Difference between revisions of "Out-of-Distribution (OOD) Generalization"
m |
m |
||
| Line 15: | Line 15: | ||
* [[Math for Intelligence#Mathematical Reasoning|Mathematical Reasoning]] | * [[Math for Intelligence#Mathematical Reasoning|Mathematical Reasoning]] | ||
* [[Transfer Learning]] | * [[Transfer Learning]] | ||
| + | * [[Causation vs. Correlation]] | ||
* [https://arxiv.org/abs/2108.13624 Towards Out-Of-Distribution Generalization: A Survey] | * [https://arxiv.org/abs/2108.13624 Towards Out-Of-Distribution Generalization: A Survey] | ||
* [https://arxiv.org/abs/2106.04496 Towards a Theoretical Framework of Out-of-Distribution Generalization] | * [https://arxiv.org/abs/2106.04496 Towards a Theoretical Framework of Out-of-Distribution Generalization] | ||
Revision as of 17:11, 27 May 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- In-Context Learning (ICL) ... Context ... Out-of-Distribution (OOD) Generalization
- Singularity ... Sentience ... AGI ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
- Mathematical Reasoning
- Transfer Learning
- Causation vs. Correlation
- Towards Out-Of-Distribution Generalization: A Survey
- Towards a Theoretical Framework of Out-of-Distribution Generalization
- Out-of-Distribution Generalization via Risk Extrapolation
- Using Interventions to Improve Out-of-Distribution Generalization of ....
- How Reliable Are Out-of-Distribution Generalization Methods for Medical ....
- Meta-Causal Feature Learning for Out-of-Distribution Generalization ...
Out-of-Distribution (OOD) generalization refers to the ability of a machine learning model to generalize to new data that comes from a different distribution than the training data. This is a challenging problem because the testing distribution is unknown and different from the training distribution. There are several methods for improving out-of-distribution generalization. According to a survey on the topic, existing methods can be categorized into three parts based on their positions in the whole learning pipeline: unsupervised representation learning, supervised model learning and optimization. Another approach to out-of-distribution generalization is via learning domain-invariant features or hypothesis-invariant features.
