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Chongxuan Li
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2020 – today
- 2024
- [j6]Ke Su
, Hang Su, Chongxuan Li
Probabilistic Neural-Symbolic Models With Inductive Posterior Constraints. IEEE Trans. Neural Networks Learn. Syst. 35(2): 2667-2679 (2024) - [c48]Yang Zhang, Zhewei Wei, Wenbing Huang, Chongxuan Li:
HierAffinity: Predicting Protein-Ligand Binding Affinity With Hierarchical Modeling. DASFAA (7) 2024: 37-52 - [c47]Yang Zhang, Zhewei Wei, Wenbing Huang, Chongxuan Li:
TransPocket: Structural and Geometric Transformer for Ligand Binding Site Detection. DASFAA (7) 2024: 188-198 - [c46]Zhengyi Wang, Yikai Wang, Yifei Chen, Chendong Xiang, Shuo Chen, Dajiang Yu, Chongxuan Li, Hang Su, Jun Zhu:
CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model. ECCV (31) 2024: 57-74 - [c45]Yong Zhong, Min Zhao, Zebin You, Xiaofeng Yu, Changwang Zhang, Chongxuan Li:
PoseCrafter: One-Shot Personalized Video Synthesis Following Flexible Pose Control. ECCV (44) 2024: 243-260 - [c44]Juntu Zhao, Junyu Deng, Yixin Ye, Chongxuan Li
Lost in Translation: Latent Concept Misalignment in Text-to-Image Diffusion Models. ECCV (69) 2024: 318-333 - [c43]Guoxing Yang, Haoyu Lu, Chongxuan Li, Guang Zhou, Haoran Wu, Zhiwu Lu:
Progressive Image Synthesis from Semantics to Details with Denoising Diffusion GAN. ICASSP 2024: 7495-7499 - [c42]Siqi Kou, Lei Gan, Dequan Wang, Chongxuan Li, Zhijie Deng:
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference. ICLR 2024 - [c41]Shen Nie, Hanzhong Allan Guo, Cheng Lu, Yuhao Zhou, Chenyu Zheng, Chongxuan Li:
The Blessing of Randomness: SDE Beats ODE in General Diffusion-based Image Editing. ICLR 2024 - [c40]Yang Zhang, Zhewei Wei, Ye Yuan, Chongxuan Li, Wenbing Huang:
EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction. ICML 2024 - [c39]Kaiwen Xue, Yuhao Zhou, Shen Nie, Xu Min, Xiaolu Zhang, Jun Zhou, Chongxuan Li:
Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations. ICML 2024 - [i63]Yijing Liu, Chao Du, Tianyu Pang
Graph Diffusion Policy Optimization. CoRR abs/2402.16302 (2024) - [i62]Zhengyi Wang, Yikai Wang, Yifei Chen, Chendong Xiang, Shuo Chen, Dajiang Yu, Chongxuan Li, Hang Su, Jun Zhu:
CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model. CoRR abs/2403.05034 (2024) - [i61]Kaiwen Xue, Yuhao Zhou, Shen Nie, Xu Min, Xiaolu Zhang, Jun Zhou, Chongxuan Li:
Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations. CoRR abs/2404.15766 (2024) - [i60]Luxi Chen, Zhengyi Wang, Chongxuan Li, Tingting Gao, Hang Su, Jun Zhu:
MicroDreamer: Zero-shot 3D Generation in ~20 Seconds by Score-based Iterative Reconstruction. CoRR abs/2404.19525 (2024) - [i59]Yong Zhong, Min Zhao, Zebin You, Xiaofeng Yu, Changwang Zhang, Chongxuan Li:
PoseCrafter: One-Shot Personalized Video Synthesis Following Flexible Pose Control. CoRR abs/2405.14582 (2024) - [i58]Chenyu Zheng, Wei Huang, Rongzhen Wang, Guoqiang Wu, Jun Zhu, Chongxuan Li:
On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability. CoRR abs/2405.16845 (2024) - [i57]Zebin You, Xinyu Zhang, Hanzhong Guo, Jingdong Wang, Chongxuan Li:
Are Image Distributions Indistinguishable to Humans Indistinguishable to Classifiers? CoRR abs/2405.18029 (2024) - [i56]Jingyang Ou, Shen Nie, Kaiwen Xue, Fengqi Zhu, Jiacheng Sun, Zhenguo Li, Chongxuan Li:
Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data. CoRR abs/2406.03736 (2024) - [i55]Min Zhao, Hongzhou Zhu, Chendong Xiang, Kaiwen Zheng, Chongxuan Li, Jun Zhu:
Identifying and Solving Conditional Image Leakage in Image-to-Video Diffusion Model. CoRR abs/2406.15735 (2024) - [i54]Juntu Zhao, Junyu Deng, Yixin Ye, Chongxuan Li, Zhijie Deng, Dequan Wang:
