Regularization

On the Origin of Implicit Regularization in Stochastic Gradient Descent ICLR 2021 Regularising Fisher Information Improves Cross-lingual Generalisation EMNLP 2021 Regularization in ResNet with Stochastic Depth NeurIPS 2021 Regularizing Generative Adversarial Networks Under Limited Data CVPR 2021 Regularizing Neural Networks via Adversarial Model Perturbation CVPR 2021 RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening CVPR 2021 Shape Matters: Understanding the Implicit Bias of the Noise Covariance COLT 2021 Simple and Effective Stochastic Neural Networks AAAI 2021 The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization NeurIPS 2021 T-vMF Similarity for Regularizing Intra-Class Feature Distribution CVPR 2021 Understanding Decoupled and Early Weight Decay AAAI 2021 Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks AAAI 2020 Consistency Regularization for Certified Robustness of Smoothed Classifiers NeurIPS 2020 Explicit Regularisation in Gaussian Noise Injections NeurIPS 2020 Feature Variance Regularization: A Simple Way to Improve the Generalizability of Neural Networks AAAI 2020 Group-Wise Dynamic Dropout Based on Latent Semantic Variations AAAI 2020 Learning from Label Proportions with Consistency Regularization ACML 2020 Learning Interpretable Models using Soft Integrity Constraints ACML 2020 Local Regularizer Improves Generalization AAAI 2020 Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models ICLR 2020 MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles NeurIPS 2020 Network as Regularization for Training Deep Neural Networks: Framework, Model and Performance AAAI 2020 On the Regularization Properties of Structured Dropout CVPR 2020 Reducing Transformer Depth on Demand with Structured Dropout ICLR 2020 Regional Tree Regularization for Interpretability in Deep Neural Networks AAAI 2020