---
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

---

*Skill generated by Clawdbot*