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