Lost in Translation: Latent Concept Misalignment in Text-to-Image Diffusion Models. CoRR abs/2408.00230 (2024) - [i53]Luping Liu, Chao Du, Tianyu Pang, Zehan Wang, Chongxuan Li, Dong Xu:
Improving Long-Text Alignment for Text-to-Image Diffusion Models. CoRR abs/2410.11817 (2024) - [i52]Shen Nie, Fengqi Zhu, Chao Du, Tianyu Pang, Qian Liu, Guangtao Zeng, Min Lin, Chongxuan Li:
Scaling up Masked Diffusion Models on Text. CoRR abs/2410.18514 (2024) - 2023
- [j5]Yudeng Lin, Qingtian Zhang, Bin Gao
Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning. Nat. Mac. Intell. 5(7): 714-723 (2023) - [c38]Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, Jun Zhu:
All are Worth Words: A ViT Backbone for Diffusion Models. CVPR 2023: 22669-22679 - [c37]Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu:
Equivariant Energy-Guided SDE for Inverse Molecular Design. ICLR 2023 - [c36]Yong Zhong, Hongtao Liu, Xiaodong Liu, Fan Bao, Weiran Shen, Chongxuan Li:
Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models. ICLR 2023 - [c35]Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu:
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale. ICML 2023: 1692-1717 - [c34]Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, Jun Zhu:
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning. ICML 2023: 22825-22855 - [c33]Guoqiang Wu, Chongxuan Li, Yilong Yin:
Towards Understanding Generalization of Macro-AUC in Multi-label Learning. ICML 2023: 37540-37570 - [c32]Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li, Jun Zhu:
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications. ICML 2023: 42420-42477 - [c31]Hanzhong Guo, Cheng Lu, Fan Bao, Tianyu Pang, Shuicheng Yan, Chao Du, Chongxuan Li:
Gaussian Mixture Solvers for Diffusion Models. NeurIPS 2023 - [c30]Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu:
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation. NeurIPS 2023 - [c29]Zebin You, Yong Zhong, Fan Bao, Jiacheng Sun, Chongxuan Li, Jun Zhu:
Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels. NeurIPS 2023 - [c28]Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Chongxuan Li, Ngai-Man Cheung, Min Lin:
On Evaluating Adversarial Robustness of Large Vision-Language Models. NeurIPS 2023 - [c27]Chenyu Zheng, Guoqiang Wu, Chongxuan Li:
Toward Understanding Generative Data Augmentation. NeurIPS 2023 - [i51]Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li, Jun Zhu:
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications. CoRR abs/2302.02334 (2023) - [i50]Zebin You, Yong Zhong, Fan Bao, Jiacheng Sun, Chongxuan Li, Jun Zhu:
Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels. CoRR abs/2302.10586 (2023) - [i49]Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu:
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale. CoRR abs/2303.06555 (2023) - [i48]Chendong Xiang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu:
A Closer Look at Parameter-Efficient Tuning in Diffusion Models. CoRR abs/2303.18181 (2023) - [i47]Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, Jun Zhu:
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning. CoRR abs/2304.12824 (2023) - [i46]Guoqiang Wu, Chongxuan Li, Yilong Yin:
Towards Understanding Generalization of Macro-AUC in Multi-label Learning. CoRR abs/2305.05248 (2023) - [i45]Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu:
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation. CoRR abs/2305.16213 (2023) - [i44]Yunqing Zhao, Tianyu Pang
On Evaluating Adversarial Robustness of Large Vision-Language Models. CoRR abs/2305.16934 (2023) - [i43]Min Zhao, Rongzhen Wang, Fan Bao, Chongxuan Li, Jun Zhu:
ControlVideo: Adding Conditional Control for One Shot Text-to-Video Editing. CoRR abs/2305.17098 (2023) - [i42]Chenyu Zheng, Guoqiang Wu, Chongxuan Li:
Toward Understanding Generative Data Augmentation. CoRR abs/2305.17476 (2023) - [i41]Yidong Ouyang, Liyan Xie, Chongxuan Li, Guang Cheng:
MissDiff: Training Diffusion Models on Tabular Data with Missing Values. CoRR abs/2307.00467 (2023) - [i40]Ximing Xing, Chuang Wang, Haitao Zhou, Zhihao Hu, Chongxuan Li, Dong Xu, Qian Yu:
Inversion-by-Inversion: Exemplar-based Sketch-to-Photo Synthesis via Stochastic Differential Equations without Training. CoRR abs/2308.07665 (2023) - [i39]Xiangming Gu, Chao Du, Tianyu Pang
On Memorization in Diffusion Models. CoRR abs/2310.02664 (2023) - [i38]Siqi Kou, Lei Gan
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference. CoRR abs/2310.11142 (2023) - [i37]Hanzhong Guo, Cheng Lu, Fan Bao, Tianyu Pang
Gaussian Mixture Solvers for Diffusion Models. CoRR abs/2311.00941 (2023) - [i36]Shen Nie, Hanzhong Allan Guo, Cheng Lu, Yuhao Zhou, Chenyu Zheng, Chongxuan Li:
The Blessing of Randomness: SDE Beats ODE in General Diffusion-based Image Editing. CoRR abs/2311.01410 (2023) - 2022
- [j4]Chongxuan Li
Triple Generative Adversarial Networks. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9629-9640 (2022) - [j3]Dong Yan, Jiayi Weng, Shiyu Huang, Chongxuan Li, Yichi Zhou, Hang Su, Jun Zhu:
Deep reinforcement learning with credit assignment for combinatorial optimization. Pattern Recognit. 124: 108466 (2022) - [c26]Fan Bao, Chongxuan Li, Jun Zhu, Bo Zhang:
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models. ICLR 2022 - [c25]Liyuan Wang, Xingxing Zhang, Kuo Yang, Longhui Yu, Chongxuan Li, Lanqing Hong, Shifeng Zhang, Zhenguo Li, Yi Zhong, Jun Zhu:
Memory Replay with Data Compression for Continual Learning. ICLR 2022 - [c24]Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu, Bo Zhang:
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models. ICML 2022: 1555-1584 - [c23]Cheng Lu, Kaiwen Zheng, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu:
Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching. ICML 2022: 14429-14460 - [c22]Siyu Wang, Jianfei Chen, Chongxuan Li, Jun Zhu, Bo Zhang:
Fast Lossless Neural Compression with Integer-Only Discrete Flows. ICML 2022: 22562-22575 - [c21]Min Zhao, Fan Bao, Chongxuan Li, Jun Zhu:
EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations. NeurIPS 2022 - [c20]Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu:
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps. NeurIPS 2022 - [i35]Fan Bao, Chongxuan Li, Jun Zhu, Bo Zhang:
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models. CoRR abs/2201.06503 (2022) - [i34]Liyuan Wang, Xingxing Zhang, Kuo Yang, Longhui Yu, Chongxuan Li, Lanqing Hong, Shifeng Zhang, Zhenguo Li, Yi Zhong, Jun Zhu:
Memory Replay with Data Compression for Continual Learning. CoRR abs/2202.06592 (2022) - [i33]Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu:
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps. CoRR abs/2206.00927 (2022) - [i32]Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu, Bo Zhang:
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models. CoRR abs/2206.07309 (2022) - [i31]Cheng Lu, Kaiwen Zheng, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu:
Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score Matching. CoRR abs/2206.08265 (2022) - [i30]Siyu Wang, Jianfei Chen, Chongxuan Li, Jun Zhu, Bo Zhang:
Fast Lossless Neural Compression with Integer-Only Discrete Flows. CoRR abs/2206.08869 (2022) - [i29]Min Zhao, Fan Bao, Chongxuan Li, Jun Zhu:
EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations. CoRR abs/2207.06635 (2022) - [i28]Yong Zhong, Hongtao Liu, Xiaodong Liu, Fan Bao, Weiran Shen, Chongxuan Li:
Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models. CoRR abs/2208.14133 (2022) - [i27]Fan Bao, Chongxuan Li, Yue Cao, Jun Zhu:
