4 papers · 1 filter
Refining the Information Bottleneck via Adversarial Information Separation
Shuai Ning, Zhenpeng Wang, Lin Wang +5
Generalizing from limited data is particularly critical for models in domains such as material science, where task-relevant features in experimental datasets are often heavily conf…
Convolution-Based Converter : A Weak-Prior Approach For Modeling Stochastic Processes Based On Conditional Density Estimation
Chaoran Pang, Lin Wang, Shuangrong Liu +4
In this paper, a Convolution-Based Converter (CBC) is proposed to develop a methodology for removing the strong or fixed priors in estimating the probability distribution of target…
Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences
Xingshen Zhang, Lin Wang, Shuangrong Liu +3
In this study, Disentanglement in Difference(DiD) is proposed to address the inherent inconsistency between the statistical independence of latent variables and the goal of semanti…
OPT-GAN: A Broad-Spectrum Global Optimizer for Black-box Problems by Learning Distribution
Minfang Lu, Shuai Ning, Shuangrong Liu +4
Black-box optimization (BBO) algorithms are concerned with finding the best solutions for problems with missing analytical details. Most classical methods for such problems are bas…