Wielopolski Patryk, Furman Oleksii, Zięba Maciej
Department of Artificial Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland.
Tooploox Ltd., 53-601 Wrocław, Poland.
Entropy (Basel). 2024 Jul 11;26(7):593. doi: 10.3390/e26070593.
We introduce NodeFlow, a flexible framework for probabilistic regression on tabular data that combines Neural Oblivious Decision Ensembles (NODEs) and Conditional Continuous Normalizing Flows (CNFs). It offers improved modeling capabilities for arbitrary probabilistic distributions, addressing the limitations of traditional parametric approaches. In NodeFlow, the NODE captures complex relationships in tabular data through a tree-like structure, while the conditional CNF utilizes the NODE's output space as a conditioning factor. The training process of NodeFlow employs standard gradient-based learning, facilitating the end-to-end optimization of the NODEs and CNF-based density estimation. This approach ensures outstanding performance, ease of implementation, and scalability, making NodeFlow an appealing choice for practitioners and researchers. Comprehensive assessments on benchmark datasets underscore NodeFlow's efficacy, revealing its achievement of state-of-the-art outcomes in multivariate probabilistic regression setup and its strong performance in univariate regression tasks. Furthermore, ablation studies are conducted to justify the design choices of NodeFlow. In conclusion, NodeFlow's end-to-end training process and strong performance make it a compelling solution for practitioners and researchers. Additionally, it opens new avenues for research and application in the field of probabilistic regression on tabular data.
我们介绍了NodeFlow，这是一个用于表格数据概率回归的灵活框架，它结合了神经遗忘决策集成（NODE）和条件连续归一化流（CNF）。它为任意概率分布提供了改进的建模能力，解决了传统参数方法的局限性。在NodeFlow中，NODE通过树状结构捕获表格数据中的复杂关系，而条件CNF则将NODE的输出空间用作条件因子。NodeFlow的训练过程采用基于梯度的标准学习方法，便于对NODE和基于CNF的密度估计进行端到端优化。这种方法确保了出色的性能、易于实现和可扩展性，使NodeFlow成为从业者和研究人员的一个有吸引力的选择。对基准数据集的全面评估强调了NodeFlow的有效性，揭示了它在多变量概率回归设置中取得了领先成果，以及在单变量回归任务中的强大性能。此外，还进行了消融研究以证明NodeFlow的设计选择是合理的。总之，NodeFlow的端到端训练过程和强大性能使其成为从业者和研究人员的一个有吸引力的解决方案。此外，它为表格数据概率回归领域的研究和应用开辟了新途径。
