Past Events

Qualifying Exam

Generative Neurosymbolic Machines


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Tuesday, April 05, 2022, 09:00am - 10:00am


Speaker: Jindong Jiang

Location : Via Zoom


Prof. Sungjin Ahn (advisor) 

Prof. Hao Wang 

Prof. Dimitris Metaxas

Prof. Sudarsun Kannan 

Event Type: Qualifying Exam

Abstract: Reconciling symbolic and distributed representations is a crucial challenge that can potentially resolve the limitations of current deep learning. Remarkable advances in this direction have been achieved recently via generative object-centric representation models. While learning a recognition model that infers object-centric symbolic representations like bounding boxes from raw images in an unsupervised way, no such model can provide another important ability of a generative model, i.e., generating (sampling) according to the structure of learned world density. In this work, we propose Generative Neurosymbolic Machines, a generative model that combines the benefits of distributed and symbolic representations to support both structured representations of symbolic components and density-based generation. These two crucial properties are achieved by a two-layer latent hierarchy with the global distributed latent for flexible density modeling and the structured symbolic latent map. To increase the model flexibility in this hierarchical structure, we also propose the StructDRAW prior. In experiments, we show that the proposed model significantly outperforms the previous structured representation models as well as the state-of-the-art non-structured generative models in terms of both structure accuracy and image generation quality.


Rutgers University School of Arts and Sciences

Contact  Sungjin Ahn

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