Out-of-Distribution (OOD) Generalization - Revision history

BPeat at 20:23, 28 April 2024

2024-04-28T20:23:00Z

BPeat at 00:26, 9 September 2023

2023-09-09T00:26:03Z

BPeat at 01:20, 17 August 2023

2023-08-17T01:20:15Z

BPeat at 16:06, 30 June 2023

2023-06-30T16:06:51Z

BPeat: /* Difference Between Autocorrelation and OOD Generalization */

2023-05-27T23:29:52Z

‎Difference Between Autocorrelation and OOD Generalization

BPeat: /* Difference Between Transfer Learning and OOD Generalization */

2023-05-27T22:57:04Z

‎Difference Between Transfer Learning and OOD Generalization

BPeat at 22:55, 27 May 2023

2023-05-27T22:55:13Z

BPeat at 22:50, 27 May 2023

2023-05-27T22:50:25Z

BPeat at 22:49, 27 May 2023

2023-05-27T22:49:54Z

BPeat at 22:49, 27 May 2023

2023-05-27T22:49:37Z

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 [https://www.bing.com/news/search?q=ai+Out+Distribution+OOD+Generalization&qft=interval%3d%228%22 ...Bing News]
-* [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
+* [[Perspective]] ... [[Context]] ... [[In-Context Learning (ICL)]] ... [[Transfer Learning]] ... [[Out-of-Distribution (OOD) Generalization]]
 * [[Artificial General Intelligence (AGI) to Singularity]] ... [[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]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]]
 * [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 | H. Ye, C. Xie, T. Cai, R. Li, Z. Li, L. Wang - arXiv - Cornell University]

@@ Line 21: / Line 21: @@
 * [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer 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]]
+* [[Artificial General Intelligence (AGI) to Singularity]] ... [[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]]
 * [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]

@@ Line 46: / Line 46: @@
 * [[In-Context Learning (ICL)]] ... [[Context]]
-[[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.
+[[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.

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 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, ChatGPT, AGI
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=ai+Out+Distribution+OOD+Generalization YouTube]

← Older revision		Revision as of 23:29, 27 May 2023
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	= Difference Between Autocorrelation and OOD Generalization =		= Difference Between Autocorrelation and OOD Generalization =
	* [[Autocorrelation]]		* [[Autocorrelation]]
		+
		+	Autocorrelation is the correlation of a signal with a delayed copy of itself as a function of delay. Informally, it is the similarity between observations of a random variable as a function of the time lag between them. It is often used in signal processing for analyzing functions or series of values, such as time domain signals.
		+
		+	On the other hand, Out-of-Distribution (OOD) generalization is a problem in machine learning that addresses the challenging setting where the testing distribution is unknown and different from the training.
		+
		+	In summary, autocorrelation deals with finding repeating patterns in signals while OOD generalization deals with generalizing to unknown and different distributions.

← Older revision		Revision as of 22:57, 27 May 2023
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	In summary, [[Transfer Learning]] deals with reusing knowledge from one task to improve performance on another related task while OOD generalization deals with the problem of generalizing to unknown and different distributions.		In summary, [[Transfer Learning]] deals with reusing knowledge from one task to improve performance on another related task while OOD generalization deals with the problem of generalizing to unknown and different distributions.
		+
		+	= Difference Between Autocorrelation and OOD Generalization =
		+	* [[Autocorrelation]]

← Older revision		Revision as of 22:55, 27 May 2023
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	The main difference between [[In-Context Learning (ICL)]] and OOD generalization is that [[In-Context Learning (ICL)\|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.		The main difference between [[In-Context Learning (ICL)]] and OOD generalization is that [[In-Context Learning (ICL)\|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.
		+
		+	= Difference Between Transfer Learning and OOD Generalization =
		+	* [[Transfer Learning]]
		+
		+	[[Transfer Learning]] is a machine learning method that reuses a trained model designed for a particular task to accomplish a different yet related task. The knowledge acquired from task one is thereby transferred to the second model that focuses on the new task.
		+
		+	Out-of-Distribution (OOD) generalization is a problem in machine learning that addresses the challenging setting where the testing distribution is unknown and different from the training1. This problem is also known as domain generalization.
		+
		+	In summary, [[Transfer Learning]] deals with reusing knowledge from one task to improve performance on another related task while OOD generalization deals with the problem of generalizing to unknown and different distributions.

← Older revision		Revision as of 22:50, 27 May 2023
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	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.		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.
		+

	<youtube>W3XE9yD5H4A</youtube>		<youtube>W3XE9yD5H4A</youtube>

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 * [https://link.springer.com/chapter/10.1007/978-3-030-92659-5_39 How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation? | A. Sanner, C. González & A. Mukhopadhyay]
 * [https://link.springer.com/chapter/10.1007/978-3-031-25075-0_36 Meta-Causal Feature Learning for Out-of-Distribution Generalization | Y. Wang, X. Li, Z. Qi, J. Li, X. Li, X. Meng & L. Meng]
 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.