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2020 – today
- 2023
[j7]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Tomas Pfister:
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch. Trans. Mach. Learn. Res. 2023 (2023)- [c40]Kihyuk Sohn, Jinsung Yoon, Chun-Liang Li, Chen-Yu Lee, Tomas Pfister:
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types. WACV 2023: 5468-5479 - [c39]Justin Lazarow, Kihyuk Sohn, Chen-Yu Lee, Chun-Liang Li, Zizhao Zhang, Tomas Pfister:
Unifying Distribution Alignment as a Loss for Imbalanced Semi-supervised Learning. WACV 2023: 5633-5642 - [i47]Ruoxi Sun, Chun-Liang Li, Sercan Ö. Arik, Michael W. Dusenberry, Chen-Yu Lee, Tomas Pfister:
Neural Spline Search for Quantile Probabilistic Modeling. CoRR abs/2301.04857 (2023) - [i46]Kuniaki Saito, Kihyuk Sohn, Xiang Zhang, Chun-Liang Li, Chen-Yu Lee, Kate Saenko, Tomas Pfister:
Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval. CoRR abs/2302.03084 (2023) - [i45]Si-An Chen, Chun-Liang Li, Nate Yoder, Sercan Ö. Arik, Tomas Pfister:
TSMixer: An all-MLP Architecture for Time Series Forecasting. CoRR abs/2303.06053 (2023) - [i44]Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister:
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes. CoRR abs/2305.02301 (2023) - [i43]Chen-Yu Lee, Chun-Liang Li, Hao Zhang, Timothy Dozat, Vincent Perot, Guolong Su, Xiang Zhang, Kihyuk Sohn, Nikolai Glushnev, Renshen Wang, Joshua Ainslie, Shangbang Long, Siyang Qin, Yasuhisa Fujii, Nan Hua, Tomas Pfister:
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction. CoRR abs/2305.02549 (2023) - 2022
- [j6]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Chen-Yu Lee, Tomas Pfister:
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection. Trans. Mach. Learn. Res. 2022 (2022) - [c38]Chen-Yu Lee, Chun-Liang Li, Timothy Dozat, Vincent Perot, Guolong Su, Nan Hua, Joshua Ainslie, Renshen Wang, Yasuhisa Fujii, Tomas Pfister:
FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction. ACL (1) 2022: 3735-3754 - [c37]Sana Tonekaboni, Chun-Liang Li, Sercan Ö. Arik, Anna Goldenberg, Tomas Pfister:
Decoupling Local and Global Representations of Time Series. AISTATS 2022: 8700-8714 - [c36]Yuliang Zou, Zizhao Zhang, Chun-Liang Li, Han Zhang, Tomas Pfister, Jia-Bin Huang:
Learning Instance-Specific Adaptation for Cross-Domain Segmentation. ECCV (33) 2022: 459-476 - [c35]Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, Rosalind W. Picard:
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals. ICLR 2022 - [i42]Sana Tonekaboni, Chun-Liang Li, Sercan Ö. Arik, Anna Goldenberg, Tomas Pfister:
Decoupling Local and Global Representations of Time Series. CoRR abs/2202.02262 (2022) - [i41]Chen-Yu Lee, Chun-Liang Li, Timothy Dozat, Vincent Perot, Guolong Su, Nan Hua, Joshua Ainslie, Renshen Wang, Yasuhisa Fujii, Tomas Pfister:
FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction. CoRR abs/2203.08411 (2022) - [i40]Yuliang Zou, Zizhao Zhang, Chun-Liang Li, Han Zhang, Tomas Pfister, Jia-Bin Huang:
Learning Instance-Specific Adaptation for Cross-Domain Segmentation. CoRR abs/2203.16530 (2022) - [i39]Kuniaki Saito, Kihyuk Sohn, Xiang Zhang, Chun-Liang Li, Chen-Yu Lee, Kate Saenko, Tomas Pfister:
Prefix Conditioning Unifies Language and Label Supervision. CoRR abs/2206.01125 (2022) - [i38]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Tomas Pfister:
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch. CoRR abs/2212.00173 (2022) - [i37]Songwei Ge, Shlok Mishra, Simon Kornblith, Chun-Liang Li, David Jacobs:
Hyperbolic Contrastive Learning for Visual Representations beyond Objects. CoRR abs/2212.00653 (2022) - 2021
