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Regularization

20 directly classified papers

Papers per year

Papers

Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines NIPS 2024
DropMessage: Unifying Random Dropping for Graph Neural Networks AAAI 2023
TokenDrop + BucketSampler: Towards Efficient Padding-free Fine-tuning of Language Models EMNLP 2023
Neuron with Steady Response Leads to Better Generalization NIPS 2022
Prediction Difference Regularization against Perturbation for Neural Machine Translation ACL 2022
PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks AAAI 2022
Dynamically Refined Regularization for Improving Cross-corpora Hate Speech Detection ACL 2022
Regularizing Generative Adversarial Networks Under Limited Data CVPR 2021
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks AAAI 2021
Regularizing Neural Networks via Adversarial Model Perturbation CVPR 2021
On Linear Stability of SGD and Input-Smoothness of Neural Networks NIPS 2021
P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification CVPR 2019
Tangent-Normal Adversarial Regularization for Semi-Supervised Learning CVPR 2019
A Logic-Driven Framework for Consistency of Neural Models EMNLP 2019
Removing the Feature Correlation Effect of Multiplicative Noise NIPS 2018
Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation CVPR 2017
S3Pool: Pooling With Stochastic Spatial Sampling CVPR 2017
DisturbLabel: Regularizing CNN on the Loss Layer CVPR 2016
Improved Dropout for Shallow and Deep Learning NIPS 2016
Adaptive dropout for training deep neural networks NIPS 2013