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Zixiang Chen
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Journal Articles
- 2024
- [j14]Qiang Fang, Yue Yang, Hao Wang, Hanxi Sun, Jiangming Chen, Zixiang Chen, Tian Pu, Xiaoqing Zhang, Fubao Liu:
LCRNet: local cross-channel recalibration network for liver cancer classification based on CT images. Health Inf. Sci. Syst. 12(1): 5 (2024) - 2023
- [j13]Zixiang Chen
, Na Zhang, Qiyang Zhang, Kaiyi Liang, Zhenxing Huang, Guotao Quan, Xiang Li, Dong Liang, Zhanli Hu:
Low-dose dynamic cerebral perfusion CT reconstruction based on voxel-level TAC correction (VTC). Biomed. Signal Process. Control. 86(Part B): 105225 (2023) - 2022
- [j12]Zhengyong Huang, Si Tang, Zixiang Chen, Guoshuai Wang, Hao Shen, Yun Zhou, Haining Wang, Wei Fan, Dong Liang, Yingying Hu, Zhanli Hu
TG-Net: Combining transformer and GAN for nasopharyngeal carcinoma tumor segmentation based on total-body uEXPLORER PET/CT scanner. Comput. Biol. Medicine 148: 105869 (2022) - [j11]Zhengyong Huang, Sijuan Zou, Guoshuai Wang, Zixiang Chen, Hao Shen, Haiyan Wang, Na Zhang, Lu Zhang, Fan Yang
ISA-Net: Improved spatial attention network for PET-CT tumor segmentation. Comput. Methods Programs Biomed. 226: 107129 (2022) - [j10]Zixiang Chen
High Temporal Resolution Total-Body Dynamic PET Imaging Based on Pixel-Level Time-Activity Curve Correction. IEEE Trans. Biomed. Eng. 69(12): 3689-3702 (2022) - 2021
- [j9]Zhanglei Ouyang, Shujun Zhao, Zhaoping Cheng
Dynamic PET Imaging Using Dual Texture Features. Frontiers Comput. Neurosci. 15: 819840 (2021) - [j8]Yongxin Ni, Xiancong Chen, Weike Pan, Zixiang Chen, Zhong Ming:
Factored heterogeneous similarity model for recommendation with implicit feedback. Neurocomputing 455: 59-67 (2021) - [j7]Zhenxing Huang
Learning a Deep CNN Denoising Approach Using Anatomical Prior Information Implemented With Attention Mechanism for Low-Dose CT Imaging on Clinical Patient Data From Multiple Anatomical Sites. IEEE J. Biomed. Health Informatics 25(9): 3416-3427 (2021) - 2020
- [j6]Wei Cai, Weike Pan, Jixiong Liu, Zixiang Chen, Zhong Ming:
k-Reciprocal nearest neighbors algorithm for one-class collaborative filtering. Neurocomputing 381: 207-216 (2020) - [j5]Zixiang Chen, Wanqi Ma, Wei Dai, Weike Pan, Zhong Ming:
Conditional restricted Boltzmann machine for item recommendation. Neurocomputing 385: 269-277 (2020) - [j4]Jing Lin, Weike Pan, Lin Li
Matrix factorization with heterogeneous multiclass preference context. Neurocomputing 390: 148-157 (2020) - [j3]Zhiyuan Huang, Zixiang Chen, Qiyang Zhang
CaGAN: A Cycle-Consistent Generative Adversarial Network With Attention for Low-Dose CT Imaging. IEEE Trans. Computational Imaging 6: 1203-1218 (2020) - 2014
- [j2]Haiping Ma, Dan Simon
Hybrid biogeography-based evolutionary algorithms. Eng. Appl. Artif. Intell. 30: 213-224 (2014) - 2013
- [j1]Haiping Ma, Dan Simon, Minrui Fei, Zixiang Chen:
On the equivalences and differences of evolutionary algorithms. Eng. Appl. Artif. Intell. 26(10): 2397-2407 (2013)
Conference and Workshop Papers
- 2024
- [c18]Zixiang Chen, Yihe Deng, Yuanzhi Li, Quanquan Gu:
Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP. ICLR 2024 - [c17]Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett:
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ICLR 2024 - [c16]Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan Gu:
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models. ICML 2024 - 2023
- [c15]Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael I. Jordan:
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. ICLR 2023 - [c14]Yiwen Kou, Zixiang Chen, Yuan Cao, Quanquan Gu:
How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study. ICLR 2023 - [c13]Xinzhe Zuo, Zixiang Chen, Huaxiu Yao, Yuan Cao, Quanquan Gu:
Understanding Train-Validation Split in Meta-Learning with Neural Networks. ICLR 2023 - [c12]Yiwen Kou, Zixiang Chen, Yuanzhou Chen, Quanquan Gu:
Benign Overfitting in Two-layer ReLU Convolutional Neural Networks. ICML 2023: 17615-17659 - [c11]Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. ICML 2023: 37919-37951 - [c10]Zixiang Chen, Junkai Zhang, Yiwen Kou, Xiangning Chen, Cho-Jui Hsieh, Quanquan Gu:
Why Does Sharpness-Aware Minimization Generalize Better Than SGD? NeurIPS 2023 - [c9]Yiwen Kou, Zixiang Chen, Quanquan Gu:
Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data. NeurIPS 2023 - 2022
- [c8]Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu:
