There are many frameworks for deep generative modeling, each often presented with their own specific training algorithms and inference methods. We present a short note on the connections between existing deep generative models and the GFlowNet framework, shedding light on their overlapping traits and providing a unifying viewpoint through the lens of learning with Markovian trajectories. This provides a means for unifying training and inference algorithms, and provides a route to construct an agglomeration of generative models.

翻译：有许多深层基因模型框架,每个框架都往往以自己的具体培训算法和推理方法提出。 我们简短地介绍了现有深层基因模型与GFlowNet框架之间的联系,阐明了这些模型的重叠特征,并通过与Markovian轨迹的学习透镜提供了统一观点。 这为统一培训和推理算法提供了一种手段,并为构建基因模型聚合提供了一条途径。