112 citations · 340 across the 21 of their papers we have counts for
9 papers · 1 filter
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…
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…
On Posterior Collapse and Encoder Feature Dispersion in Sequence VAEs
Teng Long, Yanshuai Cao, Jackie Chi Kit Cheung
Variational autoencoders (VAEs) hold great potential for modelling text, as they could in theory separate high-level semantic and syntactic properties from local regularities of na…
Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer
Yanshuai Cao, Peng Xu
In this work, we develop a novel regularizer to improve the learning of long-range dependency of sequence data. Applied on language modelling, our regularizer expresses the inducti…
Few-Shot Self Reminder to Overcome Catastrophic Forgetting
Junfeng Wen, Yanshuai Cao, Ruitong Huang
Deep neural networks are known to suffer the catastrophic forgetting problem, where they tend to forget the knowledge from the previous tasks when sequentially learning new tasks.…
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization
Yanshuai Cao, Gavin Weiguang Ding, Kry Yik-Chau Lui +1
We propose a novel regularizer to improve the training of Generative Adversarial Networks (GANs). The motivation is that when the discriminator D spreads out its model capacity in…
Transductive Log Opinion Pool of Gaussian Process Experts
Yanshuai Cao, David J. Fleet
We introduce a framework for analyzing transductive combination of Gaussian process (GP) experts, where independently trained GP experts are combined in a way that depends on test…
Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions
Yanshuai Cao, David J. Fleet
In this work, we propose a generalized product of experts (gPoE) framework for combining the predictions of multiple probabilistic models. We identify four desirable properties tha…
Efficient Optimization for Sparse Gaussian Process Regression
Yanshuai Cao, Marcus A. Brubaker, David J. Fleet +1
We propose an efficient optimization algorithm for selecting a subset of training data to induce sparsity for Gaussian process regression. The algorithm estimates an inducing set a…