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Chuan-Sheng Foo
Chuan Sheng Foo
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2020 – today
- 2024
- [j20]Weide Liu
, Zhonghua Wu, Yang Zhao, Yuming Fang, Chuan-Sheng Foo, Jun Cheng, Guosheng Lin:
Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation. Int. J. Comput. Vis. 132(4): 1277-1291 (2024) - [j19]Xun Xu, Jingyi Liao, Lile Cai
Revisiting pretraining for semi-supervised learning in the low-label regime. Neurocomputing 565: 126971 (2024) - [j18]Wenyu Zhang
Training neural networks with classification rules for incorporating domain knowledge. Knowl. Based Syst. 294: 111716 (2024) - [j17]Jingyi Liao
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection. IEEE Trans. Image Process. 33: 2090-2103 (2024) - [j16]Yinan He, Lile Cai, Jingyi Liao, Chuan-Sheng Foo:
Hybrid Active Learning with Uncertainty-Weighted Embeddings. Trans. Mach. Learn. Res. 2024 (2024) - [j15]Chaitanya K. Joshi
On Representation Knowledge Distillation for Graph Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 35(4): 4656-4667 (2024) - [j14]Xian Shi, Lile Cai, Ke Chen
Label-Efficient Point Cloud Semantic Segmentation: A Holistic Active Learning Approach. World Sci. Annu. Rev. Artif. Intell. 2: 2440006:1-2440006:29 (2024) - [c58]Kennard Yanting Chan, Fayao Liu, Guosheng Lin, Chuan Sheng Foo, Weisi Lin:
Fine Structure-Aware Sampling: A New Sampling Training Scheme for Pixel-Aligned Implicit Models in Single-View Human Reconstruction. AAAI 2024: 964-971 - [c57]Chaoyue Song, Jiacheng Wei, Chuan Sheng Foo, Guosheng Lin, Fayao Liu:
REACTO: Reconstructing Articulated Objects from a Single Video. CVPR 2024: 5384-5395 - [c56]Cheng Chen, Xiaofeng Yang, Fan Yang, Chengzeng Feng, Zhoujie Fu, Chuan-Sheng Foo, Guosheng Lin, Fayao Liu:
Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior. CVPR 2024: 10228-10237 - [c55]Kennard Yanting Chan, Fayao Liu, Guosheng Lin, Chuan Sheng Foo, Weisi Lin:
R-Cyclic Diffuser: Reductive and Cyclic Latent Diffusion for 3D Clothed Human Digitalization. CVPR 2024: 10304-10313 - [c54]Wenyu Zhang, Qingmu Liu, Felix Ong Wei Cong, Mohamed Ragab, Chuan-Sheng Foo:
Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias. CVPR 2024: 23912-23921 - [c53]Kennard Yanting Chan, Fayao Liu, Guosheng Lin, Chuan Sheng Foo, Weisi Lin:
3DFG-PIFu: 3D Feature Grids for Human Digitization from Sparse Views. ECCV (24) 2024: 38-54 - [c52]Gregory Kang Ruey Lau, Xinyuan Niu, Hieu Dao, Jiangwei Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Waterfall: Scalable Framework for Robust Text Watermarking and Provenance for LLMs. EMNLP 2024: 20432-20466 - [c51]Sebastian Shenghong Tay, Chuan-Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low:
A Unified Framework for Bayesian Optimization under Contextual Uncertainty. ICLR 2024 - [c50]Zhiliang Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components. ICML 2024 - [c49]Dapeng Hu, Jian Liang, Xinchao Wang, Chuan-Sheng Foo:
Pseudo-Calibration: Improving Predictive Uncertainty Estimation in Unsupervised Domain Adaptation. ICML 2024 - [c48]Jingtan Wang, Xiaoqiang Lin, Rui Qiao, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions. ICML 2024 - [c47]Sezin Kircali Ata, Zhi-Hui Kong, Anusha James, Lile Cai, Kiat Seng Yeo, Khin Mi Mi Aung, Chuan Sheng Foo, Ashish James:
