The learning of probability distributions of data enables various applications, including but not limited to data synthesis and probability inference. A conditional non-linear normalizing flow model,and a system and method for training said model, are provided. The normalizing flow model may be trained to model unknown and complex conditional probability distributions which are at the heart of many real-life applications. For example, the trained normalizing flow model may be used in (semi)autonomous driving systems to infer what the probability is that a pedestrian is at position x at futuretime t given the pedestrian features c, which may be observed from sensor data, or may be used to synthesize likely pedestrian positions x at future time t given the observed pedestrian features c. This may allow the driving system to determine a route avoiding the pedestrian. Various other applications for the trained normalizing flow model are conceived as well.
数据概率分布的学习使得能够实现各种应用,其包括但不限于数据合成和概率推断。提供了一种条件非线性标准化流模型以及用于训练所述模型的系统和方法。标准化流模型可以被训练以建模未知的和复杂的条件概率分布,这是许多现实生活应用的核心。例如,经训练的标准化流模型可以用在(半)自主驾驶系统中,以推断行人在给定行人特征——其可以从传感器数据中观察到——的情况下在未来时间处于位置的概率是什么,或者可以用于合成在给定观察到的行人特征的情况下在未来时间t处可能的行人位置。这可以允许驾驶系统确定避开行人的路线。还设想了对于经训练的标准化流模型的各种其它应用。
Training and data synthesis and probability inference using nonlinear conditional normalizing flow model
使用非线性条件标准化流模型的训练和数据合成和概率推断
2021-01-19
Patent
Electronic Resource
Chinese
IPC: | G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion |
A MACHINE LEARNABLE SYSTEM WITH CONDITIONAL NORMALIZING FLOW
European Patent Office | 2021
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