All are Worth Words: a ViT Backbone for Score-based Diffusion Models. CoRR abs/2209.12152 (2022) - [i26]Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu:
Equivariant Energy-Guided SDE for Inverse Molecular Design. CoRR abs/2209.15408 (2022) - [i25]Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu:
DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models. CoRR abs/2211.01095 (2022) - [i24]Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu:
Why Are Conditional Generative Models Better Than Unconditional Ones? CoRR abs/2212.00362 (2022) - 2021
- [c19]Liyuan Wang, Kuo Yang, Chongxuan Li, Lanqing Hong, Zhenguo Li, Jun Zhu:
ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-Supervised Continual Learning. CVPR 2021: 5383-5392 - [c18]Cheng Lu, Jianfei Chen, Chongxuan Li, Qiuhao Wang, Jun Zhu:
Implicit Normalizing Flows. ICLR 2021 - [c17]Tsung Wei Tsai, Chongxuan Li, Jun Zhu:
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering. ICLR 2021 - [c16]Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu, Bo Zhang:
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models. ICML 2021: 651-661 - [c15]Fan Bao, Guoqiang Wu, Chongxuan Li, Jun Zhu, Bo Zhang:
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization. NeurIPS 2021: 4529-4541 - [c14]Guoqiang Wu, Chongxuan Li, Kun Xu, Jun Zhu:
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization. NeurIPS 2021: 14332-14344 - [i23]Liyuan Wang, Kuo Yang, Chongxuan Li, Lanqing Hong, Zhenguo Li, Jun Zhu:
ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning. CoRR abs/2101.00407 (2021) - [i22]Qijun Luo, Zhili Liu, Lanqing Hong, Chongxuan Li, Kuo Yang, Liyuan Wang, Fengwei Zhou, Guilin Li, Zhenguo Li, Jun Zhu:
Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation. CoRR abs/2101.01400 (2021) - [i21]Cheng Lu, Jianfei Chen, Chongxuan Li, Qiuhao Wang, Jun Zhu:
Implicit Normalizing Flows. CoRR abs/2103.09527 (2021) - [i20]Tsung Wei Tsai, Chongxuan Li, Jun Zhu:
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering. CoRR abs/2105.01899 (2021) - [i19]Guoqiang Wu, Chongxuan Li, Kun Xu, Jun Zhu:
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization. CoRR abs/2105.05026 (2021) - [i18]Fan Bao, Guoqiang Wu, Chongxuan Li, Jun Zhu, Bo Zhang:
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization. CoRR abs/2106.04188 (2021) - 2020
- [c13]Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, Bo Zhang:
To Relieve Your Headache of Training an MRF, Take AdVIL. ICLR 2020 - [c12]Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang:
Understanding and Stabilizing GANs' Training Dynamics Using Control Theory. ICML 2020: 10566-10575 - [c11]Fan Bao, Chongxuan Li, Taufik Xu, Hang Su, Jun Zhu, Bo Zhang:
Bi-level Score Matching for Learning Energy-based Latent Variable Models. NeurIPS 2020 - [c10]Tianyu Pang, Taufik Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu:
Efficient Learning of Generative Models via Finite-Difference Score Matching. NeurIPS 2020 - [c9]Kun Xu, Chao Du, Chongxuan Li, Jun Zhu, Bo Zhang:
Learning Implicit Generative Models by Teaching Density Estimators. ECML/PKDD (2) 2020: 239-255 - [i17]Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu:
Efficient Learning of Generative Models via Finite-Difference Score Matching. CoRR abs/2007.03317 (2020) - [i16]Fan Bao, Chongxuan Li, Kun Xu, Hang Su, Jun Zhu, Bo Zhang:
Bi-level Score Matching for Learning Energy-based Latent Variable Models. CoRR abs/2010.07856 (2020) - [i15]Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu, Bo Zhang:
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models. CoRR abs/2010.08258 (2020)
2010 – 2019
- 2019
- [c8]Taufik Xu, Chongxuan Li, Jun Zhu, Bo Zhang:
Multi-objects Generation with Amortized Structural Regularization. NeurIPS 2019: 6615-6625 - [i14]Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, Bo Zhang:
Adversarial Variational Inference and Learning in Markov Random Fields. CoRR abs/1901.08400 (2019) - [i13]Tsung Wei Tsai, Chongxuan Li, Jun Zhu:
DS3L: Deep Self-Semi-Supervised Learning for Image Recognition. CoRR abs/1905.13305 (2019) - [i12]Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang:
Multi-objects Generation with Amortized Structural Regularization. CoRR abs/1906.03923 (2019) - [i11]Kun Xu, Chongxuan Li, Huanshu Wei, Jun Zhu, Bo Zhang:
Understanding and Stabilizing GANs' Training Dynamics with Control Theory. CoRR abs/1909.13188 (2019) - [i10]Chongxuan Li, Kun Xu, Jiashuo Liu, Jun Zhu, Bo Zhang:
Triple Generative Adversarial Networks. CoRR abs/1912.09784 (2019) - 2018
- [j2]Chongxuan Li
Max-Margin Deep Generative Models for (Semi-)Supervised Learning. IEEE Trans. Pattern Anal. Mach. Intell. 40(11): 2762-2775 (2018) - [c7]Chao Du, Chongxuan Li, Yin Zheng, Jun Zhu, Bo Zhang:
Collaborative Filtering With User-Item Co-Autoregressive Models. AAAI 2018: 2175-2182 - [c6]Danyang Sun, Tongzheng Ren, Chongxuan Li, Hang Su, Jun Zhu:
Learning to Write Stylized Chinese Characters by Reading a Handful of Examples. IJCAI 2018: 920-927 - [c5]Chongxuan Li, Max Welling, Jun Zhu, Bo Zhang:
Graphical Generative Adversarial Networks. NeurIPS 2018: 6072-6083 - [i9]Chongxuan Li, Max Welling, Jun Zhu, Bo Zhang:
Graphical Generative Adversarial Networks. CoRR abs/1804.03429 (2018) - [i8]Chao Du, Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang:
Learning Implicit Generative Models by Teaching Explicit Ones. CoRR abs/1807.03870 (2018) - 2017
- [j1]Mengchen Liu, Jiaxin Shi, Zhen Li
Towards Better Analysis of Deep Convolutional Neural Networks. IEEE Trans. Vis. Comput. Graph. 23(1): 91-100 (2017) - [c4]Chongxuan Li, Taufik Xu, Jun Zhu, Bo Zhang:
Triple Generative Adversarial Nets. NIPS 2017: 4088-4098 - [c3]Jianfei Chen, Chongxuan Li, Yizhong Ru, Jun Zhu:
Population Matching Discrepancy and Applications in Deep Learning. NIPS 2017: 6262-6272 - [i7]Chongxuan Li, Kun Xu, Jun Zhu, Bo Zhang:
Triple Generative Adversarial Nets. CoRR abs/1703.02291 (2017) - [i6]Danyang Sun, Tongzheng Ren, Chongxuan Li, Jun Zhu, Hang Su:
Learning to Write Stylized Chinese Characters by Reading a Handful of Examples. CoRR abs/1712.06424 (2017) - 2016
- [c2]Chongxuan Li, Jun Zhu, Bo Zhang:
Learning to Generate with Memory. ICML 2016: 1177-1186 - [i5]Chongxuan Li, Jun Zhu, Bo Zhang:
Learning to Generate with Memory. CoRR abs/1602.07416 (2016) - [i4]Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, Shixia Liu:
Towards Better Analysis of Deep Convolutional Neural Networks. CoRR abs/1604.07043 (2016) - [i3]Chongxuan Li, Jun Zhu, Bo Zhang:
Max-Margin Deep Generative Models for (Semi-)Supervised Learning. CoRR abs/1611.07119 (2016) - [i2]Chao Du, Chongxuan Li, Yin Zheng, Jun Zhu, Cailiang Liu, Hanning Zhou, Bo Zhang:
Collaborative Filtering with User-Item Co-Autoregressive Models. CoRR abs/1612.07146 (2016) - 2015
- [c1]Chongxuan Li, Jun Zhu, Tianlin Shi, Bo Zhang:
Max-Margin Deep Generative Models. NIPS 2015: 1837-1845 - [i1]Chongxuan Li, Jun Zhu, Tianlin Shi, Bo Zhang:
Max-margin Deep Generative Models. CoRR abs/1504.06787 (2015)
Coauthor Index
[j6] [c46] [i62] [i60] [i55] [j5] [c38] [c37] [c35] [c34] [c30] [i51] [i50] [i49] [i48] [i47] [i45] [i43] [j4] [j3] [c26] [c25] [c24] [c23] [c22] [c21] [c20] [i35] [i34] [i33] [i32] [i31] [i30] [i29] [i27] [i26] [i25] [i24] [c19] [c18] [c17] [c16] [c15] [c14] [i23] [i22] [i21] [i20] [i19] [i18] [c13] [c12] [c11] [c10] [c9] [i17] [i16] [i15] [c8] [i14] [i13] [i12] [i11] [i10] [j2] [c7] [c6] [c5] [i9] [i8] [j1] [c4] [c3] [i7] [i6] [c2] [i5] [i4] [i3] [i2] [c1] [i1]
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