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Yixuan Li 0001
Sharon Li 0001
Person information
- affiliation: University of Wisconsin-Madison, WI, USA
- affiliation (2019 - 2020): Stanford University, CA, USA
- affiliation (2017 - 2019): Facebook AI, Menlo Park, CA, USA
- affiliation (PhD 2017): Cornell University, Ithaca, NY, USA
Other persons with the same name
- Yixuan Li — disambiguation page
- Yixuan Li 0002
— Chinese University of Hong Kong, Department of Information Engineering, Hong Kong (and 1 more)
- Sharon Li — disambiguation page
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2020 – today
- 2024
- [j9]Yifei Ming, Yixuan Li
How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models? Int. J. Comput. Vis. 132(2): 596-609 (2024) - [j8]Soumya Suvra Ghosal
Are Vision Transformers Robust to Spurious Correlations? Int. J. Comput. Vis. 132(3): 689-709 (2024) - [j7]Jingkang Yang, Kaiyang Zhou, Yixuan Li, Ziwei Liu
Generalized Out-of-Distribution Detection: A Survey. Int. J. Comput. Vis. 132(12): 5635-5662 (2024) - [j6]Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu:
On the Learnability of Out-of-distribution Detection. J. Mach. Learn. Res. 25: 84:1-84:83 (2024) - [j5]Haobo Wang
PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 3183-3198 (2024) - [c51]Soumya Suvra Ghosal, Yiyou Sun, Yixuan Li:
How to Overcome Curse-of-Dimensionality for Out-of-Distribution Detection? AAAI 2024: 19849-19857 - [c50]Ruixuan Xiao, Lei Feng, Kai Tang, Junbo Zhao, Yixuan Li, Gang Chen, Haobo Wang:
Targeted Representation Alignment for Open-World Semi-Supervised Learning. CVPR 2024: 23072-23082 - [c49]Haoyue Bai, Yifei Ming, Julian Katz-Samuels, Yixuan Li:
HYPO: Hyperspherical Out-Of-Distribution Generalization. ICLR 2024 - [c48]Xuefeng Du, Zhen Fang, Ilias Diakonikolas, Yixuan Li:
How Does Unlabeled Data Provably Help Out-of-Distribution Detection? ICLR 2024 - [c47]Bo Peng, Yadan Luo, Yonggang Zhang, Yixuan Li, Zhen Fang:
ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection. ICLR 2024 - [c46]Xuefeng Du, Yiyou Sun, Yixuan Li:
When and How Does In-Distribution Label Help Out-of-Distribution Detection? ICML 2024 - [c45]Yifei Ming, Yixuan Li:
Understanding Retrieval-Augmented Task Adaptation for Vision-Language Models. ICML 2024 - [c44]Wei Jin
DCAI: Data-centric Artificial Intelligence. WWW (Companion Volume) 2024: 1482-1485 - [i58]Xuefeng Du, Zhen Fang, Ilias Diakonikolas, Yixuan Li:
How Does Unlabeled Data Provably Help Out-of-Distribution Detection? CoRR abs/2402.03502 (2024) - [i57]Yifei Ming, Haoyue Bai, Julian Katz-Samuels, Yixuan Li:
HYPO: Hyperspherical Out-of-Distribution Generalization. CoRR abs/2402.07785 (2024) - [i56]Bo Peng, Yadan Luo, Yonggang Zhang, Yixuan Li, Zhen Fang:
ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection. CoRR abs/2402.17888 (2024) - [i55]Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang, Yifei Ming, Qing Yu, Go Irie, Yixuan Li, Hai Li, Ziwei Liu, Kiyoharu Aizawa:
Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models. CoRR abs/2403.20331 (2024) - [i54]Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu:
On the Learnability of Out-of-distribution Detection. CoRR abs/2404.04865 (2024) - [i53]Yifei Ming, Yixuan Li:
Understanding Retrieval-Augmented Task Adaptation for Vision-Language Models. CoRR abs/2405.01468 (2024) - [i52]Xuefeng Du, Yiyou Sun, Yixuan Li:
When and How Does In-Distribution Label Help Out-of-Distribution Detection? CoRR abs/2405.18635 (2024) - [i51]Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang, Yifei Ming, Yueqian Lin, Qing Yu, Go Irie, Shafiq Joty, Yixuan Li, Hai Li, Ziwei Liu, Toshihiko Yamasaki, Kiyoharu Aizawa:
Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey. CoRR abs/2407.21794 (2024) - [i50]Haoyue Bai, Xuefeng Du, Katie Rainey, Shibin Parameswaran, Yixuan Li:
Out-of-Distribution Learning with Human Feedback. CoRR abs/2408.07772 (2024) - [i49]Leitian Tao, Yixuan Li:
Your Weak LLM is Secretly a Strong Teacher for Alignment. CoRR abs/2409.08813 (2024) - [i48]Xuefeng Du, Chaowei Xiao, Yixuan Li:
HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detection. CoRR abs/2409.17504 (2024) - [i47]Xuefeng Du, Reshmi Ghosh, Robert Sim, Ahmed Salem, Vitor Carvalho, Emily Lawton, Yixuan Li, Jack W. Stokes:
VLMGuard: Defending VLMs against Malicious Prompts via Unlabeled Data. CoRR abs/2410.00296 (2024) - [i46]Min-Hsuan Yeh, Leitian Tao, Jeffrey Wang, Xuefeng Du, Yixuan Li:
How Reliable Is Human Feedback For Aligning Large Language Models? CoRR abs/2410.01957 (2024) - 2023
- [j4]Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Yixuan Li, Junzhou Huang:
Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions. Trans. Mach. Learn. Res. 2023 (2023) - [j3]Yiyou Sun, Yixuan Li:
OpenCon: Open-world Contrastive Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c43]Soumya Suvra Ghosal, Yixuan Li:
Distributionally Robust Optimization with Probabilistic Group. AAAI 2023: 11809-11817 - [c42]Yiyou Sun, Yaojie Liu, Xiaoming Liu, Yixuan Li, Wen-Sheng Chu:
Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment. CVPR 2023: 24563-24574 - [c41]Jiuxiang Gu, Yifei Ming, Yi Zhou, Jason Kuen, Vlad I. Morariu, Handong Zhao, Ruiyi Zhang, Nikolaos Barmpalios, Anqi Liu, Yixuan Li, Tong Sun, Ani Nenkova:
A Critical Analysis of Document Out-of-Distribution Detection. EMNLP (Findings) 2023: 4973-4999 - [c40]Yifei Ming, Yiyou Sun, Ousmane Dia, Yixuan Li:
How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection? ICLR 2023 - [c39]Leitian Tao, Xuefeng Du, Jerry Zhu, Yixuan Li:
Non-parametric Outlier Synthesis. ICLR 2023 - [c38]Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D. Nowak, Yixuan Li:
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection. ICML 2023: 1454-1471 - [c37]Yiyou Sun, Zhenmei Shi, Yingyu Liang, Yixuan Li:
When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis. ICML 2023: 33014-33043 - [c36]Hongxin Wei, Huiping Zhuang, Renchunzi Xie, Lei Feng, Gang Niu, Bo An, Yixuan Li:
Mitigating Memorization of Noisy Labels by Clipping the Model Prediction. ICML 2023: 36868-36886 - [c35]Xuefeng Du, Yiyou Sun, Jerry Zhu, Yixuan Li:
Dream the Impossible: Outlier Imagination with Diffusion Models. NeurIPS 2023 - [c34]Yiyou Sun, Zhenmei Shi, Yixuan Li:
A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning. NeurIPS 2023 - [c33]Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han:
Learning to Augment Distributions for Out-of-distribution Detection. NeurIPS 2023 - [c32]Tingru Cui, Sharon Li, Kaiping Chen, James Bailey, Feng Liu:
Designing Fair AI Systems: Exploring the Interaction of Explainable AI and Task Objectivity on Users' Fairness Perception. PACIS 2023: 161 - [i45]Leitian Tao, Xuefeng Du, Xiaojin Zhu, Yixuan Li:
Non-Parametric Outlier Synthesis. CoRR abs/2303.02966 (2023) - [i44]Soumya Suvra Ghosal, Yixuan Li:
Distributionally Robust Optimization with Probabilistic Group. CoRR abs/2303.05809 (2023) - [i43]Yiyou Sun, Yaojie Liu, Xiaoming Liu, Yixuan Li, Wen-Sheng Chu:
Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment. CoRR abs/2303.13662 (2023) - [i42]Yifei Ming, Yixuan Li:
How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models? CoRR abs/2306.06048 (2023) - [i41]Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D. Nowak, Yixuan Li:
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection. CoRR abs/2306.09158 (2023) - [i40]Jingyang Zhang, Jingkang Yang, Pengyun Wang, Haoqi Wang, Yueqian Lin, Haoran Zhang, Yiyou Sun, Xuefeng Du, Kaiyang Zhou, Wayne Zhang
OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection. CoRR abs/2306.09301 (2023) - [i39]Yiyou Sun, Zhenmei Shi, Yingyu Liang, Yixuan Li:
When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis. CoRR abs/2308.05017 (2023) - [i38]Xuefeng Du, Yiyou Sun, Xiaojin Zhu, Yixuan Li:
Dream the Impossible: Outlier Imagination with Diffusion Models. CoRR abs/2309.13415 (2023) - [i37]Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han:
Learning to Augment Distributions for Out-of-Distribution Detection. CoRR abs/2311.01796 (2023) - [i36]Yiyou Sun, Zhenmei Shi, Yixuan Li:
A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning. CoRR abs/2311.03524 (2023) - [i35]Soumya Suvra Ghosal, Yiyou Sun, Yixuan Li:
How to Overcome Curse-of-Dimensionality for Out-of-Distribution Detection? CoRR abs/2312.14452 (2023) - 2022
- [j2]Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li, Mohammad Hossein Rohban, Mohammad Sabokrou:
A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges. Trans. Mach. Learn. Res. 2022 (2022) - [c31]Yifei Ming, Hang Yin, Yixuan Li:
On the Impact of Spurious Correlation for Out-of-Distribution Detection. AAAI 2022: 10051-10059 - [c30]Xuefeng Du, Xin Wang, Gabriel Gozum, Yixuan Li:
Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild. CVPR 2022: 13668-13678 - [c29]Yiyou Sun
DICE: Leveraging Sparsification for Out-of-Distribution Detection. ECCV (24) 2022: 691-708 - [c28]Xuefeng Du, Zhaoning Wang, Mu Cai
VOS: Learning What You Don't Know by Virtual Outlier Synthesis. ICLR 2022 - [c27]Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao:
PiCO: Contrastive Label Disambiguation for Partial Label Learning. ICLR 2022 - [c26]Julian Katz-Samuels, Julia B. Nakhleh, Robert D. Nowak, Yixuan Li:
Training OOD Detectors in their Natural Habitats. ICML 2022: 10848-10865 - [c25]Yifei Ming, Ying Fan
POEM: Out-of-Distribution Detection with Posterior Sampling. ICML 2022: 15650-15665 - [c24]Yiyou Sun, Yifei Ming, Xiaojin Zhu, Yixuan Li:
Out-of-Distribution Detection with Deep Nearest Neighbors. ICML 2022: 20827-20840 - [c23]Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, Yixuan Li:
Mitigating Neural Network Overconfidence with Logit Normalization. ICML 2022: 23631-23644 - [c22]Xuefeng Du, Gabriel Gozum, Yifei Ming, Yixuan Li:
SIREN: Shaping Representations for Detecting Out-of-Distribution Objects. NeurIPS 2022 - [c21]Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu:
Is Out-of-Distribution Detection Learnable? NeurIPS 2022 - [c20]Yifei Ming, Ziyang Cai, Jiuxiang Gu, Yiyou Sun, Wei Li, Yixuan Li:
Delving into Out-of-Distribution Detection with Vision-Language Representations. NeurIPS 2022 - [c19]Haobo Wang, Mingxuan Xia, Yixuan Li, Yuren Mao, Lei Feng, Gang Chen, Junbo Zhao:
SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning. NeurIPS 2022 - [c18]Jingkang Yang, Pengyun Wang, Dejian Zou, Zitang Zhou, Kunyuan Ding, Wenxuan Peng, Haoqi Wang, Guangyao Chen, Bo Li, Yiyou Sun, Xuefeng Du, Kaiyang Zhou, Wayne Zhang, Dan Hendrycks, Yixuan Li, Ziwei Liu:
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection. NeurIPS 2022 - [i34]Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao:
PiCO: Contrastive Label Disambiguation for Partial Label Learning. CoRR abs/2201.08984 (2022) - [i33]Xuefeng Du, Zhaoning Wang, Mu Cai, Yixuan Li:
VOS: Learning What You Don't Know by Virtual Outlier Synthesis. CoRR abs/2202.01197 (2022) - [i32]Julian Katz-Samuels, Julia B. Nakhleh, Robert Nowak, Yixuan Li:
Training OOD Detectors in their Natural Habitats. CoRR abs/2202.03299 (2022) - [i31]Xuefeng Du, Xin Wang, Gabriel Gozum, Yixuan Li:
Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild. CoRR abs/2203.03800 (2022) - [i30]Yifei Ming, Yiyou Sun, Ousmane Dia, Yixuan Li:
