output
20162025
most citedAssessing and tuning brain decoders: cross-validation, caveats, and guidelines

887 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

5 papers

cs.CL2025★ 4 cited

Event Segmentation Applications in Large Language Model Enabled Automated Recall Assessments

Ryan A. Panela, Alex J. Barnett, Morgan D. Barense +1

Understanding how individuals perceive and recall information in their natural environments is critical to understanding potential failures in perception (e.g., sensory loss) and m…

cs.RO2024★ 32 cited

The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare

Souren Pashangpour, Goldie Nejat

The potential use of large language models (LLMs) in healthcare robotics can help address the significant demand put on healthcare systems around the world with respect to an aging…

cs.HC2024★ 3 cited

Image memorability predicts social media virality and externally-associated commenting

Shikang Peng, Wilma A. Bainbridge

Visual content on social media plays a key role in entertainment and information sharing, yet some images gain more engagement than others. We propose that image memorability - the…

cs.MS2020

Generalized eigen, singular value, and partial least squares decompositions: The GSVD package

Derek Beaton

The generalized singular value decomposition (GSVD, a.k.a. "SVD triplet", "duality diagram" approach) provides a unified strategy and basis to perform nearly all of the most common…

stat.ML2016★ 887 cited

Assessing and tuning brain decoders: cross-validation, caveats, and guidelines

Gaël Varoquaux, Pradeep Reddy Raamana, Denis Engemann +3

Decoding, ie prediction from brain images or signals, calls for empirical evaluation of its predictive power. Such evaluation is achieved via cross-validation, a method also used t…