论文标题
用Gflownets及以后统一生成模型
Unifying Generative Models with GFlowNets and Beyond
论文作者
论文摘要
有许多用于深层生成建模的框架，每个框架通常都有自己的特定培训算法和推理方法。在这里，我们演示了现有的深层生成模型与最近引入的GFLOWNET框架之间的连接，GFLOWNET框架是一种概率推理机，将采样视为决策过程。该分析阐明了它们的重叠特征，并通过马尔可夫轨迹的学习镜头提供了一个统一的观点。我们的框架为统一培训和推理算法提供了一种手段，并提供了一条途径，以使许多生成模型都散发出统一的光。除此之外，我们还提供了一种实用且经过实验验证的配方，可从Gflownet的角度通过见解来改进生成型建模。
There are many frameworks for deep generative modeling, each often presented with their own specific training algorithms and inference methods. Here, we demonstrate the connections between existing deep generative models and the recently introduced GFlowNet framework, a probabilistic inference machine which treats sampling as a decision-making process. This analysis sheds light on their overlapping traits and provides a unifying viewpoint through the lens of learning with Markovian trajectories. Our framework provides a means for unifying training and inference algorithms, and provides a route to shine a unifying light over many generative models. Beyond this, we provide a practical and experimentally verified recipe for improving generative modeling with insights from the GFlowNet perspective.