The Initialization Factor: Understanding its Impact on Active Learning for Analog Circuit Design. ISCAS 2024: 1-5 - [c46]Brian Formento, Wenjie Feng, Chuan-Sheng Foo, Anh Tuan Luu, See-Kiong Ng:
SemRoDe: Macro Adversarial Training to Learn Representations that are Robust to Word-Level Attacks. NAACL-HLT 2024: 8005-8028 - [c45]Yiting Li, Adam David Goodge, Fayao Liu, Chuan-Sheng Foo:
PromptAD: Zero-shot Anomaly Detection using Text Prompts. WACV 2024: 1082-1091 - [i51]Jingyi Liao, Xun Xu, Manh Cuong Nguyen, Adam Goodge, Chuan Sheng Foo:
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection. CoRR abs/2402.18998 (2024) - [i50]Kennard Yanting Chan, Fayao Liu, Guosheng Lin, Chuan Sheng Foo, Weisi Lin:
Fine Structure-Aware Sampling: A New Sampling Training Scheme for Pixel-Aligned Implicit Models in Single-View Human Reconstruction. CoRR abs/2402.19197 (2024) - [i49]Cheng Chen, Xiaofeng Yang, Fan Yang, Chengzeng Feng, Zhoujie Fu, Chuan-Sheng Foo, Guosheng Lin, Fayao Liu:
Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior. CoRR abs/2403.09140 (2024) - [i48]Wenyu Zhang, Qingmu Liu, Felix Ong Wei Cong, Mohamed Ragab, Chuan-Sheng Foo:
Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias. CoRR abs/2403.11234 (2024) - [i47]Brian Formento, Wenjie Feng, Chuan Sheng Foo, Luu Anh Tuan, See-Kiong Ng:
SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks. CoRR abs/2403.18423 (2024) - [i46]Chaoyue Song, Jiacheng Wei, Chuan-Sheng Foo, Guosheng Lin, Fayao Liu:
REACTO: Reconstructing Articulated Objects from a Single Video. CoRR abs/2404.11151 (2024) - [i45]Wenyu Zhang, Li Shen, Chuan-Sheng Foo:
Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-Training. CoRR abs/2405.02954 (2024) - [i44]Mohamed Ragab, Peiliang Gong, Emadeldeen Eldele, Wenyu Zhang, Min Wu, Chuan-Sheng Foo, Daoqiang Zhang, Xiaoli Li, Zhenghua Chen:
Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation. CoRR abs/2406.02635 (2024) - [i43]Jingtan Wang, Xiaoqiang Lin, Rui Qiao, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions. CoRR abs/2406.04606 (2024) - [i42]Gregory Kang Ruey Lau, Xinyuan Niu, Hieu Dao, Jiangwei Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Waterfall: Framework for Robust and Scalable Text Watermarking. CoRR abs/2407.04411 (2024) - [i41]Xinyi Xu, Shuaiqi Wang, Chuan-Sheng Foo, Bryan Kian Hsiang Low, Giulia Fanti:
Data Distribution Valuation. CoRR abs/2410.04386 (2024) - [i40]Yongyi Su, Yushu Li, Nanqing Liu, Kui Jia, Xulei Yang, Chuan-Sheng Foo, Xun Xu:
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning. CoRR abs/2410.04682 (2024) - 2023
- [j13]Cuong Manh Nguyen
Diverse and consistent multi-view networks for semi-supervised regression. Mach. Learn. 112(7): 2359-2395 (2023) - [j12]Mohamed Ragab
ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data. ACM Trans. Knowl. Discov. Data 17(8): 106:1-106:18 (2023) - [j11]Kiran Krishnamachari, See-Kiong Ng, Chuan-Sheng Foo:
Mitigating Real-World Distribution Shifts in the Fourier Domain. Trans. Mach. Learn. Res. 2023 (2023) - [c44]Zijian Zhou, Xinyi Xu, Rachael Hwee Ling Sim, Chuan Sheng Foo, Bryan Kian Hsiang Low:
