Training deep learning models and performing hyperparameter tuning can be computationally demanding and time-consuming. Meanwhile, traditional machine learning methods like gradient-boosting algorithms remain the preferred choice for most tabular data applications, while neural network alternatives require extensive hyperparameter tuning or work only in toy datasets under limited settings. In this paper, we introduce HyperFast, a meta-trained hypernetwork designed for instant classification of tabular data in a single forward pass. HyperFast generates a task-specific neural network tailored to an unseen dataset that can be directly used for classification inference, removing the need for training a model. We report extensive experiments with OpenML and genomic data, comparing HyperFast to competing tabular data neural networks, traditional ML methods, AutoML systems, and boosting machines. HyperFast shows highly competitive results, while being significantly faster. Additionally, our approach demonstrates robust adaptability across a variety of classification tasks with little to no fine-tuning, positioning HyperFast as a strong solution for numerous applications and rapid model deployment. HyperFast introduces a promising paradigm for fast classification, with the potential to substantially decrease the computational burden of deep learning. Our code, which offers a scikit-learn-like interface, along with the trained HyperFast model, can be found at https://github.com/AI-sandbox/HyperFast.

翻译：训练深度学习模型并进行超参数调优可能计算成本高昂且耗时。与此同时，传统机器学习方法（如梯度提升算法）仍多数表格数据应用中的首选方案，而神经网络替代方案要么需要大量超参数调优，要么仅能在有限设置下的玩具数据集中运作。本文提出HyperFast——一种元训练的超级网络，专为在单次前向传播中实现表格数据即时分类而设计。HyperFast能为未见数据集生成任务特定的神经网络，并可直接用于分类推理，无需模型训练。我们通过OpenML与基因组数据开展广泛实验，将HyperFast与竞争性表格数据神经网络、传统机器学习方法、AutoML系统及提升机器进行对比。结果显示，HyperFast在保持高度竞争力的同时，显著提升了运行速度。此外，本方法在几乎无需微调的情况下展现出对多种分类任务的稳健适应性，使HyperFast成为众多应用场景与快速模型部署的有力解决方案。HyperFast为快速分类引入了极具前景的新范式，具有大幅降低深度学习计算负担的潜力。提供scikit-learn风格接口的代码及训练好的HyperFast模型已开源：https://github.com/AI-sandbox/HyperFast。