no code implementations • NeurIPS 2020 • Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang, Andrew J. Greenshaw, Russell Greiner
The optimization procedure extracts the common features for each site by using a single-iteration algorithm and maps these site-specific common features to the site-independent shared space.
no code implementations • 28 Sep 2020 • Muhammad Yousefnezhad, Jeffrey Sawalha, Alessandro Selvitella, Daoqiang Zhang
This paper develops Deep Representational Similarity Learning (DRSL), a deep extension of RSA that is appropriate for analyzing similarities between various cognitive tasks in fMRI datasets with a large number of subjects, and high-dimensionality -- such as whole-brain images.
no code implementations • 9 Jan 2020 • Muhammad Yousefnezhad, Alessandro Selvitella, Liangxiu Han, Daoqiang Zhang
This paper proposes a Supervised Hyperalignment (SHA) method to ensure better functional alignment for MVP analysis, where the proposed method provides a supervised shared space that can maximize the correlation among the stimuli belonging to the same category and minimize the correlation between distinct categories of stimuli.
no code implementations • 9 Sep 2015 • Beate Franke, Jean-François Plante, Ribana Roscher, Annie Lee, Cathal Smyth, Armin Hatefi, Fuqi Chen, Einat Gil, Alexander Schwing, Alessandro Selvitella, Michael M. Hoffman, Roger Grosse, Dieter Hendricks, Nancy Reid
The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context.