CIDER: Exploiting Hyperspherical Embeddings for Out-of-Distribution Detection. CoRR abs/2203.04450 (2022) - [i29]Soumya Suvra Ghosal, Yifei Ming, Yixuan Li:
Are Vision Transformers Robust to Spurious Correlations? CoRR abs/2203.09125 (2022) - [i28]Yiyou Sun, Yifei Ming, Xiaojin Zhu, Yixuan Li:
Out-of-distribution Detection with Deep Nearest Neighbors. CoRR abs/2204.06507 (2022) - [i27]Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng
Mitigating Neural Network Overconfidence with Logit Normalization. CoRR abs/2205.09310 (2022) - [i26]Yifei Ming, Ying Fan, Yixuan Li:
POEM: Out-of-Distribution Detection with Posterior Sampling. CoRR abs/2206.13687 (2022) - [i25]Yiyou Sun, Yixuan Li:
Open-world Contrastive Learning. CoRR abs/2208.02764 (2022) - [i24]Haobo Wang, Mingxuan Xia, Yixuan Li, Yuren Mao, Lei Feng
SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning. CoRR abs/2209.10365 (2022) - [i23]Jingkang Yang, Pengyun Wang, Dejian Zou, Zitang Zhou, Kunyuan Ding, Wenxuan Peng, Haoqi Wang
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection. CoRR abs/2210.07242 (2022) - [i22]Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu:
Is Out-of-Distribution Detection Learnable? CoRR abs/2210.14707 (2022) - [i21]Yifei Ming, Ziyang Cai, Jiuxiang Gu, Yiyou Sun, Wei Li, Yixuan Li:
Delving into Out-of-Distribution Detection with Vision-Language Representations. CoRR abs/2211.13445 (2022) - [i20]Hongxin Wei, Huiping Zhuang, Renchunzi Xie, Lei Feng, Gang Niu, Bo An, Yixuan Li:
Logit Clipping for Robust Learning against Label Noise. CoRR abs/2212.04055 (2022) - 2021
- [c17]Mu Cai, Hong Zhang, Huijuan Huang, Qichuan Geng, Yixuan Li, Gao Huang:
Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving. ICCV 2021: 13910-13920 - [c16]Yiyou Sun, Chuan Guo, Yixuan Li:
ReAct: Out-of-distribution Detection With Rectified Activations. NeurIPS 2021: 144-157 - [c15]Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha:
ATOM: Robustifying Out-of-Distribution Detection Using Outlier Mining. ECML/PKDD (3) 2021: 430-445 - [i19]Yifei Ming, Hang Yin, Yixuan Li:
On the Impact of Spurious Correlation for Out-of-distribution Detection. CoRR abs/2109.05642 (2021) - [i18]Jingkang Yang, Kaiyang Zhou, Yixuan Li, Ziwei Liu:
Generalized Out-of-Distribution Detection: A Survey. CoRR abs/2110.11334 (2021) - [i17]Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li, Mohammad Hossein Rohban, Mohammad Sabokrou:
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges. CoRR abs/2110.14051 (2021) - [i16]Yiyou Sun, Yixuan Li:
On the Effectiveness of Sparsification for Detecting the Deep Unknowns. CoRR abs/2111.09805 (2021) - [i15]Yiyou Sun, Chuan Guo, Yixuan Li:
ReAct: Out-of-distribution Detection With Rectified Activations. CoRR abs/2111.12797 (2021) - 2020
- [i14]Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha:
Robust Out-of-distribution Detection in Neural Networks. CoRR abs/2003.09711 (2020) - [i13]Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha:
Robust Out-of-distribution Detection via Informative Outlier Mining. CoRR abs/2006.15207 (2020)
2010 – 2019
- 2019
- [c14]Abhimanyu Dubey, Laurens van der Maaten, Zeki Yalniz, Yixuan Li, Dhruv Mahajan:
Defense Against Adversarial Images Using Web-Scale Nearest-Neighbor Search. CVPR 2019: 8767-8776 - [i12]Abhimanyu Dubey, Laurens van der Maaten, Zeki Yalniz, Yixuan Li, Dhruv Mahajan:
Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search. CoRR abs/1903.01612 (2019) - 2018
- [j1]Yixuan Li, Kun He, Kyle Kloster, David Bindel, John E. Hopcroft:
Local Spectral Clustering for Overlapping Community Detection. ACM Trans. Knowl. Discov. Data 12(2): 17:1-17:27 (2018) - [c13]Dhruv Mahajan, Ross B. Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, Laurens van der Maaten:
