no code implementations • ECNLP (ACL) 2022 • Yang Liu, Varnith Chordia, Hua Li, Siavash Fazeli Dehkordy, Yifei Sun, Vincent Gao, Na Zhang
To harness such information to better serve customers, in this paper, we created a machine learning approach to automatically identify product issues and uncover root causes from the customer feedback text.
1 code implementation • NeurIPS 2023 • Zhenglin Huang, Xiaoan Bao, Na Zhang, Qingqi Zhang, Xiaomei Tu, Biao Wu, Xi Yang
Data augmentation has been proven effective for training high-accuracy convolutional neural network classifiers by preventing overfitting.
2 code implementations • 9 Aug 2023 • Hui Zeng, Jingyuan Xue, Meng Hao, Chen Sun, Bin Ning, Na Zhang
This paper unveils CG-Eval, the first-ever comprehensive and automated evaluation framework designed for assessing the generative capabilities of large Chinese language models across a spectrum of academic disciplines.
no code implementations • 8 Jul 2023 • Iqbal Nouyed, Na Zhang
To perform this without experimental bias, we have developed a new protocol for recognition with low quality face photos and validate the performance experimentally.
no code implementations • 6 Jul 2023 • Na Zhang
Our work is first to perform facial landmark detection evaluation on the mobile still data, i. e., face images from MOBIO database.
no code implementations • 5 Jul 2023 • Na Zhang
In this paper, we partition face images into three different quality sets to evaluate the performance of deep learning methods on cross-quality face images in the wild, and then design a human face verification experiment on these cross-quality data.
no code implementations • 20 Apr 2023 • Mindi Ruan, Xiangxu Yu, Na Zhang, Chuanbo Hu, Shuo Wang, Xin Li
How can we teach a computer to recognize 10, 000 different actions?
no code implementations • 18 Feb 2023 • Na Zhang, Xudong Liu, Xin Li, Guo-Jun Qi
Semantic face image manipulation has received increasing attention in recent years.
no code implementations • 4 Nov 2022 • Zhengyong Huang, Sijuan Zou, Guoshuai Wang, Zixiang Chen, Hao Shen, HaiYan Wang, Na Zhang, Lu Zhang, Fan Yang, Haining Wangg, Dong Liang, Tianye Niu, Xiaohua Zhuc, Zhanli Hua
In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information.
1 code implementation • 27 Oct 2022 • Na Zhang, Shan Jia, Siwei Lyu, Xin Li
Our technical contributions include: 1) We propose a fusion-based few-shot learning (FSL) method to learn discriminative features that can generalize to unseen morphing attack types from predefined presentation attacks; 2) The proposed FSL based on the fusion of the PRNU model and Noiseprint network is extended from binary MAD to multiclass morphing attack fingerprinting (MAF).
no code implementations • 5 Jun 2022 • Jiarong Ye, Yin-Ting Yeh, Yuan Xue, Ziyang Wang, Na Zhang, He Liu, Kunyan Zhang, RyeAnne Ricker, Zhuohang Yu, Allison Roder, Nestor Perea Lopez, Lindsey Organtini, Wallace Greene, Susan Hafenstein, Huaguang Lu, Elodie Ghedin, Mauricio Terrones, Shengxi Huang, Sharon Xiaolei Huang
We present such a machine learning approach for analyzing Raman spectra of human and avian viruses.
no code implementations • ICLR 2021 • Yihao Feng, Ziyang Tang, Na Zhang, Qiang Liu
Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies.
1 code implementation • NeurIPS 2020 • Xingchao Liu, Xing Han, Na Zhang, Qiang Liu
In this work, we propose to certify the monotonicity of the general piece-wise linear neural networks by solving a mixed integer linear programming problem. This provides a new general approach for learning monotonic neural networks with arbitrary model structures.
no code implementations • NeurIPS 2020 • Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, Qiang Liu
Off-policy evaluation provides an essential tool for evaluating the effects of different policies or treatments using only observed data.
1 code implementation • 6 Apr 2020 • Xinglei Wang, Xuefeng Guan, Jun Cao, Na Zhang, Huayi Wu
This model builds on sequence to sequence (seq2seq) architecture to capture temporal feature and relies on graph convolution for aggregating spatial information.
no code implementations • 14 Apr 2019 • Na Zhang, Xuefeng Guan, Jun Cao, Xinglei Wang, Huayi Wu
In this paper, we propose a hybrid approach that learns the spatio-temporal dependency in traffic flows and predicts short-term traffic speeds on a road network.