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P24: Unifying Generative Models with GFlowNets
Dinghuai Zhang · Ricky T. Q. Chen
Event URL: https://drive.google.com/file/d/1ejC6vxzMpN8krSJ5XYpqNXwZTVroAVXj/view »

Authors: Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin, Yoshua Bengio

Abstract: 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~\citep{Bengio2021GFlowNetF}, 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.

Author Information

Dinghuai Zhang (Mila, Meta)
Ricky T. Q. Chen (Facebook AI Research)

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