dimensional subspace, along with some number of outliers.

A Novel M-Estimator for Robust PCA

no code implementations20 Dec 2011 Teng Zhang, Gilad Lerman

That is, we assume a data set that some of its points are sampled around a fixed subspace and the rest of them are spread in the whole ambient space, and we aim to recover the fixed underlying subspace.

lp-Recovery of the Most Significant Subspace among Multiple Subspaces with Outliers

no code implementations18 Dec 2010 Gilad Lerman, Teng Zhang

We say that one of the underlying subspaces of the model is most significant if its mixture weight is higher than the sum of the mixture weights of all other subspaces.

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