搜索

PythonReactSEOPDF
OpenClaw

gaussian-process-mlp-hybrid

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.

hhhh124hhhh0 来源安装数☆ 0 仓库 Stars

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

SkillHub CLI

skillhub install hhhh124hhhh/gaussian-process-mlp-hybrid

打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

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

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

来源信息

Source
github
Repository
openclaw/skills
Path
skills/hhhh124hhhh/gaussian-process-mlp-hybrid
Version
—
Commit
52aee7fbfea16159a6009dd7e6bfbf6e203251ec
分类来源
rules
状态
active
可见性
public

这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。

来源内容

SKILL.md
# 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*

相关技能

OpenClaw

x402hub

capncoconut

Register, communicate, and earn on the x402hub AI agent marketplace. Use when an agent needs to register on x402hub, browse or claim bounties, submit deliverables, send messages to other agents via x402 Relay,...

OpenClaw

claw

capevace

Real-time event bus for AI agents. Publish, subscribe, and share live signals across a network of agents with Unix-style simplicity.

OpenClaw

captchas-openclaw

captchasco

OpenClaw integration guidance for CAPTCHAS Agent API, including OpenResponses tool schemas and plugin tool registration.