Probably Approximate Shapley Fairness with Applications in Machine Learning. AAAI 2023: 5910-5918 - [c43]Sebastian Shenghong Tay, Quoc Phong Nguyen, Chuan Sheng Foo, Bryan Kian Hsiang Low:
No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities. AISTATS 2023: 3591-3619 - [c42]Xinyi Xu, Zhaoxuan Wu
FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery. AISTATS 2023: 4033-4057 - [c41]Brian Formento, Chuan-Sheng Foo, Anh Tuan Luu, See-Kiong Ng:
Using Punctuation as an Adversarial Attack on Deep Learning-Based NLP Systems: An Empirical Study. EACL (Findings) 2023: 1-34 - [c40]Kangkang Lu, Manh Cuong Nguyen
On Adversarial Robustness of Audio Classifiers. ICASSP 2023: 1-5 - [c39]Wenyu Zhang, Li Shen, Chuan-Sheng Foo:
Rethinking the Role of Pre-Trained Networks in Source-Free Domain Adaptation. ICCV 2023: 18795-18805 - [c38]Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Fair yet Asymptotically Equal Collaborative Learning. ICML 2023: 21223-21259 - [c37]Mohamed Ragab
Source-Free Domain Adaptation with Temporal Imputation for Time Series Data. KDD 2023: 1989-1998 - [c36]Sebastian Tay, Chuan Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low:
Bayesian Optimization with Cost-varying Variable Subsets. NeurIPS 2023 - [c35]Xinyi Xu, Thanh Lam, Chuan Sheng Foo, Bryan Kian Hsiang Low:
Model Shapley: Equitable Model Valuation with Black-box Access. NeurIPS 2023 - [i39]Kiran Krishnamachari, See-Kiong Ng, Chuan-Sheng Foo:
Fourier Sensitivity and Regularization of Computer Vision Models. CoRR abs/2301.13514 (2023) - [i38]Weide Liu, Zhonghua Wu, Yang Zhao, Yuming Fang, Chuan-Sheng Foo, Jun Cheng, Guosheng Lin:
Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation. CoRR abs/2303.13724 (2023) - [i37]Chaoyue Song, Tianyi Chen, Yiwen Chen, Jiacheng Wei, Chuan Sheng Foo, Fayao Liu, Guosheng Lin:
MoDA: Modeling Deformable 3D Objects from Casual Videos. CoRR abs/2304.08279 (2023) - [i36]Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Chuan-Sheng Foo, Bryan Kian Hsiang Low:
Fair yet Asymptotically Equal Collaborative Learning. CoRR abs/2306.05764 (2023) - [i35]Dapeng Hu, Jian Liang, Xinchao Wang, Chuan-Sheng Foo:
PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation. CoRR abs/2307.07489 (2023) - [i34]Mohamed Ragab, Emadeldeen Eldele, Min Wu, Chuan-Sheng Foo, Xiaoli Li, Zhenghua Chen:
Source-Free Domain Adaptation with Temporal Imputation for Time Series Data. CoRR abs/2307.07542 (2023) - [i33]Jingtan Wang, Xinyang Lu, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, Bryan Kian Hsiang Low:
WASA: WAtermark-based Source Attribution for Large Language Model-Generated Data. CoRR abs/2310.00646 (2023) - 2022
- [j10]Saisubramaniam Gopalakrishnan
Classify and generate: Using classification latent space representations for image generations. Neurocomputing 471: 296-334 (2022) - [j9]Kazuki Osawa
Scalable and Practical Natural Gradient for Large-Scale Deep Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(1): 404-415 (2022) - [j8]Zeyu Jiang
MA-GANet: A Multi-Attention Generative Adversarial Network for Defocus Blur Detection. IEEE Trans. Image Process. 31: 3494-3508 (2022) - [j7]Xun Xu