Self-training Converts Weak Learners to Strong Learners in Mixture Models. AISTATS 2022: 8003-8021 - [c7]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. ALT 2022: 176-204 - [c6]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games. ALT 2022: 227-261 - [c5]Yuan Cao, Zixiang Chen, Misha Belkin, Quanquan Gu:
Benign Overfitting in Two-layer Convolutional Neural Networks. NeurIPS 2022 - [c4]Zixiang Chen, Yihe Deng, Yue Wu, Quanquan Gu, Yuanzhi Li:
Towards Understanding the Mixture-of-Experts Layer in Deep Learning. NeurIPS 2022 - 2021
- [c3]Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu:
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? ICLR 2021 - 2020
- [c2]Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang:
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks. NeurIPS 2020 - 2017
- [c1]Yunfeng Huang, Zixiang Chen, Lin Li
k-CoFi: Modeling k-Granularity Preference Context in Collaborative Filtering. SmartCom 2017: 406-416
Informal and Other Publications
- 2024
- [i23]Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan Gu:
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models. CoRR abs/2401.01335 (2024) - [i22]Huizhuo Yuan, Zixiang Chen, Kaixuan Ji, Quanquan Gu:
Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation. CoRR abs/2402.10210 (2024) - [i21]Zixiang Chen, Jun Han, Yongqian Li, Yiwen Kou, Eran Halperin, Robert E. Tillman, Quanquan Gu:
Guided Discrete Diffusion for Electronic Health Record Generation. CoRR abs/2404.12314 (2024) - [i20]Yiwen Kou, Zixiang Chen, Quanquan Gu, Sham M. Kakade:
Matching the Statistical Query Lower Bound for k-sparse Parity Problems with Stochastic Gradient Descent. CoRR abs/2404.12376 (2024) - [i19]Zikun Zhang, Zixiang Chen, Quanquan Gu:
Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis. CoRR abs/2410.02321 (2024) - 2023
- [i18]Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples. CoRR abs/2303.02255 (2023) - [i17]Yiwen Kou, Zixiang Chen, Yuanzhou Chen, Quanquan Gu:
Benign Overfitting for Two-layer ReLU Networks. CoRR abs/2303.04145 (2023) - [i16]Zixiang Chen, Yihe Deng, Yuanzhi Li, Quanquan Gu:
Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP. CoRR abs/2310.00927 (2023) - [i15]Zixiang Chen, Junkai Zhang, Yiwen Kou, Xiangning Chen, Cho-Jui Hsieh, Quanquan Gu:
Why Does Sharpness-Aware Minimization Generalize Better Than SGD? CoRR abs/2310.07269 (2023) - [i14]Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett:
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? CoRR abs/2310.08391 (2023) - [i13]Yiwen Kou, Zixiang Chen, Quanquan Gu:
Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data. CoRR abs/2310.18935 (2023) - [i12]Yihe Deng, Weitong Zhang, Zixiang Chen, Quanquan Gu:
Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves. CoRR abs/2311.04205 (2023) - [i11]Zixiang Chen, Huizhuo Yuan, Yongqian Li, Yiwen Kou, Junkai Zhang, Quanquan Gu:
Fast Sampling via De-randomization for Discrete Diffusion Models. CoRR abs/2312.09193 (2023) - 2022
- [i10]Yuan Cao, Zixiang Chen, Mikhail Belkin, Quanquan Gu:
Benign Overfitting in Two-layer Convolutional Neural Networks. CoRR abs/2202.06526 (2022) - [i9]Zixiang Chen, Yihe Deng, Yue Wu, Quanquan Gu, Yuanzhi Li:
Towards Understanding Mixture of Experts in Deep Learning. CoRR abs/2208.02813 (2022) - [i8]Zixiang Chen, Chris Junchi Li, Angela Yuan, Quanquan Gu, Michael I. Jordan:
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. CoRR abs/2209.15634 (2022) - [i7]Zhengyong Huang, Sijuan Zou, Guoshuai Wang, Zixiang Chen, Hao Shen, Haiyan Wang, Na Zhang, Lu Zhang, Fan Yang, Haining Wang, Dong Liang, Tianye Niu, Xiaohua Zhu, Zhanli Hu:
ISA-Net: Improved spatial attention network for PET-CT tumor segmentation. CoRR abs/2211.02256 (2022) - 2021
- [i6]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation. CoRR abs/2102.07404 (2021) - [i5]Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu:
Self-training Converts Weak Learners to Strong Learners in Mixture Models. CoRR abs/2106.13805 (2021) - [i4]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. CoRR abs/2110.13144 (2021) - 2020
- [i3]Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang:
Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds. CoRR abs/2002.04026 (2020) - 2019
- [i2]Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu:
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? CoRR abs/1911.12360 (2019) - 2018
- [i1]Tianyang Hu, Zixiang Chen, Hanxi Sun, Jincheng Bai, Mao Ye, Guang Cheng:
Stein Neural Sampler. CoRR abs/1810.03545 (2018)
Coauthor Index
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