Generalization

التعميم

قدرة النموذج على تقديم تنبؤات صحيحة لبيانات جديدة ومستقلة خارج نطاق التدريب.

Generalization

Also translated asالقدرة على التعميم، الشمولية النموذجية

First appears in this corpus in: Computing Machinery and Intelligence (1950)

Appears in these papers

A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting1997 in the sky ✦
Decoupled Weight Decay Regularization2019 in the sky ✦
Intriguing Properties of Neural Networks2014 in the sky ✦
Intriguing Properties of Neural Networks2014 in the sky ✦
A General Reinforcement Learning Algorithm That Masters Chess, Shogi, and Go Through Self-Play2018 in the sky ✦
Neural Networks and the Bias/Variance Dilemma1992 in the sky ✦
Neural Networks and the Bias/Variance Dilemma1992 in the sky ✦
C4.5: Programs for Machine Learning1993 in the sky ✦
Evaluating Large Language Models Trained on Code2021 in the sky ✦
Cyclical Learning Rates for Training Neural Networks2017 in the sky ✦
Deep Learning2015 in the sky ✦
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data2024 in the sky ✦
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data2024 in the sky ✦
Learning Distributed Representations of Concepts1986 in the sky ✦
Learning Distributed Representations of Concepts1986 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 ✦
Mastering Diverse Domains Through World Models2023 in the sky ✦
Explaining and Harnessing Adversarial Examples2015 in the sky ✦
Explaining and Harnessing Adversarial Examples2015 in the sky ✦
Finetuned Language Models Are Zero-Shot Learners2022 in the sky ✦
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding2018 in the sky ✦
Going Deeper with Convolutions2014 in the sky ✦
Improving Language Understanding by Generative Pre-Training2018 in the sky ✦
Language Models Are Unsupervised Multitask Learners2019 in the sky ✦
Greedy Function Approximation: A Gradient Boosting Machine2001 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 ✦
Growing Neural Cellular Automata2020 in the sky ✦
Growing Neural Cellular Automata2020 in the sky ✦
Hindsight Experience Replay2017 in the sky ✦
Curiosity-Driven Exploration by Self-Supervised Prediction2017 in the sky ✦
Curiosity-Driven Exploration by Self-Supervised Prediction2017 in the sky ✦
Distilling the Knowledge in a Neural Network2015 in the sky ✦
When Does Label Smoothing Help?2019 in the sky ✦
When Does Label Smoothing Help?2019 in the sky ✦
Latent Dirichlet Allocation2003 in the sky ✦
LIMA: Less Is More for Alignment2023 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 ✦
Visualizing the Loss Landscape of Neural Nets2018 in the sky ✦
Visualizing the Loss Landscape of Neural Nets2018 in the sky ✦
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks2019 in the sky ✦
mixup: Beyond Empirical Risk Minimization2018 in the sky ✦
mixup: Beyond Empirical Risk Minimization2018 in the sky ✦
A Neural Probabilistic Language Model2003 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 ✦
Neural Tangent Kernel: Convergence and Generalization in Neural Networks2018 in the sky ✦
Neural Tangent Kernel: Convergence and Generalization in Neural Networks2018 in the sky ✦
Open X-Embodiment: Robotic Learning Datasets and RT-X Models2024 in the sky ✦
Open X-Embodiment: Robotic Learning Datasets and RT-X Models2024 in the sky ✦
Optimal Brain Damage1989 in the sky ✦
A Theory of the Learnable1984 in the sky ✦
A Theory of the Learnable1984 in the sky ✦
Textbooks Are All You Need2023 in the sky ✦
RAFT: Recurrent All-Pairs Field Transforms for Optical Flow2020 in the sky ✦
RAFT: Recurrent All-Pairs Field Transforms for Optical Flow2020 in the sky ✦
Random Forests2001 in the sky ✦
RT-1: Robotics Transformer for Real-World Control at Scale2022 in the sky ✦
RT-1: Robotics Transformer for Real-World Control at Scale2022 in the sky ✦
Some Studies in Machine Learning Using the Game of Checkers1959 in the sky ✦
Some Studies in Machine Learning Using the Game of Checkers1959 in the sky ✦
Scaling Laws for Neural Language Models2020 in the sky ✦
Self-Instruct: Aligning Language Models with Self-Generated Instructions2022 in the sky ✦
Self-Instruct: Aligning Language Models with Self-Generated Instructions2022 in the sky ✦
Sharpness-Aware Minimization for Efficiently Improving Generalization2021 in the sky ✦
Sharpness-Aware Minimization for Efficiently Improving Generalization2021 in the sky ✦
Signature Verification Using a "Siamese" Time Delay Neural Network1993 in the sky ✦
Support-Vector Networks1995 in the sky ✦
Temporal Difference Learning and TD-Gammon1995 in the sky ✦
Computing Machinery and Intelligence1950 in the sky ✦
On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities1971 in the sky ✦
On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities1971 in the sky ✦
Very Deep Convolutional Networks for Large-Scale Image Recognition2014 in the sky ✦
The Strength of Weak Learnability1990 in the sky ✦
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision2023 in the sky ✦
Wide & Deep Learning for Recommender Systems2016 in the sky ✦
Wide & Deep Learning for Recommender Systems2016 in the sky ✦
World Models2018 in the sky ✦
You Only Look Once: Unified, Real-Time Object Detection2015 in the sky ✦