SemiCurv: Semi-Supervised Curvilinear Structure Segmentation. IEEE Trans. Image Process. 31: 5109-5120 (2022) - [j6]Kiran Krishnamachari, See-Kiong Ng, Chuan-Sheng Foo:
Fourier Sensitivity and Regularization of Computer Vision Models. Trans. Mach. Learn. Res. 2022 (2022) - [j5]Astha Garg
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series. IEEE Trans. Neural Networks Learn. Syst. 33(6): 2508-2517 (2022) - [c34]Fayao Liu, Guosheng Lin, Chuan-Sheng Foo, Chaitanya K. Joshi, Jie Lin:
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis. 3DV 2022: 42-51 - [c33]Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo, Bryan Kian Hsiang Low:
Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards. AAAI 2022: 9448-9456 - [c32]Huijuan Yang
A Minimally Supervised Approach for Medical Image Quality Assessment in Domain Shift Settings. ICASSP 2022: 1286-1290 - [c31]Yasin Yazici, Bruno Lecouat, Kim-Hui Yap, Stefan Winkler, Georgios Piliouras, Vijay Chandrasekhar, Chuan-Sheng Foo:
Mixed Membership Generative Adversarial Networks. ICIP 2022: 1026-1030 - [c30]Lile Cai, Ramanpreet Singh Pahwa, Xun Xu, Jie Wang, Richard Chang, Lining Zhang, Chuan-Sheng Foo:
Exploring Active Learning for Semiconductor Defect Segmentation. ICIP 2022: 1796-1800 - [c29]Sebastian Shenghong Tay, Chuan Sheng Foo, Daisuke Urano, Richalynn Leong, Bryan Kian Hsiang Low:
Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity. ICML 2022: 21180-21204 - [c28]Wenyu Zhang, Mohamed Ragab
Domain Generalization via Selective Consistency Regularization for Time Series Classification. ICPR 2022: 2149-2156 - [c27]Xian Shi, Xun Xu, Wanyue Zhang, Xiatian Zhu, Chuan Sheng Foo, Kui Jia:
Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding. ICPR 2022: 5045-5051 - [c26]Wenyu Zhang, Li Shen, Wanyue Zhang, Chuan-Sheng Foo:
Few-Shot Adaptation of Pre-Trained Networks for Domain Shift. IJCAI 2022: 1665-1671 - [c25]Lining Zhang, Salahuddin Raju, Ashish James, Rahul Dutta, Gregoire Fournier, Damien Lancry, Kevin Chai Tshun Chuan, Vijay Ramaseshan Chandrasekhar, Chuan Sheng Foo:
Bayesian Deep Active Learning for Analog Circuit Performance Classification. ISCAS 2022: 3018-3022 - [c24]Rahul Dutta, Ashish James, Salahuddin Raju, Yong-Joon Jeon, Chuan Sheng Foo, Kevin Tshun Chuan Chai:
Automated Deep Learning Platform for Accelerated Analog Circuit Design. SOCC 2022: 1-5 - [i32]Mohamed Ragab
ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data. CoRR abs/2203.08321 (2022) - [i31]Xian Shi, Xun Xu, Wanyue Zhang, Xiatian Zhu, Chuan Sheng Foo, Kui Jia:
Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding. CoRR abs/2205.01006 (2022) - [i30]Xun Xu, Jingyi Liao, Lile Cai, Cuong Manh Nguyen, Kangkang Lu, Wanyue Zhang, Yasin Yazici, Chuan Sheng Foo:
Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regime. CoRR abs/2205.03001 (2022) - [i29]Xun Xu, Manh Cuong Nguyen, Yasin Yazici, Kangkang Lu, Hlaing Min, Chuan-Sheng Foo:
SemiCurv: Semi-Supervised Curvilinear Structure Segmentation. CoRR abs/2205.08706 (2022) - [i28]Wenyu Zhang, Li Shen, Wanyue Zhang, Chuan-Sheng Foo:
Few-Shot Adaptation of Pre-Trained Networks for Domain Shift. CoRR abs/2205.15234 (2022) - [i27]Wenyu Zhang, Mohamed Ragab
