Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders. - Yu Wang 060; Bin Dai 008; Gang Hua 001; John A. D. Aston; David P. Wipf (UAI, 2017)

Veiled Attributes of the Variational Autoencoder. - Bin Dai 008; Yu Wang 060; John A. D. Aston; Gang Hua 001; David P. Wipf (CoRR, 2017)

Understanding and Predicting The Attractiveness of Human Action Shot. - Bin Dai 008; Baoyuan Wang; Gang Hua 001 (CoRR, 2017)

Compressing Neural Networks using the Variational Information Bottleneck. - Bin Dai 008; Chen Zhu; Baining Guo; David P. Wipf (ICML, 2018)

Compressing Neural Networks using the Variational Information Bottleneck. - Bin Dai 008; Chen Zhu; David P. Wipf (CoRR, 2018)

Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models. - Bin Dai 008; Yu Wang 060; John A. D. Aston; Gang Hua 001; David P. Wipf (J. Mach. Learn. Res., 2018)

Recurrent Variational Autoencoders for Learning Nonlinear Generative Models in the Presence of Outliers. - Yu Wang 060; Bin Dai 008; Gang Hua 001; John A. D. Aston; David P. Wipf (IEEE J. Sel. Top. Signal Process., 2018)

Diagnosing and Enhancing VAE Models. - Bin Dai 008; David P. Wipf (ICLR (Poster), 2019)

The Usual Suspects? Reassessing Blame for VAE Posterior Collapse. - Bin Dai 008; Ziyu Wang 006; David P. Wipf (ICML, 2020)

Further Analysis of Outlier Detection with Deep Generative Models. - Ziyu Wang 006; Bin Dai 008; David P. Wipf; Jun Zhu 001 (NeurIPS, 2020)

On the Value of Infinite Gradients in Variational Autoencoder Models. - Bin Dai 008; Wenliang Li; David P. Wipf (NeurIPS, 2021)

Disentangling Planning, Driving and Rendering for Photorealistic Avatar Agents. - Duomin Wang; Bin Dai 008; Yu Deng 006; Baoyuan Wang (ECCV Workshops (14), 2024)