Difference between revisions of "Out-of-Distribution (OOD) Generalization"
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* [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]] | * [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]] | ||
* [[Math for Intelligence#Mathematical Reasoning|Mathematical Reasoning]] | * [[Math for Intelligence#Mathematical Reasoning|Mathematical Reasoning]] | ||
| − | * [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 | Z. Shen, J. Liu, Y. He, X. Zhang, R. Xu, H. Yu, P. Cui - arXiv - Cornell University] |
* [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] | ||
* [http://proceedings.mlr.press/v139/krueger21a/krueger21a.pdf Out-of-Distribution Generalization via Risk Extrapolation] | * [http://proceedings.mlr.press/v139/krueger21a/krueger21a.pdf Out-of-Distribution Generalization via Risk Extrapolation] | ||
Revision as of 17:39, 27 May 2023
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- In-Context Learning (ICL) ... Context ... Causation vs. Correlation ... Autocorrelation ... Out-of-Distribution (OOD) Generalization ... Transfer Learning
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- Mathematical Reasoning
- Towards Out-Of-Distribution Generalization: A Survey | Z. Shen, J. Liu, Y. He, X. Zhang, R. Xu, H. Yu, P. Cui - arXiv - Cornell University
- 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.
Difference Between In-Context Learning and OOD Generalization
In-Context Learning (ICL) refers to the ability of a machine learning model to learn from a few examples provided in the context of a task, without any fine-tuning. This is also known as few-shot learning or zero-shot learning.
Out-of-distribution (OOD) generalization, on the other hand, refers to the ability of a machine learning model to generalize to new data that comes from a different distribution than the training data1.
The main difference between In-Context Learning (ICL) and OOD generalization is that in-context learning focuses on the ability of a model to learn from a few examples provided in the context of a task, while OOD generalization focuses on the ability of a model to generalize to new data that comes from a different distribution than the training data.