Domain Generalization via Selective Consistency Regularization for Time Series Classification. CoRR abs/2206.07876 (2022) - [i26]Manas Gupta, Efe Camci, Vishandi Rudy Keneta, Abhishek Vaidyanathan, Ritwik Kanodia, Chuan-Sheng Foo, Min Wu
Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning. CoRR abs/2209.14624 (2022) - [i25]Zijian Zhou, Xinyi Xu, Rachael Hwee Ling Sim, Chuan Sheng Foo, Kian Hsiang Low:
Probably Approximate Shapley Fairness with Applications in Machine Learning. CoRR abs/2212.00630 (2022) - [i24]Wenyu Zhang, Li Shen, Chuan-Sheng Foo:
Co-Learning with Pre-Trained Networks Improves Source-Free Domain Adaptation. CoRR abs/2212.07585 (2022) - 2021
- [j4]Balagopal Unnikrishnan, Cuong Manh Nguyen
Semi-supervised classification of radiology images with NoTeacher: A teacher that is not mean. Medical Image Anal. 73: 102148 (2021) - [j3]Mohamed Ragab
Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction. IEEE Trans. Ind. Informatics 17(8): 5239-5249 (2021) - [j2]Lile Cai
Exploring Spatial Diversity for Region-Based Active Learning. IEEE Trans. Image Process. 30: 8702-8712 (2021) - [c23]Wanyue Zhang, Xun Xu, Fayao Liu, Le Zhang, Chuan Sheng Foo:
On Automatic Data Augmentation for 3D Point Cloud Classification. BMVC 2021: 392 - [c22]Lile Cai, Xun Xu, Jun Hao Liew, Chuan Sheng Foo:
Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs. CVPR 2021: 10988-10997 - [c21]Kangkang Lu, Cuong Manh Nguyen, Xun Xu, Kiran Chari, Yu Jing Goh, Chuan-Sheng Foo:
ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity. ICLR 2021 - [c20]Brian Formento, See-Kiong Ng, Chuan-Sheng Foo:
Special Symbol Attacks On NLP Systems. IJCNN 2021: 1-8 - [c19]Xinyi Xu, Zhaoxuan Wu, Chuan Sheng Foo, Bryan Kian Hsiang Low:
Validation Free and Replication Robust Volume-based Data Valuation. NeurIPS 2021: 10837-10848 - [c18]Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, Bryan Kian Hsiang Low:
Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning. NeurIPS 2021: 16104-16117 - [i23]Govind Narasimman, Kangkang Lu, Arun Raja, Chuan Sheng Foo, Mohamed M. Sabry Aly, Jie Lin, Vijay Chandrasekhar:
A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity Recognition. CoRR abs/2101.04859 (2021) - [i22]Xian Shi, Xun Xu, Ke Chen, Lile Cai, Chuan Sheng Foo, Kui Jia:
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach. CoRR abs/2101.06931 (2021) - [i21]Fayao Liu, Guosheng Lin, Chuan-Sheng Foo:
Point Discriminative Learning for Unsupervised Representation Learning on 3D Point Clouds. CoRR abs/2108.02104 (2021) - [i20]Balagopal Unnikrishnan, Cuong Manh Nguyen, Shafa Balaram, Chao Li, Chuan Sheng Foo, Pavitra Krishnaswamy:
Semi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean. CoRR abs/2108.04423 (2021) - [i19]Astha Garg, Wenyu Zhang, Jules Samaran, Savitha Ramasamy, Chuan-Sheng Foo:
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series. CoRR abs/2109.11428 (2021) - [i18]Chaitanya K. Joshi, Fayao Liu, Xu Xun, Jie Lin, Chuan-Sheng Foo:
On Representation Knowledge Distillation for Graph Neural Networks. CoRR abs/2111.04964 (2021) - [i17]Wanyue Zhang, Xun Xu, Fayao Liu, Le Zhang, Chuan-Sheng Foo:
On Automatic Data Augmentation for 3D Point Cloud Classification. CoRR abs/2112.06029 (2021) - [i16]Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo, Bryan Kian Hsiang Low:
Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards. CoRR abs/2112.09327 (2021) - 2020
- [j1]Anran Wang
Holistic Multi-Modal Memory Network for Movie Question Answering. IEEE Trans. Image Process. 29: 489-499 (2020) - [c17]Yubo Hou, Zhenghua Chen, Min Wu
Mahalanobis Distance Based Adversarial Network for Anomaly Detection. ICASSP 2020: 3192-3196 - [c16]Yasin Yazici, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Vijay Chandrasekhar:
Empirical Analysis Of Overfitting And Mode Drop In Gan Training. ICIP 2020: 1651-1655 - [c15]Balagopal Unnikrishnan, Cuong Manh Nguyen, Shafa Balaram, Chuan Sheng Foo, Pavitra Krishnaswamy:
Semi-supervised Classification of Diagnostic Radiographs with NoTeacher: A Teacher that is Not Mean. MICCAI (1) 2020: 624-634 - [i15]Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Chuan-Sheng Foo, Rio Yokota:
Scalable and Practical Natural Gradient for Large-Scale Deep Learning. CoRR abs/2002.06015 (2020) - [i14]Saisubramaniam Gopalakrishnan, Pranshu Ranjan Singh, Yasin Yazici, Chuan-Sheng Foo, Vijay Chandrasekhar, Arulmurugan Ambikapathi:
Classification Representations Can be Reused for Downstream Generations. CoRR abs/2004.07543 (2020) - [i13]Yasin Yazici, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap
Empirical Analysis of Overfitting and Mode Drop in GAN Training. CoRR abs/2006.14265 (2020)
2010 – 2019
- 2019
- [c14]Lile Cai, Bin Zhao, Zhe Wang, Jie Lin, Chuan Sheng Foo, Mohamed M. Sabry Aly, Vijay Chandrasekhar:
MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors. CVPR 2019: 9356-9364 - [c13]Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras:
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile. ICLR (Poster) 2019 - [c12]Yasin Yazici, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras, Vijay Chandrasekhar:
The Unusual Effectiveness of Averaging in GAN Training. ICLR (Poster) 2019 - [c11]Kangkang Lu, Chuan-Sheng Foo, Kah Kuan Teh, Huy Dat Tran, Vijay Ramaseshan Chandrasekhar:
Semi-Supervised Audio Classification with Consistency-Based Regularization. INTERSPEECH 2019: 3654-3658 - [c10]Lile Cai, Anne-Maelle Barneche, Arthur Herbout, Chuan Sheng Foo, Jie Lin, Vijay Ramaseshan Chandrasekhar, Mohamed M. Sabry Aly:
TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks. ISLPED 2019: 1-6 - [c9]Khalil Ouardini, Huijuan Yang
Towards Practical Unsupervised Anomaly Detection on Retinal Images. DART/MIL3ID@MICCAI 2019: 225-234 - [c8]Rahul Dutta, Salahuddin Raju, Ashish James
Learning of Multi-Dimensional Analog Circuits Through Generative Adversarial Network (GAN). SoCC 2019: 394-399 - [i12]Yasin Yazici, Bruno Lecouat, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras, Vijay Chandrasekhar:
Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions. CoRR abs/1902.03444 (2019) - [i11]Wei-Hong Li, Chuan-Sheng Foo, Hakan Bilen:
Learning to Impute: A General Framework for Semi-supervised Learning. CoRR abs/1912.10364 (2019) - 2018
- [c7]Houssam Zenati, Manon Romain, Chuan-Sheng Foo, Bruno Lecouat, Vijay Chandrasekhar:
Adversarially Learned Anomaly Detection. ICDM 2018: 727-736 - [c6]Bruno Lecouat, Chuan Sheng Foo, Houssam Zenati, Vijay Ramaseshan Chandrasekhar:
