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.
1 code implementation • 5 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.
no code implementations • 10 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.
no code implementations • 31 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.
1 code implementation • 21 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.
1 code implementation • 23 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.
no code implementations • 25 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.
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.
Ranked #2 on Node Property Prediction on ogbn-papers100M
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
1 code implementation • 23 Jun 2021 • Wei-Cheng Chang, Daniel Jiang, Hsiang-Fu Yu, Choon-Hui Teo, Jiong Zhang, Kai Zhong, Kedarnath Kolluri, Qie Hu, Nikhil Shandilya, Vyacheslav Ievgrafov, Japinder Singh, Inderjit S. Dhillon
In this paper, we aim to improve semantic product search by using tree-based XMC models where inference time complexity is logarithmic in the number of products.
no code implementations • 26 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
no code implementations • 12 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.
1 code implementation • 10 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.
Ranked #1 on Retinal Vessel Segmentation on ROSE-1 DVC
1 code implementation • NeurIPS 2019 • Jiong Zhang, Hsiang-Fu Yu, Inderjit S. Dhillon
In this paper, we propose AutoAssist, a simple framework to accelerate training of a deep neural network.
no code implementations • 21 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.
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.
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.
1 code implementation • 21 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.
no code implementations • 20 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.
no code implementations • 31 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.
no code implementations • 13 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.