13 citations · 20 across the 9 of their papers we have counts for
9 papers
Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World
Bowen Lei, Dongkuan Xu, Ruqi Zhang +1
Sparse training has emerged as a promising method for resource-efficient deep neural networks (DNNs) in real-world applications. However, the reliability of sparse models remains a…
Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data
Yabo Niu, Yang Ni, Debdeep Pati +1
In a traditional Gaussian graphical model, data homogeneity is routinely assumed with no extra variables affecting the conditional independence. In modern genomic datasets, there i…
Bayesian Flexible Modelling of Spatially Resolved Transcriptomic Data
Arhit Chakrabarti, Yang Ni, Bani K. Mallick
Single-cell RNA-sequencing technologies may provide valuable insights to the understanding of the composition of different cell types and their functions within a tissue. Recent te…
An Active Learning-based Approach for Hosting Capacity Analysis in Distribution Systems
Kiyeob Lee, Peng Zhao, Anirban Bhattacharya +2
With the increasing amount of distributed energy resources (DERs) integration, there is a significant need to model and analyze hosting capacity (HC) for future electric distributi…
An Approximate Bayesian Approach to Covariate-dependent Graphical Modeling
Sutanoy Dasgupta, Peng Zhao, Jacob Helwig +3
Gaussian graphical models typically assume a homogeneous structure across all subjects, which is often restrictive in applications. In this article, we propose a weighted pseudo-li…
Calibrating the Rigged Lottery: Making All Tickets Reliable
Bowen Lei, Ruqi Zhang, Dongkuan Xu +1
Although sparse training has been successfully used in various resource-limited deep learning tasks to save memory, accelerate training, and reduce inference time, the reliability…