- [j5]Sercan Ö. Arik
, Joel Shor, Rajarishi Sinha, Jinsung Yoon, Joseph R. Ledsam, Long T. Le, Michael W. Dusenberry, Nathanael C. Yoder, Kris Popendorf, Arkady Epshteyn, Johan Euphrosine, Elli Kanal, Isaac Jones, Chun-Liang Li, Beth Luan, Joe Mckenna, Vikas Menon, Shashank Singh, Mimi Sun, Ashwin Sura Ravi, Leyou Zhang, Dario Sava, Kane Cunningham, Hiroki Kayama, Thomas C. Tsai, Daisuke Yoneoka, Shuhei Nomura, Hiroaki Miyata, Tomas Pfister:
A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan. npj Digit. Medicine 4 (2021) - [j4]Haoran You, Yu Cheng, Tianheng Cheng, Chun-Liang Li, Pan Zhou:
Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling. IEEE Trans. Neural Networks Learn. Syst. 32(10): 4389-4403 (2021) - [c34]Chen-Yu Lee, Chun-Liang Li, Chu Wang, Renshen Wang, Yasuhisa Fujii, Siyang Qin, Ashok C. Popat, Tomas Pfister:
ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction. ACL/IJCNLP (2) 2021: 314-321 - [c33]Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister:
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization. CVPR 2021: 9664-9674 - [c32]Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee:
i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning. ICLR 2021 - [c31]Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, Tomas Pfister:
Learning and Evaluating Representations for Deep One-Class Classification. ICLR 2021 - [c30]Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, Tomas Pfister:
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation. ICLR 2021 - [c29]Sangwoo Mo, Hyunwoo Kang, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin:
Object-aware Contrastive Learning for Debiased Scene Representation. NeurIPS 2021: 12251-12264 - [c28]Songwei Ge, Shlok Mishra, Chun-Liang Li, Haohan Wang, David Jacobs:
Robust Contrastive Learning Using Negative Samples with Diminished Semantics. NeurIPS 2021: 27356-27368 - [c27]Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin:
A Unified View of cGANs with and without Classifiers. NeurIPS 2021: 27566-27579 - [c26]Chenghui Zhou, Chun-Liang Li, Barnabás Póczos:
Unsupervised program synthesis for images by sampling without replacement. UAI 2021: 408-418 - [i36]Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister:
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization. CoRR abs/2104.04015 (2021) - [i35]Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, Rosalind W. Picard:
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals. CoRR abs/2105.15164 (2021) - [i34]Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li, Sercan Ö. Arik, Chen-Yu Lee, Tomas Pfister:
Self-Trained One-class Classification for Unsupervised Anomaly Detection. CoRR abs/2106.06115 (2021) - [i33]Chen-Yu Lee, Chun-Liang Li, Chu Wang, Renshen Wang, Yasuhisa Fujii, Siyang Qin, Ashok C. Popat, Tomas Pfister:
ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction. CoRR abs/2106.10786 (2021) - [i32]Sangwoo Mo, Hyunwoo Kang, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin:
Object-aware Contrastive Learning for Debiased Scene Representation. CoRR abs/2108.00049 (2021) - [i31]Songwei Ge, Shlok Mishra, Haohan Wang, Chun-Liang Li, David Jacobs:
Robust Contrastive Learning Using Negative Samples with Diminished Semantics. CoRR abs/2110.14189 (2021) - [i30]Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin:
A Unified View of cGANs with and without Classifiers. CoRR abs/2111.01035 (2021) - [i29]Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin:
Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration. CoRR abs/2111.02316 (2021) - [i28]Kihyuk Sohn, Jinsung Yoon, Chun-Liang Li, Chen-Yu Lee, Tomas Pfister:
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types. CoRR abs/2112.11573 (2021) - 2020
- [b1]Chun-Liang Li:
Learning Generative Models using Transformations. Carnegie Mellon University, USA, 2020 - [c25]Sercan Ömer Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long T. Le, Vikas Menon, Shashank Singh, Leyou Zhang, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister:
Interpretable Sequence Learning for Covid-19 Forecasting. NeurIPS 2020 - [c24]Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin Raffel, Ekin Dogus Cubuk, Alexey Kurakin, Chun-Liang Li:
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. NeurIPS 2020 - [c23]Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar:
On Completeness-aware Concept-Based Explanations in Deep Neural Networks. NeurIPS 2020 - [i27]Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel:
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. CoRR abs/2001.07685 (2020) - [i26]Chenghui Zhou, Chun-Liang Li, Barnabás Póczos:
Unsupervised Program Synthesis for Images using Tree-Structured LSTM. CoRR abs/2001.10119 (2020) - [i25]Kihyuk Sohn, Zizhao Zhang, Chun-Liang Li, Han Zhang, Chen-Yu Lee, Tomas Pfister:
A Simple Semi-Supervised Learning Framework for Object Detection. CoRR abs/2005.04757 (2020) - [i24]Wei-Cheng Chang, Chun-Liang Li, Youssef Mroueh, Yiming Yang:
Kernel Stein Generative Modeling. CoRR abs/2007.03074 (2020) - [i23]Sercan Ömer Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long T. Le, Vikas Menon, Shashank Singh, Leyou Zhang, Nate Yoder, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister:
Interpretable Sequence Learning for COVID-19 Forecasting. CoRR abs/2008.00646 (2020) - [i22]Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee:
i-Mix: A Strategy for Regularizing Contrastive Representation Learning. CoRR abs/2010.08887 (2020) - [i21]Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, Tomas Pfister:
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation. CoRR abs/2010.09713 (2020) - [i20]Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, Tomas Pfister:
Learning and Evaluating Representations for Deep One-class Classification. CoRR abs/2011.02578 (2020)
2010 – 2019
- 2019
- [c22]Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabás Póczos:
Implicit Kernel Learning. AISTATS 2019: 2007-2016 - [c21]Chun-Liang Li, Tomas Simon, Jason M. Saragih, Barnabás Póczos, Yaser Sheikh:
LBS Autoencoder: Self-Supervised Fitting of Articulated Meshes to Point Clouds. CVPR 2019: 11967-11976 - [c20]Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos:
Kernel Change-point Detection with Auxiliary Deep Generative Models. ICLR (Poster) 2019 - [c19]Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabás Póczos, Ruslan Salakhutdinov:
Point Cloud GAN. DGS@ICLR 2019 - [c18]Hsueh-Ti Derek Liu, Michael Tao, Chun-Liang Li, Derek Nowrouzezahrai, Alec Jacobson:
Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer. ICLR (Poster) 2019 - [i19]Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos:
Kernel Change-point Detection with Auxiliary Deep Generative Models. CoRR abs/1901.06077 (2019) - [i18]Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh, Yiming Yang, Barnabás Póczos:
Implicit Kernel Learning. CoRR abs/1902.10214 (2019) - [i17]Chun-Liang Li, Tomas Simon, Jason M. Saragih, Barnabás Póczos, Yaser Sheikh:
LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds. CoRR abs/1904.10037 (2019) - [i16]Songwei Ge, Austin Dill, Eunsu Kang, Chun-Liang Li, Lingyao Zhang, Manzil Zaheer, Barnabás Póczos:
Developing Creative AI to Generate Sculptural Objects. CoRR abs/1908.07587 (2019) - [i15]Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Pradeep Ravikumar, Tomas Pfister:
On Concept-Based Explanations in Deep Neural Networks. CoRR abs/1910.07969 (2019) - [i14]Austin Dill, Chun-Liang Li, Songwei Ge, Eunsu Kang:
Getting Topology and Point Cloud Generation to Mesh. CoRR abs/1912.03787 (2019) - [i13]Austin Dill, Songwei Ge, Eunsu Kang, Chun-Liang Li, Barnabás Póczos:
Learned Interpolation for 3D Generation. CoRR abs/1912.10787 (2019) - 2018
- [c17]Yusha Liu, Chun-Liang Li, Barnabás Póczos:
Classifier Two Sample Test for Video Anomaly Detections. BMVC 2018: 71 - [c16]Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, Yu Cheng:
Sobolev GAN. ICLR (Poster) 2018 - [c15]Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer, Barnabás Póczos:
Nonparametric Density Estimation under Adversarial Losses. NeurIPS 2018: 10246-10257 - [i12]Xi Ouyang, Yu Cheng, Yifan Jiang, Chun-Liang Li, Pan Zhou:
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond. CoRR abs/1804.02047 (2018) - [i11]Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer, Barnabás Póczos:
Nonparametric Density Estimation under Adversarial Losses. CoRR abs/1805.08836 (2018) - [i10]Hsueh-Ti Derek Liu, Michael Tao, Chun-Liang Li, Derek Nowrouzezahrai, Alec Jacobson:
Adversarial Geometry and Lighting using a Differentiable Renderer. CoRR abs/1808.02651 (2018) - [i9]Chun-Liang Li, Manzil Zaheer, Yang Zhang, Barnabás Póczos, Ruslan Salakhutdinov:
Point Cloud GAN. CoRR abs/1810.05795 (2018) - [i8]Chun-Liang Li, Eunsu Kang, Songwei Ge, Lingyao Zhang, Austin Dill, Manzil Zaheer, Barnabás Póczos:
Hallucinating Point Cloud into 3D Sculptural Object. CoRR abs/1811.05389 (2018) - [i7]Haoran You, Yu Cheng, Tianheng Cheng, Chun-Liang Li, Pan Zhou:
Bayesian CycleGAN via Marginalizing Latent Sampling. CoRR abs/1811.07465 (2018) - 2017
- [c14]Po-Wei Wang, Chun-Liang Li, J. Zico Kolter:
Polynomial Optimization Methods for Matrix Factorization. AAAI 2017: 2710-2717 - [c13]Jen-Hao Rick Chang, Chun-Liang Li, Barnabás Póczos, B. V. K. Vijaya Kumar:
One Network to Solve Them All - Solving Linear Inverse Problems Using Deep Projection Models. ICCV 2017: 5889-5898 - [c12]Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos:
Data-driven Random Fourier Features using Stein Effect. IJCAI 2017: 1497-1503 - [c11]Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabás Póczos:
MMD GAN: Towards Deeper Understanding of Moment Matching Network. NIPS 2017: 2203-2213 - [i6]Jen-Hao Rick Chang, Chun-Liang Li, Barnabás Póczos, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan:
One Network to Solve Them All - Solving Linear Inverse Problems using Deep Projection Models. CoRR abs/1703.09912 (2017) - [i5]Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos:
Data-driven Random Fourier Features using Stein Effect. CoRR abs/1705.08525 (2017) - [i4]Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabás Póczos:
MMD GAN: Towards Deeper Understanding of Moment Matching Network. CoRR abs/1705.08584 (2017) - [i3]Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, Yu Cheng:
Sobolev GAN. CoRR abs/1711.04894 (2017) - 2016
- [c10]Chun-Liang Li, Hsuan-Tien Lin, Chi-Jen Lu:
Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA. AISTATS 2016: 473-481 - [c9]Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider:
High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models. AISTATS 2016: 884-892 - [c8]Chun-Liang Li, Barnabás Póczos:
Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods. UAI 2016 - [i2]Chun-Liang Li, Siamak Ravanbakhsh, Barnabás Póczos:
Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM. CoRR abs/1611.03879 (2016) - 2015
- [j3]Chun-Liang Li, Yu-Chuan Su, Ting-Wei Lin, Cheng-Hao Tsai, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Chun-Pai Yang, Cheng-Xia Chang, Wei-Sheng Chin, Yu-Chin Juan, Hsiao-Yu Tung, Jui-Pin Wang, Cheng-Kuang Wei, Felix Wu, Tu-Chun Yin, Tong Yu, Yong Zhuang, Shou-De Lin, Hsuan-Tien Lin, Chih-Jen Lin:
Combination of feature engineering and ranking models for paper-author identification in KDD cup 2013. J. Mach. Learn. Res. 16: 2921-2947 (2015) - [j2]Chun-Liang Li, Chun-Sung Ferng, Hsuan-Tien Lin:
Active Learning Using Hint Information. Neural Comput. 27(8): 1738-1765 (2015) - [i1]Chun-Liang Li, Hsuan-Tien Lin, Chi-Jen Lu:
Rivalry of Two Families of Algorithms for Memory-Restricted Streaming PCA. CoRR abs/1506.01490 (2015) - 2014
- [j1]Wei-Sheng Chin, Yong Zhuang, Yu-Chin Juan, Felix Wu, Hsiao-Yu Tung, Tong Yu, Jui-Pin Wang, Cheng-Xia Chang, Chun-Pai Yang, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Yu-Chuan Su, Cheng-Kuang Wei, Tu-Chun Yin, Chun-Liang Li, Ting-Wei Lin, Cheng-Hao Tsai, Shou-De Lin, Hsuan-Tien Lin, Chih-Jen Lin:
Effective string processing and matching for author disambiguation. J. Mach. Learn. Res. 15(1): 3037-3064 (2014) - [c7]Jyun-Yu Jiang, Chun-Liang Li, Chun-Pai Yang, Chung-Tsai Su:
POSTER: Scanning-free Personalized Malware Warning System by Learning Implicit Feedback from Detection Logs. CCS 2014: 1436-1438 - [c6]Chun-Liang Li, Hsuan-Tien Lin:
Condensed Filter Tree for Cost-Sensitive Multi-Label Classification. ICML 2014: 423-431 - 2013
- [c5]Chun-Liang Li, Yu-Chuan Su, Ting-Wei Lin, Cheng-Hao Tsai, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Chun-Pai Yang, Cheng-Xia Chang, Wei-Sheng Chin, Yu-Chin Juan, Hsiao-Yu Tung, Jui-Pin Wang, Cheng-Kuang Wei, Felix Wu, Tu-Chun Yin, Tong Yu, Yong Zhuang, Shou-de Lin, Hsuan-Tien Lin, Chih-Jen Lin:
Combination of feature engineering and ranking models for paper-author identification in KDD Cup 2013. KDD Cup 2013: 2:1-2:7 - [c4]Wei-Sheng Chin, Yu-Chin Juan, Yong Zhuang, Felix Wu, Hsiao-Yu Tung, Tong Yu, Jui-Pin Wang, Cheng-Xia Chang, Chun-Pai Yang, Wei-Cheng Chang, Kuan-Hao Huang, Tzu-Ming Kuo, Shan-Wei Lin, Young-San Lin, Yu-Chen Lu, Yu-Chuan Su, Cheng-Kuang Wei, Tu-Chun Yin, Chun-Liang Li, Ting-Wei Lin, Cheng-Hao Tsai, Shou-De Lin, Hsuan-Tien Lin, Chih-Jen Lin:
Effective string processing and matching for author disambiguation. KDD Cup 2013: 7:1-7:9 - 2012
- [c3]Po-Lung Chen, Chen-Tse Tsai, Yao-Nan Chen, Ku-Chun Chou, Chun-Liang Li, Cheng-Hao Tsai, Kuan-Wei Wu, Yu-Cheng Chou, Chung-Yi Li, Wei-Shih Lin, Shu-Hao Yu, Rong-Bing Chiu, Chieh-Yen Lin, Chien-Chih Wang, Po-Wei Wang, Wei-Lun Su, Chen-Hung Wu, Tsung-Ting Kuo, Todd G. McKenzie, Ya-Hsuan Chang, Chun-Sung Ferng, Chia-Mau Ni, Hsuan-Tien Lin, Chih-Jen Lin, Shou-De Lin:
A Linear Ensemble of Individual and Blended Models for Music Rating Prediction. KDD Cup 2012: 21-60 - [c2]Todd G. McKenzie, Chun-Sung Ferng, Yao-Nan Chen, Chun-Liang Li, Cheng-Hao Tsai, Kuan-Wei Wu, Ya-Hsuan Chang, Chung-Yi Li, Wei-Shih Lin, Shu-Hao Yu, Chieh-Yen Lin, Po-Wei Wang, Chia-Mau Ni, Wei-Lun Su, Tsung-Ting Kuo, Chen-Tse Tsai, Po-Lung Chen, Rong-Bing Chiu, Ku-Chun Chou, Yu-Cheng Chou, Chien-Chih Wang, Chen-Hung Wu, Hsuan-Tien Lin, Chih-Jen Lin, Shou-De Lin:
Novel Models and Ensemble Techniques to Discriminate Favorite Items from Unrated Ones for Personalized Music Recommendation. KDD Cup 2012: 101-135 - [c1]Chun-Liang Li, Chun-Sung Ferng, Hsuan-Tien Lin:
Active Learning with Hinted Support Vector Machine. ACML 2012: 221-235
Coauthor Index
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