1 citations · 2 across the 4 of their papers we have counts for
5 papers
Learn Feasibility Once, Optimize All Objectives: Derivative-Free Diffusion Models for Chance-Constrained Programming
Ziwen Liu, Yan Liu, Congying Han +3
Chance-constrained programs (CCPs) optimize decisions under uncertainty by limiting the probability of constraint violation. Despite advances in traditional and learning-based appr…
Symbolic Density Estimation for Discrete Distributions
Ziwen Liu, Meng Li
Discrete probability laws underpin statistical modeling, yet the catalog of interpretable distributions has expanded only gradually through centuries of case-by-case mathematical d…
Learning Dynamic Tetrahedra for High-Quality Talking Head Synthesis
Zicheng Zhang, Ruobing Zheng, Ziwen Liu +7
Recent works in implicit representations, such as Neural Radiance Fields (NeRF), have advanced the generation of realistic and animatable head avatars from video sequences. These i…
Masked Reconstruction Contrastive Learning with Information Bottleneck Principle
Ziwen Liu, Bonan Li, Congying Han +2
Contrastive learning (CL) has shown great power in self-supervised learning due to its ability to capture insight correlations among large-scale data. Current CL models are biased…
Blocked and Hierarchical Disentangled Representation From Information Theory Perspective
Ziwen Liu, Mingqiang Li, Congying Han
We propose a novel and theoretical model, blocked and hierarchical variational autoencoder (BHiVAE), to get better-disentangled representation. It is well known that information th…