论文标题
部分可观测时空混沌系统的无模型预测
Heterogenous Ensemble of Models for Molecular Property Prediction
论文作者
论文摘要
先前的工作已经证明了考虑分子上不同方式的重要性，每个分子都为下游属性预测任务提供了各种信息的粒度。我们的方法将最近的变压器架构的变体与变压器，GNN和Resnet骨干架构结合在一起。对模型进行了对2D数据，3D数据和分子图的图像方式的训练。我们将这些模型与Huberregressor结合在一起。对原始火车 +有效数据集的4个不同的火车/验证拆分进行了培训。这为OGB大规模挑战（2022）在PCQM4MV2分子属性预测数据集上的2 \ textsuperscript {nd}版本提供了成功解决方案。我们提出的方法实现了$ 0.0723 $的测试挑战MAE，验证MAE $ 0.07145 $。解决方案的总推理时间少于2小时。我们在https://github.com/jfpuget/nvidia-pcqm4mv2上开放代码。
Previous works have demonstrated the importance of considering different modalities on molecules, each of which provide a varied granularity of information for downstream property prediction tasks. Our method combines variants of the recent TransformerM architecture with Transformer, GNN, and ResNet backbone architectures. Models are trained on the 2D data, 3D data, and image modalities of molecular graphs. We ensemble these models with a HuberRegressor. The models are trained on 4 different train/validation splits of the original train + valid datasets. This yields a winning solution to the 2\textsuperscript{nd} edition of the OGB Large-Scale Challenge (2022) on the PCQM4Mv2 molecular property prediction dataset. Our proposed method achieves a test-challenge MAE of $0.0723$ and a validation MAE of $0.07145$. Total inference time for our solution is less than 2 hours. We open-source our code at https://github.com/jfpuget/NVIDIA-PCQM4Mv2.
