Beginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a typical classification problem encountered in the domain of high-energy physics, using a well-studied dataset: the 2014 Higgs ML Kaggle dataset. The advantages are evaluated in terms of both performance metrics and the time required to train and apply the resulting models. Techniques examined include domain-specific data-augmentation, learning rate and momentum scheduling, (advanced) ensembling in both model-space and weight-space, and alternative architectures and connection methods. Following the investigation, we arrive at a model which achieves equal performance to the winning solution of the original Kaggle challenge, whilst being significantly quicker to train and apply, and being suitable for use with both GPU and CPU hardware setups. These reductions in timing and hardwar...

中文翻译：

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在实验性的高能物理用例中，研究所选现代深度学习技术对分类模型的性能和速度的影响

从基本的神经网络架构开始，在高能物理领域遇到的典型分类问题的背景下，我们测试了一系列先进的机器学习技术（尤其是深度学习）所提供的潜在利益。深入研究的数据集：2014年希格斯ML Kaggle数据集。根据性能指标以及训练和应用所得模型所需的时间来评估优势。研究的技术包括特定领域的数据增强，学习率和动量调度，模型空间和权重空间中的（高级）集成，以及替代的体系结构和连接方法。经过调查，我们得出了一个模型，该模型可以达到与原始Kaggle挑战的获奖解决方案相同的性能，同时大大加快了培训和应用的速度，并且适用于GPU和CPU硬件设置。时间和艰苦战争的减少...