Overview
- Skill Key
- hhhh124hhhh/gaussian-process-mlp-hybrid
- Author
- hhhh124hhhh
- Source Repo
- openclaw/skills
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- -
- Source Path
- skills/hhhh124hhhh/gaussian-process-mlp-hybrid
- Latest Commit SHA
- 52aee7fbfea16159a6009dd7e6bfbf6e203251ec
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.
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# 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 --- *Skill generated by Clawdbot*
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