most citedA Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation

22 citations · 35 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20212 cited

ProBF: Learning Probabilistic Safety Certificates with Barrier Functions

Athindran Ramesh Kumar, Sulin Liu, Jaime F. Fisac +2

Safety-critical applications require controllers/policies that can guarantee safety with high confidence. The control barrier function is a useful tool to guarantee safety if we ha…

stat.ML20166 cited

A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis

Hejia Zhang, Po-Hsuan Chen, Janice Chen +5

There is a growing interest in joint multi-subject fMRI analysis. The challenge of such analysis comes from inherent anatomical and functional variability across subjects. One appr…

cs.LG20161 cited

The Symmetry of a Simple Optimization Problem in Lasso Screening

Yun Wang, Peter J. Ramadge

Recently dictionary screening has been proposed as an effective way to improve the computational efficiency of solving the lasso problem, which is one of the most commonly used met…

cs.LG20164 cited

Feedback-Controlled Sequential Lasso Screening

Yun Wang, Xu Chen, Peter J. Ramadge

One way to solve lasso problems when the dictionary does not fit into available memory is to first screen the dictionary to remove unneeded features. Prior research has shown that…

stat.ML201622 cited

A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation

Po-Hsuan Chen, Xia Zhu, Hejia Zhang +5

Finding the most effective way to aggregate multi-subject fMRI data is a long-standing and challenging problem. It is of increasing interest in contemporary fMRI studies of human c…