21 citations · 52 across the 11 of their papers we have counts for
15 papers
StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis
Zhiheng Li, Martin Renqiang Min, Kai Li +1
Although progress has been made for text-to-image synthesis, previous methods fall short of generalizing to unseen or underrepresented attribute compositions in the input text. Lac…
AE-StyleGAN: Improved Training of Style-Based Auto-Encoders
Ligong Han, Sri Harsha Musunuri, Martin Renqiang Min +3
StyleGANs have shown impressive results on data generation and manipulation in recent years, thanks to its disentangled style latent space. A lot of efforts have been made in inver…
Disentangled Recurrent Wasserstein Autoencoder
Jun Han, Martin Renqiang Min, Ligong Han +2
Learning disentangled representations leads to interpretable models and facilitates data generation with style transfer, which has been extensively studied on static data such as i…
Ranking-based Convolutional Neural Network Models for Peptide-MHC Binding Prediction
Ziqi Chen, Martin Renqiang Min, Xia Ning
T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identify…
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation
Yizhe Zhu, Martin Renqiang Min, Asim Kadav +1
We propose a sequential variational autoencoder to learn disentangled representations of sequential data (e.g., videos and audios) under self-supervision. Specifically, we exploit…
CNN-based Dual-Chain Models for Knowledge Graph Learning
Bo Peng, Renqiang Min, Xia Ning
Knowledge graph learning plays a critical role in integrating domain specific knowledge bases when deploying machine learning and data mining models in practice. Existing methods o…