• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 64 matches

• 2021
• 
  Tobias Hatt, Stefan Feuerriegel:
  Estimating Average Treatment Effects via Orthogonal Regularization. CoRR abs/2101.08490 (2021)
• 
  Oussama Dhifallah, Yue M. Lu:
  On the Inherent Regularization Effects of Noise Injection During Training. CoRR abs/2102.07379 (2021)
• 2020
• 
  Soojin Jang, Kyohoon Jin, Junhyeok An, Young Bin Kim:
  Regional Patch-Based Feature Interpolation Method for Effective Regularization. IEEE Access 8: 33658-33665 (2020)
• 
  Juncai He, Xiaodong Jia, Jinchao Xu, Lian Zhang, Liang Zhao:
  Make ℓ ₁ regularization effective in training sparse CNN. Comput. Optim. Appl. 77(1): 163-182 (2020)
• 
  Dennis Amelunxen, Martin Lotz, Jake Walvin:
  Effective Condition Number Bounds for Convex Regularization. IEEE Trans. Inf. Theory 66(4): 2501-2516 (2020)
• 
  Ivan Provilkov, Dmitrii Emelianenko, Elena Voita:
  BPE-Dropout: Simple and Effective Subword Regularization. ACL 2020: 1882-1892
• 
  Wei Hu, Zhiyuan Li, Dingli Yu:
  Simple and Effective Regularization Methods for Training on Noisily Labeled Data with Generalization Guarantee. ICLR 2020
• 
  Cheolhyoung Lee, Kyunghyun Cho, Wanmo Kang:
  Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models. ICLR 2020
• 
  Colin Wei, Sham M. Kakade, Tengyu Ma:
  The Implicit and Explicit Regularization Effects of Dropout. ICML 2020: 10181-10192
• 
  Pietro G. Di Stefano, James M. Hickey, Vlasios Vasileiou:
  Counterfactual fairness: removing direct effects through regularization. CoRR abs/2002.10774 (2020)
• 
  Colin Wei, Sham M. Kakade, Tengyu Ma:
  The Implicit and Explicit Regularization Effects of Dropout. CoRR abs/2002.12915 (2020)
• 
  Yue Ju, Tianshi Chen, Bi-Qiang Mu, Lennart Ljung:
  On Effects of Condition Number of Regression Matrix upon Hyper-parameter Estimators for Kernel-based Regularization Methods. CoRR abs/2003.13435 (2020)
• 
  Eustasio del Barrio, Jean-Michel Loubes:
  The statistical effect of entropic regularization in optimal transportation. CoRR abs/2006.05199 (2020)
• 
  Yigitcan Kaya, Sanghyun Hong, Tudor Dumitras:
  On the Effectiveness of Regularization Against Membership Inference Attacks. CoRR abs/2006.05336 (2020)
• 
  Masanari Kimura, Ryohei Izawa:
  Density Fixing: Simple yet Effective Regularization Method based on the Class Prior. CoRR abs/2007.03899 (2020)
• 
  Santiago Gonzalez, Risto Miikkulainen:
  Effective Regularization Through Loss-Function Metalearning. CoRR abs/2010.00788 (2020)
• 
  Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu:
  Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate. CoRR abs/2011.02538 (2020)
• 
  Olivier Rukundo:
  Effect of the regularization hyperparameter on deep learning-based segmentation in LGE-MRI. CoRR abs/2012.05661 (2020)
• 2019
• 
  Xueqi Ma, Dapeng Tao, Weifeng Liu:
  Effective human action recognition by combining manifold regularization and pairwise constraints. Multim. Tools Appl. 78(10): 13313-13329 (2019)
• 
  Lulu Tang, Zhi-Xin Yang, Kui Jia:
  Canonical Correlation Analysis Regularization: An Effective Deep Multiview Learning Baseline for RGB-D Object Recognition. IEEE Trans. Cogn. Dev. Syst. 11(1): 107-118 (2019)
• 
  Motoki Sato, Jun Suzuki, Shun Kiyono:
  Effective Adversarial Regularization for Neural Machine Translation. ACL (1) 2019: 204-210
• 
  Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma:
  The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects. ICML 2019: 7654-7663
• 
  Yuanzhi Li, Colin Wei, Tengyu Ma:
  Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks. NeurIPS 2019: 11669-11680
• 
  Nicolas Couellan:
  The coupling effect of Lipschitz regularization in deep neural networks. CoRR abs/1904.06253 (2019)
• 
  Gabriel Grand, Yonatan Belinkov:
  Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects. CoRR abs/1906.08430 (2019)
• 
  Yuanzhi Li, Colin Wei, Tengyu Ma:
  Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks. CoRR abs/1907.04595 (2019)
• 
  Cheolhyoung Lee, Kyunghyun Cho, Wanmo Kang:
  Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models. CoRR abs/1909.11299 (2019)
• 
  Ivan Provilkov, Dmitrii Emelianenko, Elena Voita:
  BPE-Dropout: Simple and Effective Subword Regularization. CoRR abs/1910.13267 (2019)
• 
  Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu:
  Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy. CoRR abs/1911.09307 (2019)
• 2018
• 
  Yong Zhao, Hong Qin, Xueying Zeng, Junli Xu, Junyu Dong:
  Robust and effective mesh denoising using L0 sparse regularization. Comput. Aided Des. 101: 82-97 (2018)

skipping 34 more matches