{"total":33,"rows":[{"id":"2106.14448","title":"R-Drop: Regularized Dropout for Neural Networks","created":"2021-06-28","cited_by_count":202,"area":null},{"id":"2201.03299","title":"Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks","created":"2022-01-10","cited_by_count":55,"area":null},{"id":"2106.00149","title":"HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalization","created":"2021-05-31","cited_by_count":32,"area":null},{"id":"2210.12928","title":"GFlowOut: Dropout with Generative Flow Networks","created":"2022-10-24","cited_by_count":22,"area":null},{"id":"2310.08847","title":"On the Over-Memorization During Natural, Robust and Catastrophic Overfitting","created":"2023-10-13","cited_by_count":20,"area":null},{"id":"2103.14026","title":"AutoLoss-Zero: Searching Loss Functions from Scratch for Generic Tasks","created":"2021-03-25","cited_by_count":19,"area":null},{"id":"2403.11395","title":"Automated data processing and feature engineering for deep learning and big data applications: a survey","created":"2024-03-18","cited_by_count":14,"area":null},{"id":"2109.07383","title":"RankNAS: Efficient Neural Architecture Search by Pairwise Ranking","created":"2021-09-15","cited_by_count":12,"area":null},{"id":"2110.00046","title":"SpliceOut: A Simple and Efficient Audio Augmentation Method","created":"2021-09-30","cited_by_count":7,"area":null},{"id":"2205.08358","title":"Perturbation of Deep Autoencoder Weights for Model Compression and Classification of Tabular Data","created":"2022-05-17","cited_by_count":7,"area":null},{"id":"2302.06112","title":"How to Use Dropout Correctly on Residual Networks with Batch Normalization","created":"2023-02-13","cited_by_count":7,"area":null},{"id":"2405.13672","title":"Advancing Spiking Neural Networks towards Multiscale Spatiotemporal Interaction Learning","created":"2024-05-22","cited_by_count":7,"area":null},{"id":"2201.09209","title":"Weight Expansion: A New Perspective on Dropout and Generalization","created":"2022-01-23","cited_by_count":6,"area":null},{"id":"2104.04692","title":"Not All Attention Is All You Need","created":"2021-04-10","cited_by_count":5,"area":null},{"id":"2312.13435","title":"The Adaptive Arms Race: Redefining Robustness in AI Security","created":"2023-12-20","cited_by_count":5,"area":null},{"id":"2204.01662","title":"Evolving Neural Selection with Adaptive Regularization","created":"2022-04-04","cited_by_count":4,"area":null},{"id":"2204.02027","title":"A Survey on Dropout Methods and Experimental Verification in Recommendation","created":"2022-04-05","cited_by_count":4,"area":null},{"id":"2210.05883","title":"AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning","created":"2022-10-12","cited_by_count":4,"area":null},{"id":"2103.15425","title":"FocusedDropout for Convolutional Neural Network","created":"2021-03-29","cited_by_count":3,"area":null},{"id":"2106.13749","title":"Jitter: Random Jittering Loss Function","created":"2021-06-25","cited_by_count":3,"area":null},{"id":"2111.09838","title":"On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications","created":"2021-11-11","cited_by_count":2,"area":null},{"id":"2409.17370","title":"The Overfocusing Bias of Convolutional Neural Networks: A Saliency-Guided Regularization Approach","created":"2024-09-25","cited_by_count":2,"area":null},{"id":"2106.01805","title":"Partial Graph Reasoning for Neural Network Regularization","created":"2021-06-03","cited_by_count":1,"area":null},{"id":"2206.00913","title":"Improving the Robustness and Generalization of Deep Neural Network with Confidence Threshold Reduction","created":"2022-06-02","cited_by_count":1,"area":null},{"id":"2304.09369","title":"ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision","created":"2023-04-19","cited_by_count":1,"area":null},{"id":"2103.15383","title":"Selective Output Smoothing Regularization: Regularize Neural Networks by Softening Output Distributions","created":"2021-03-29","cited_by_count":0,"area":null},{"id":"2112.10543","title":"Spiral Language Modeling","created":"2021-12-20","cited_by_count":0,"area":null},{"id":"2211.10944","title":"Feature Weaken: Vicinal Data Augmentation for Classification","created":"2022-11-20","cited_by_count":0,"area":null},{"id":"2305.13447","title":"Regularization Through Simultaneous Learning: A Case Study on Plant Classification","created":"2023-05-22","cited_by_count":0,"area":null},{"id":"2307.15150","title":"R-Block: Regularized Block of Dropout for convolutional networks","created":"2023-07-27","cited_by_count":0,"area":null},{"id":"2408.13426","title":"Optimal Layer Selection for Latent Data Augmentation","created":"2024-08-24","cited_by_count":0,"area":null},{"id":"2409.16630","title":"Stochastic Subsampling With Average Pooling","created":"2024-09-25","cited_by_count":0,"area":null},{"id":"2509.18165","title":"Self Identity Mapping","created":"2025-09-17","cited_by_count":0,"area":null}],"next_cursor":null}