167 citations · 1k across the 81 of their papers we have counts for
11 papers · 1 filter
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis
Jianwen Xie, Zilong Zheng, Ruiqi Gao +3
3D data that contains rich geometry information of objects and scenes is valuable for understanding 3D physical world. With the recent emergence of large-scale 3D datasets, it beco…
Learning Energy-Based Models by Diffusion Recovery Likelihood
Ruiqi Gao, Yang Song, Ben Poole +2
While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging. Inspired by recent progress on di…
Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling
Bo Pang, Erik Nijkamp, Jiali Cui +2
This paper proposes a latent space energy-based prior model for semi-supervised learning. The model stands on a generator network that maps a latent vector to the observed example.…
Learning Latent Space Energy-Based Prior Model for Molecule Generation
Bo Pang, Tian Han, Ying Nian Wu
Deep generative models have recently been applied to molecule design. If the molecules are encoded in linear SMILES strings, modeling becomes convenient. However, models relying on…
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)…
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model
Tian Han, Erik Nijkamp, Linqi Zhou +3
This paper proposes a joint training method to learn both the variational auto-encoder (VAE) and the latent energy-based model (EBM). The joint training of VAE and latent EBM are b…