Exploring the Limits of Weakly Supervised Pretraining. ECCV (2) 2018: 185-201 - [c12]Shiyu Liang, Yixuan Li, R. Srikant:
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks. ICLR (Poster) 2018 - [c11]Shiyu Liang, Ruoyu Sun, Yixuan Li, R. Srikant:
Understanding the Loss Surface of Single-Layered Neural Networks for Binary Classification. ICLR (Workshop) 2018 - [c10]Shiyu Liang, Ruoyu Sun, Yixuan Li, Rayadurgam Srikant:
Understanding the Loss Surface of Neural Networks for Binary Classification. ICML 2018: 2840-2849 - [i11]Shiyu Liang, Ruoyu Sun, Yixuan Li, R. Srikant:
Understanding the Loss Surface of Neural Networks for Binary Classification. CoRR abs/1803.00909 (2018) - [i10]Dhruv Mahajan, Ross B. Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, Laurens van der Maaten:
Exploring the Limits of Weakly Supervised Pretraining. CoRR abs/1805.00932 (2018) - 2017
- [c9]Xun Huang, Yixuan Li, Omid Poursaeed
Stacked Generative Adversarial Networks. CVPR 2017: 1866-1875 - [c8]Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger:
Snapshot Ensembles: Train 1, Get M for Free. ICLR (Poster) 2017 - [c7]Yixuan Li, Pingmei Xu, Dmitry Lagun, Vidhya Navalpakkam:
Towards Measuring and Inferring User Interest from Gaze. WWW (Companion Volume) 2017: 525-533 - [i9]Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger:
Snapshot Ensembles: Train 1, get M for free. CoRR abs/1704.00109 (2017) - [i8]Shiyu Liang, Yixuan Li, R. Srikant:
Principled Detection of Out-of-Distribution Examples in Neural Networks. CoRR abs/1706.02690 (2017) - 2016
- [c6]Yixuan Li, Oscar Martinez, Xing Chen, Yi Li, John E. Hopcroft:
In a World That Counts: Clustering and Detecting Fake Social Engagement at Scale. WWW 2016: 111-120 - [c5]Jiezhong Qiu, Yixuan Li, Jie Tang, Zheng Lu, Hao Ye, Bo Chen, Qiang Yang, John E. Hopcroft:
The Lifecycle and Cascade of WeChat Social Messaging Groups. WWW 2016: 311-320 - [c4]Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John E. Hopcroft:
Convergent Learning: Do different neural networks learn the same representations? ICLR 2016 - [i7]Xun Huang, Yixuan Li, Omid Poursaeed, John E. Hopcroft, Serge J. Belongie:
Stacked Generative Adversarial Networks. CoRR abs/1612.04357 (2016) - 2015
- [c3]Kun He, Yiwei Sun, David Bindel
Detecting Overlapping Communities from Local Spectral Subspaces. ICDM 2015: 769-774 - [c2]Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John E. Hopcroft:
Convergent Learning: Do different neural networks learn the same representations? FE@NIPS 2015: 196-212 - [c1]Yixuan Li, Kun He, David Bindel
Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach. WWW 2015: 658-668 - [i6]Yixuan Li, Kun He, David Bindel, John E. Hopcroft:
Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach. CoRR abs/1509.07715 (2015) - [i5]Yixuan Li, Kun He, David Bindel, John E. Hopcroft:
Overlapping Community Detection via Local Spectral Clustering. CoRR abs/1509.07996 (2015) - [i4]Kun He, Yiwei Sun, David Bindel, John E. Hopcroft, Yixuan Li:
Detecting Overlapping Communities from Local Spectral Subspaces. CoRR abs/1509.08065 (2015) - [i3]Jacob R. Gardner, Matt J. Kusner, Yixuan Li, Paul Upchurch, Kilian Q. Weinberger, John E. Hopcroft:
Deep Manifold Traversal: Changing Labels with Convolutional Features. CoRR abs/1511.06421 (2015) - [i2]Yixuan Li, Oscar Martinez, Xing Chen, Yi Li, John E. Hopcroft:
In a World that Counts: Clustering and Detecting Fake Social Engagement at Scale. CoRR abs/1512.05457 (2015) - [i1]Jiezhong Qiu, Yixuan Li, Jie Tang, Zheng Lu, Hao Ye, Bo Chen, Qiang Yang, John E. Hopcroft:
The Lifecycle and Cascade of Social Messaging Groups. CoRR abs/1512.07831 (2015)
Coauthor Index
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