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Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm

Oct 18, 2021
Shaoyi Huang, Dongkuan Xu, Ian E. H. Yen, Sung-en Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Hang Liu, Caiwen Ding

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Rethinking Network Pruning -- under the Pre-train and Fine-tune Paradigm

Apr 18, 2021
Dongkuan Xu, Ian E. H. Yen, Jinxi Zhao, Zhibin Xiao

* 7 pages, 6 figures, 1 table 

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Minimizing FLOPs to Learn Efficient Sparse Representations

Apr 12, 2020
Biswajit Paria, Chih-Kuan Yeh, Ian E. H. Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos

* Published at ICLR 2020 

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Representer Point Selection for Explaining Deep Neural Networks

Nov 23, 2018
Chih-Kuan Yeh, Joon Sik Kim, Ian E. H. Yen, Pradeep Ravikumar

* NIPS 2018 

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Word Mover's Embedding: From Word2Vec to Document Embedding

Oct 30, 2018
Lingfei Wu, Ian E. H. Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock

* EMNLP'18 Camera-Ready Version 

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Learning Tensor Latent Features

Oct 10, 2018
Sung-En Chang, Xun Zheng, Ian E. H. Yen, Pradeep Ravikumar, Rose Yu

* 10 pages, 3 figures 

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Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability

Sep 19, 2018
Lingfei Wu, Ian E. H. Yen, Jie Chen, Rui Yan

* KDD16, Oral Paper, Add Code Link for generating Random Binning Features 

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Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators

Jan 23, 2015
Kai Zhong, Ian E. H. Yen, Inderjit S. Dhillon, Pradeep Ravikumar

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