Regularization

الضبط الهيكلي

أساليب رياضية تضاف لمنع التعلم الزائد (الإفراط) وضمان قدرة النموذج على التعميم لبيانات جديدة.

Regularization

Also translated asمعالجة الإفراط، تسوية التعميم البنيوي

First appears in this corpus in: Ridge Regression: Biased Estimation for Nonorthogonal Problems (1970)

Appears in these papers

A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting1997 in the sky ✦
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization2011 in the sky ✦
Decoupled Weight Decay Regularization2019 in the sky ✦
Intriguing Properties of Neural Networks2014 in the sky ✦
Mastering the Game of Go Without Human Knowledge2017 in the sky ✦
Bagging Predictors1996 in the sky ✦
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift2015 in the sky ✦
Practical Bayesian Optimization of Machine Learning Algorithms2012 in the sky ✦
Neural Networks and the Bias/Variance Dilemma1992 in the sky ✦
BPR: Bayesian Personalized Ranking from Implicit Feedback2009 in the sky ✦
BPR: Bayesian Personalized Ranking from Implicit Feedback2009 in the sky ✦
Dynamic Routing Between Capsules2017 in the sky ✦
A ConvNet for the 2020s2022 in the sky ✦
Conservative Q-Learning for Offline Reinforcement Learning2020 in the sky ✦
Conservative Q-Learning for Offline Reinforcement Learning2020 in the sky ✦
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks2017 in the sky ✦
Deep Reinforcement Learning from Human Preferences2017 in the sky ✦
Training Data-Efficient Image Transformers & Distillation Through Attention2021 in the sky ✦
Training Data-Efficient Image Transformers & Distillation Through Attention2021 in the sky ✦
Extracting and Composing Robust Features with Denoising Autoencoders2008 in the sky ✦
Deep Interest Network for Click-Through Rate Prediction2018 in the sky ✦
Deep Interest Network for Click-Through Rate Prediction2018 in the sky ✦
Reconciling Modern Machine-Learning Practice and the Classical Bias–Variance Trade-Off2019 in the sky ✦
Reconciling Modern Machine-Learning Practice and the Classical Bias–Variance Trade-Off2019 in the sky ✦
Dropout: A Simple Way to Prevent Neural Networks from Overfitting2014 in the sky ✦
Explaining and Harnessing Adversarial Examples2015 in the sky ✦
Explaining and Harnessing Adversarial Examples2015 in the sky ✦
Graph Attention Networks2018 in the sky ✦
Gaussian Processes for Machine Learning2006 in the sky ✦
Semi-Supervised Classification with Graph Convolutional Networks2017 in the sky ✦
Gaussian Error Linear Units (GELUs)2016 in the sky ✦
Going Deeper with Convolutions2014 in the sky ✦
Improving Language Understanding by Generative Pre-Training2018 in the sky ✦
Greedy Function Approximation: A Gradient Boosting Machine2001 in the sky ✦
On the Difficulty of Training Recurrent Neural Networks2013 in the sky ✦
Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets2022 in the sky ✦
Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets2022 in the sky ✦
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization2018 in the sky ✦
Distilling the Knowledge in a Neural Network2015 in the sky ✦
When Does Label Smoothing Help?2019 in the sky ✦
Regression Shrinkage and Selection via the Lasso1996 in the sky ✦
Regression Shrinkage and Selection via the Lasso1996 in the sky ✦
Latent Dirichlet Allocation2003 in the sky ✦
Learning to Summarize from Human Feedback2020 in the sky ✦
"Why Should I Trust You?": Explaining the Predictions of Any Classifier2016 in the sky ✦
Symbolic Discovery of Optimization Algorithms2023 in the sky ✦
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks2019 in the sky ✦
Mask R-CNN2017 in the sky ✦
Matrix Factorization Techniques for Recommender Systems2009 in the sky ✦
Matrix Factorization Techniques for Recommender Systems2009 in the sky ✦
mixup: Beyond Empirical Risk Minimization2018 in the sky ✦
mixup: Beyond Empirical Risk Minimization2018 in the sky ✦
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications2017 in the sky ✦
Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer2022 in the sky ✦
A Neural Probabilistic Language Model2003 in the sky ✦
No Free Lunch Theorems for Optimization1997 in the sky ✦
No Free Lunch Theorems for Optimization1997 in the sky ✦
Optimal Brain Damage1989 in the sky ✦
Optimal Brain Damage1989 in the sky ✦
Perceiver: General Perception with Iterative Attention2021 in the sky ✦
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation2017 in the sky ✦
Forecasting at Scale2018 in the sky ✦
Forecasting at Scale2018 in the sky ✦
Rectified Linear Units Improve Restricted Boltzmann Machines2010 in the sky ✦
Ridge Regression: Biased Estimation for Nonorthogonal Problems1970 in the sky ✦
Ridge Regression: Biased Estimation for Nonorthogonal Problems1970 in the sky ✦
SGDR: Stochastic Gradient Descent with Warm Restarts2017 in the sky ✦
A Unified Approach to Interpreting Model Predictions2017 in the sky ✦
Sharpness-Aware Minimization for Efficiently Improving Generalization2021 in the sky ✦
Sharpness-Aware Minimization for Efficiently Improving Generalization2021 in the sky ✦
Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images1996 in the sky ✦
Analyzing and Improving the Image Quality of StyleGAN2020 in the sky ✦
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity2022 in the sky ✦
TD3: Addressing Function Approximation Error in Actor-Critic Methods2018 in the sky ✦
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision2023 in the sky ✦
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision2023 in the sky ✦
Robust Speech Recognition via Large-Scale Weak Supervision2022 in the sky ✦
XGBoost: A Scalable Tree Boosting System2016 in the sky ✦