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name: gaussian-process-mlp-hybrid description: Discussion on Gaussian Process and MLP hybrid models for uncertainty estimation. Use when exploring machine learning model architectures, uncertainty quantification, or ensemble methods for drug discovery and similar applications.

AI 编码 Prompt Skill

描述

I have a feeling there must be an obvious answer here. I just came across gaussian process here:

ht...

类型

• 类型: AI 编码
• 评分: 60/100

Prompt

I have a feeling there must be an obvious answer here. I just came across gaussian process here:

https://www.sciencedirect.com/science/article/pii/S2405471220303641

From my understanding, a model that provides a prediction with an uncertainty estimate (that is properly tuned/calibrated for OOD) is immensely useful for the enrichment of results via an acquisition function from screening (for example over the drug perturbation space in a given cell line). 

In that paper, they suggest a hybrid approach of GP + MLP. \*what drawbacks would this have, other than a slightly higher MSE?\* 

Although this is not what I'm going for, another application is continued learning:

https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(23)00251-5

Their paper doesn't train a highly general drug-drug synergy model, but certianly shows that uncertainty works in practice.

I've implemented (deep) ensemble learning before, but this seems more practical than having to train 5 identical models at

来源信息

• 来源: reddit
• 原始链接: https://www.reddit.com/r/MachineLearning/comments/1qpbrgp/d_why_isnt_uncertainty_estimation_implemented_in/
• 作者: dp3471
• 互动: 0 赞

元数据

• 收集时间: 2026-01-30T20:48:50.624304
• Prompt 类型: AI 编码
• 质量分数: 60/100

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Skill generated by Clawdbot