Regularization

Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success JMLR 2020 Regularizing Black-box Models for Improved Interpretability NeurIPS 2020 Regularizing Class-Wise Predictions via Self-Knowledge Distillation CVPR 2020 Regularizing Neural Networks via Minimizing Hyperspherical Energy CVPR 2020 Semantic Label Smoothing for Sequence to Sequence Problems EMNLP 2020 Topic Balancing with Additive Regularization of Topic Models ACL 2020 Towards Understanding the Regularization of Adversarial Robustness on Neural Networks ICML 2020 Understanding and Robustifying Differentiable Architecture Search ICLR 2020 VECA: A Method for Detecting Overfitting in Neural Networks (Student Abstract) AAAI 2020 Capacity Control of ReLU Neural Networks by Basis-Path Norm AAAI 2019 Cost-sensitive Regularization for Label Confusion-aware Event Detection ACL 2019 Guided Dropout AAAI 2019 MixUp as Locally Linear Out-of-Manifold Regularization AAAI 2019 On Implicit Filter Level Sparsity in Convolutional Neural Networks CVPR 2019 Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel NeurIPS 2019 Regularized Weighted Low Rank Approximation NeurIPS 2019 Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters AAAI 2019 Using Benson’s Algorithm for Regularization Parameter Tracking AAAI 2019 Avoiding Speaker Overfitting in End-to-End DNNs Using Raw Waveform for Text-Independent Speaker Verification Interspeech 2018 Connecting Optimization and Regularization Paths NeurIPS 2018 Dropout Training, Data-dependent Regularization, and Generalization Bounds ICML 2018 Learning towards Minimum Hyperspherical Energy NeurIPS 2018 Early stopping for kernel boosting algorithms: A general analysis with localized complexities NeurIPS 2017 Information-theoretic analysis of generalization capability of learning algorithms NeurIPS 2017 On Optimal Generalizability in Parametric Learning NeurIPS 2017