论文标题
元学习的自适应深核高斯过程分子性质预测
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction
论文作者
论文摘要
我们提出了具有隐式函数定理（ADKF-IFT）的自适应深内核拟合，这是一种通过在元学习和传统深内核学习之间插值来学习深内核高斯过程（GPS）的新型框架。我们的方法采用了一个双重优化目标，从任务跨任务的特定特定特定的GP模型估计在此类特征之上估计的特定特定的GP模型可以达到平均可能的预测损失。我们使用隐式函数定理（IFT）解决了所得的嵌套优化问题。我们表明，我们的ADKF-IFT框架包含先前提出的深内核学习（DKL）和深内核转移（DKT）作为特殊情况。尽管ADKF-IFT是一种完全通用的方法，但我们认为它特别适合药物发现问题，并证明它在各种现实世界中的几乎没有分子的财产预测任务和层次范围的分子财产预测和优化任务上大大优于先前的最新方法。
We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem (ADKF-IFT), a novel framework for learning deep kernel Gaussian processes (GPs) by interpolating between meta-learning and conventional deep kernel learning. Our approach employs a bilevel optimization objective where we meta-learn generally useful feature representations across tasks, in the sense that task-specific GP models estimated on top of such features achieve the lowest possible predictive loss on average. We solve the resulting nested optimization problem using the implicit function theorem (IFT). We show that our ADKF-IFT framework contains previously proposed Deep Kernel Learning (DKL) and Deep Kernel Transfer (DKT) as special cases. Although ADKF-IFT is a completely general method, we argue that it is especially well-suited for drug discovery problems and demonstrate that it significantly outperforms previous state-of-the-art methods on a variety of real-world few-shot molecular property prediction tasks and out-of-domain molecular property prediction and optimization tasks.
