167 citations · 1k across the 81 of their papers we have counts for
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stat.ML2020★ 11 cited
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning
Qing Li, Siyuan Huang, Yining Hong +3
The goal of neural-symbolic computation is to integrate the connectionist and symbolist paradigms. Prior methods learn the neural-symbolic models using reinforcement learning (RL)…
stat.ML2020★ 4 cited
Learning Latent Space Energy-Based Prior Model
Bo Pang, Tian Han, Erik Nijkamp +2
We propose to learn energy-based model (EBM) in the latent space of a generator model, so that the EBM serves as a prior model that stands on the top-down network of the generator…
stat.ML2020★ 3 cited
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC
Erik Nijkamp, Ruiqi Gao, Pavel Sountsov +4
Learning energy-based model (EBM) requires MCMC sampling of the learned model as an inner loop of the learning algorithm. However, MCMC sampling of EBMs in high-dimensional data sp…