921 citations · 9.3k across the 292 of their papers we have counts for
6 papers · 2 filters
Latent Variable Modeling with Diversity-Inducing Mutual Angular Regularization
Pengtao Xie, Yuntian Deng, Eric Xing
Latent Variable Models (LVMs) are a large family of machine learning models providing a principled and effective way to extract underlying patterns, structure and knowledge from ob…
Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines
Hao Zhang, Zhiting Hu, Jinliang Wei +4
Deep learning (DL) has achieved notable successes in many machine learning tasks. A number of frameworks have been developed to expedite the process of designing and training deep…
Distributed Machine Learning via Sufficient Factor Broadcasting
Pengtao Xie, Jin Kyu Kim, Yi Zhou +4
Matrix-parametrized models, including multiclass logistic regression and sparse coding, are used in machine learning (ML) applications ranging from computer vision to computational…
On the Generalization Error Bounds of Neural Networks under Diversity-Inducing Mutual Angular Regularization
Pengtao Xie, Yuntian Deng, Eric Xing
Recently diversity-inducing regularization methods for latent variable models (LVMs), which encourage the components in LVMs to be diverse, have been studied to address several iss…
Deep Kernel Learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov +1
We introduce scalable deep kernels, which combine the structural properties of deep learning architectures with the non-parametric flexibility of kernel methods. Specifically, we t…
The Human Kernel
Andrew Gordon Wilson, Christoph Dann, Christopher G. Lucas +1
Bayesian nonparametric models, such as Gaussian processes, provide a compelling framework for automatic statistical modelling: these models have a high degree of flexibility, and a…