Search Results for author: Jiong Zhang

Found 22 papers, 10 papers with code

Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification

no code implementations ACL (MetaNLP) 2021 Yue Li, Jiong Zhang

The semi-supervised meta training approach avoids overfitting prototypical networks on a small number of labeled training examples and quickly learns cross-domain task-specific representation only from a few supporting examples.

Cross-Domain Few-Shot intent-classification +3

Automating Vessel Segmentation in the Heart and Brain: A Trend to Develop Multi-Modality and Label-Efficient Deep Learning Techniques

no code implementations2 Apr 2024 Nazik Elsayed, Yousuf Babiker M. Osman, Cheng Li, Jiong Zhang, Shanshan Wang

Cardio-cerebrovascular diseases are the leading causes of mortality worldwide, whose accurate blood vessel segmentation is significant for both scientific research and clinical usage.


PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models

1 code implementation5 Dec 2023 Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang, Mutasem Al-Darabsah, Choon Hui Teo, Cho-Jui Hsieh, Hsiang-Fu Yu, S. V. N. Vishwanathan

For product search, PEFA improves the Recall@100 of the fine-tuned ERMs by an average of 5. 3% and 14. 5%, for PEFA-XS and PEFA-XL, respectively.

Retrieval Text Retrieval

Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images

no code implementations10 Nov 2023 Shouyue Liu, Jinkui Hao, Yanwu Xu, Huazhu Fu, Xinyu Guo, Jiang Liu, Yalin Zheng, Yonghuai Liu, Jiong Zhang, Yitian Zhao

Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature.

Alzheimer's Disease Detection Decision Making

Representer Point Selection for Explaining Regularized High-dimensional Models

no code implementations31 May 2023 Che-Ping Tsai, Jiong Zhang, Eli Chien, Hsiang-Fu Yu, Cho-Jui Hsieh, Pradeep Ravikumar

We introduce a novel class of sample-based explanations we term high-dimensional representers, that can be used to explain the predictions of a regularized high-dimensional model in terms of importance weights for each of the training samples.

Binary Classification Collaborative Filtering +1

PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation

1 code implementation21 May 2023 Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu

Unlike most existing XMC frameworks that treat labels and input instances as featureless indicators and independent entries, PINA extracts information from the label metadata and the correlations among training instances.

Extreme Multi-Label Classification Recommendation Systems

Retinal Structure Detection in OCTA Image via Voting-based Multi-task Learning

1 code implementation23 Aug 2022 Jinkui Hao, Ting Shen, Xueli Zhu, Yonghuai Liu, Ardhendu Behera, Dan Zhang, Bang Chen, Jiang Liu, Jiong Zhang, Yitian Zhao

Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making.

Classification Decision Making +1

Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction

no code implementations25 Jul 2022 Huaying Hao, Cong Xu, Dan Zhang, Qifeng Yan, Jiong Zhang, Yue Liu, Yitian Zhao

To be more specific, we first perform a simple degradation of the 3x3 mm2/high-resolution (HR) image to obtain the synthetic LR image.

Domain Adaptation Image Super-Resolution

Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction

4 code implementations ICLR 2022 Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, Inderjit S Dhillon

We also provide a theoretical analysis that justifies the use of XMC over link prediction and motivates integrating XR-Transformers, a powerful method for solving XMC problems, into the GIANT framework.

Extreme Multi-Label Classification Language Modelling +3

Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification

1 code implementation NeurIPS 2021 Jiong Zhang, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S. Dhillon

Despite leveraging pre-trained transformer models for text representation, the fine-tuning procedure of transformer models on large label space still has lengthy computational time even with powerful GPUs.

Multi Label Text Classification Multi-Label Text Classification +2

3D Vessel Reconstruction in OCT-Angiography via Depth Map Estimation

no code implementations26 Feb 2021 Shuai Yu, Jianyang Xie, Jinkui Hao, Yalin Zheng, Jiong Zhang, Yan Hu, Jiang Liu, Yitian Zhao

Experimental results demonstrate that our method is effective in the depth prediction and 3D vessel reconstruction for OCTA images.% results may be used to guide subsequent vascular analysis

Decision Making Depth Estimation +3

PECOS: Prediction for Enormous and Correlated Output Spaces

no code implementations12 Oct 2020 Hsiang-Fu Yu, Kai Zhong, Jiong Zhang, Wei-Cheng Chang, Inderjit S. Dhillon

In this paper, we propose the Prediction for Enormous and Correlated Output Spaces (PECOS) framework, a versatile and modular machine learning framework for solving prediction problems for very large output spaces, and apply it to the eXtreme Multilabel Ranking (XMR) problem: given an input instance, find and rank the most relevant items from an enormous but fixed and finite output space.

ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

1 code implementation10 Jul 2020 Yuhui Ma, Huaying Hao, Huazhu Fu, Jiong Zhang, Jianlong Yang, Jiang Liu, Yalin Zheng, Yitian Zhao

To address these issues, for the first time in the field of retinal image analysis we construct a dedicated Retinal OCT-A SEgmentation dataset (ROSE), which consists of 229 OCT-A images with vessel annotations at either centerline-level or pixel level.

Retinal Vessel Segmentation Segmentation

Minimal Paths for Tubular Structure Segmentation with Coherence Penalty and Adaptive Anisotropy

no code implementations21 Sep 2018 Da Chen, Jiong Zhang, Laurent D. Cohen

In this paper, we propose a new minimal path model associated with a dynamic Riemannian metric embedded with an appearance feature coherence penalty and an adaptive anisotropy enhancement term.

Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization

1 code implementation ICML 2018 Jiong Zhang, Qi Lei, Inderjit S. Dhillon

Theoretically, we demonstrate that our parameterization does not lose any expressive power, and show how it controls generalization of RNN for the classification task.

Learning Long Term Dependencies via Fourier Recurrent Units

2 code implementations ICML 2018 Jiong Zhang, Yibo Lin, Zhao Song, Inderjit S. Dhillon

In this paper we propose a simple recurrent architecture, the Fourier Recurrent Unit (FRU), that stabilizes the gradients that arise in its training while giving us stronger expressive power.

Retinal Microaneurysms Detection using Local Convergence Index Features

no code implementations21 Jul 2017 Behdad Dashtbozorg, Jiong Zhang, Bart M. ter Haar Romeny

Detection of microaneurysms is crucial for the early diagnosis of diabetic retinopathy and prevention of blindness.

Retrieving challenging vessel connections in retinal images by line co-occurrence statistics

no code implementations20 Oct 2016 Samaneh Abbasi-Sureshjani, Jiong Zhang, Remco Duits, Bart ter Haar Romeny

We propose to find line co-occurrence statistics from the centerlines of blood vessels in retinal images and show its remarkable similarity to a well-known probabilistic model for the connectivity pattern in the primary visual cortex.


Extreme Stochastic Variational Inference: Distributed and Asynchronous

no code implementations31 May 2016 Jiong Zhang, Parameswaran Raman, Shihao Ji, Hsiang-Fu Yu, S. V. N. Vishwanathan, Inderjit S. Dhillon

Moreover, it requires the parameters to fit in the memory of a single processor; this is problematic when the number of parameters is in billions.

Variational Inference

Numerical Approaches for Linear Left-invariant Diffusions on SE(2), their Comparison to Exact Solutions, and their Applications in Retinal Imaging

no code implementations13 Mar 2014 Jiong Zhang, Remco Duits, Gonzalo Sanguinetti, Bart M. ter Haar Romeny

We also provide an improvement of Mathematica algorithms for evaluating Mathieu-functions, crucial in implementations of the exact solutions.

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