Semi-Supervised Learning With GANs: Revisiting Manifold Regularization. ICLR (Workshop) 2018 - [i10]Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, Vijay Ramaseshan Chandrasekhar:
Efficient GAN-Based Anomaly Detection. CoRR abs/1802.06222 (2018) - [i9]Bruno Lecouat, Chuan Sheng Foo, Houssam Zenati, Vijay Ramaseshan Chandrasekhar:
Semi-Supervised Learning with GANs: Revisiting Manifold Regularization. CoRR abs/1805.08957 (2018) - [i8]Yasin Yazici, Chuan Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras, Vijay Chandrasekhar:
The Unusual Effectiveness of Averaging in GAN Training. CoRR abs/1806.04498 (2018) - [i7]Panayotis Mertikopoulos, Houssam Zenati, Bruno Lecouat, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras:
Mirror descent in saddle-point problems: Going the extra (gradient) mile. CoRR abs/1807.02629 (2018) - [i6]Bruno Lecouat, Chuan Sheng Foo, Houssam Zenati, Vijay Chandrasekhar:
Manifold regularization with GANs for semi-supervised learning. CoRR abs/1807.04307 (2018) - [i5]Anran Wang, Anh Tuan Luu, Chuan-Sheng Foo, Hongyuan Zhu, Yi Tay, Vijay Chandrasekhar:
Holistic Multi-modal Memory Network for Movie Question Answering. CoRR abs/1811.04595 (2018) - [i4]Léo Laugier, Daniil Bash, Jose Recatala, Hong Kuan Ng, Savitha Ramasamy, Chuan-Sheng Foo, Vijay Ramaseshan Chandrasekhar, Kedar Hippalgaonkar:
Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional materials. CoRR abs/1811.06219 (2018) - [i3]Lile Cai, Anne-Maelle Barneche, Arthur Herbout, Chuan Sheng Foo, Jie Lin, Vijay Ramaseshan Chandrasekhar, Mohamed M. Sabry:
TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks. CoRR abs/1811.12065 (2018) - [i2]Houssam Zenati, Manon Romain, Chuan Sheng Foo, Bruno Lecouat, Vijay Ramaseshan Chandrasekhar:
Adversarially Learned Anomaly Detection. CoRR abs/1812.02288 (2018) - [i1]Bruno Lecouat, Ken Chang, Chuan-Sheng Foo, Balagopal Unnikrishnan, James M. Brown, Houssam Zenati, Andrew Beers, Vijay Chandrasekhar, Jayashree Kalpathy-Cramer, Pavitra Krishnaswamy:
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images. CoRR abs/1812.07832 (2018) - 2017
- [b1]Chuan-Sheng Foo:
Machine learning models for analyzing chromatin and RNA structure data. Stanford University, USA, 2017
2000 – 2009
- 2009
- [c5]Xiaoli Li
Searching for Rising Stars in Bibliography Networks. DASFAA 2009: 288-292 - [c4]Chuong B. Do, Quoc V. Le, Chuan-Sheng Foo:
Proximal regularization for online and batch learning. ICML 2009: 257-264 - [c3]Chuan-Sheng Foo, Chuong B. Do, Andrew Y. Ng:
A majorization-minimization algorithm for (multiple) hyperparameter learning. ICML 2009: 321-328 - 2008
- [c2]Chuong B. Do, Chuan-Sheng Foo, Serafim Batzoglou:
A max-margin model for efficient simultaneous alignment and folding of RNA sequences. ISMB 2008: 68-76 - 2007
- [c1]Chuong B. Do, Chuan-Sheng Foo, Andrew Y. Ng:
Efficient multiple hyperparameter learning for log-linear models. NIPS 2007: 377-384
Coauthor Index
Vijay Chandrasekhar 0001
aka: Vijay Ramaseshan Chandrasekhar
aka: Vijay Ramaseshan Chandrasekhar
Kian Hsiang Low
aka: Bryan Kian Hsiang Low
aka: Bryan Kian Hsiang Low
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