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Regularization
20 directly classified papers
Papers per year
2013: 1
2016: 2
2017: 2
2018: 1
2019: 3
2021: 4
2022: 4
2023: 2
2024: 1
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
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