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cs.LG2020★ 13 cited
Evaluating Lossy Compression Rates of Deep Generative Models
Sicong Huang, Alireza Makhzani, Yanshuai Cao +1
The field of deep generative modeling has succeeded in producing astonishingly realistic-seeming images and audio, but quantitative evaluation remains a challenge. Log-likelihood i…
cs.LG2020★ 3 cited
Variational Hyper RNN for Sequence Modeling
Ruizhi Deng, Yanshuai Cao, Bo Chang +3
In this work, we propose a novel probabilistic sequence model that excels at capturing high variability in time series data, both across sequences and within an individual sequence…
cs.LG2019★ 16 cited
Graph Generation with Variational Recurrent Neural Network
Shih-Yang Su, Hossein Hajimirsadeghi, Greg Mori
Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes. In this paper, we introduce Graph Variational Recurren…