55 citations · 124 across the 12 of their papers we have counts for
12 papers
Big Cooperative Learning
Yulai Cong
Cooperation plays a pivotal role in the evolution of human intelligence; moreover, it also underlies the recent revolutionary advancement of artificial intelligence (AI) that is dr…
Big Learning
Yulai Cong, Miaoyun Zhao
Recent advances in big/foundation models reveal a promising path for deep learning, where the roadmap steadily moves from big data to big models to (the newly-introduced) big learn…
Bridging Maximum Likelihood and Adversarial Learning via -Divergence
Miaoyun Zhao, Yulai Cong, Shuyang Dai +1
Maximum likelihood (ML) and adversarial learning are two popular approaches for training generative models, and from many perspectives these techniques are complementary. ML learni…
GO Hessian for Expectation-Based Objectives
Yulai Cong, Miaoyun Zhao, Jianqiao Li +2
An unbiased low-variance gradient estimator, termed GO gradient, was proposed recently for expectation-based objectives $\mathbb{E}_{q_{\boldsymbolγ}(\boldsymbol{y})} [f(\boldsymbo…
Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference
Hao Zhang, Bo Chen, Yulai Cong +3
To build a flexible and interpretable model for document analysis, we develop deep autoencoding topic model (DATM) that uses a hierarchy of gamma distributions to construct its mul…
GAN Memory with No Forgetting
Yulai Cong, Miaoyun Zhao, Jianqiao Li +2
As a fundamental issue in lifelong learning, catastrophic forgetting is directly caused by inaccessible historical data; accordingly, if the data (information) were memorized perfe…
On Leveraging Pretrained GANs for Generation with Limited Data
Miaoyun Zhao, Yulai Cong, Lawrence Carin
Recent work has shown generative adversarial networks (GANs) can generate highly realistic images, that are often indistinguishable (by humans) from real images. Most images so gen…
GO Gradient for Expectation-Based Objectives
Yulai Cong, Miaoyun Zhao, Ke Bai +1
Within many machine learning algorithms, a fundamental problem concerns efficient calculation of an unbiased gradient wrt parameters $\gammav$ for expectation-based objectives $\Eb…
Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC
Yulai Cong, Bo Chen, Hongwei Liu +1
It is challenging to develop stochastic gradient based scalable inference for deep discrete latent variable models (LVMs), due to the difficulties in not only computing the gradien…
Fast Simulation of Hyperplane-Truncated Multivariate Normal Distributions
Yulai Cong, Bo Chen, Mingyuan Zhou
We introduce a fast and easy-to-implement simulation algorithm for a multivariate normal distribution truncated on the intersection of a set of hyperplanes, and further generalize…
The Poisson Gamma Belief Network
Mingyuan Zhou, Yulai Cong, Bo Chen
To infer a multilayer representation of high-dimensional count vectors, we propose the Poisson gamma belief network (PGBN) that factorizes each of its layers into the product of a…
Gamma Belief Networks
Mingyuan Zhou, Yulai Cong, Bo Chen
To infer multilayer deep representations of high-dimensional discrete and nonnegative real vectors, we propose an augmentable gamma belief network (GBN) that factorizes each of its…