TP6b2.5

Information-Theoretic Limits on the Performance of Auditory Attention Decoders

Ruwanthi Abeysekara, University of Maryland, United States; Christopher Smalt, MIT Lincoln Laboratory, United States; I. M. Dushyanthi Karunathilake, Jonathan Simon, Behtash Babadi, University of Maryland, United States
Session:
TP6b2: Machine Learning Models for Biomedical Signal and Image Processing Poster
Track:
Biomedical Signal and Image Processing
Location:
Kiln
Presentation Time:
Tue, 31 Oct, 15:30 - 17:35 PT (UTC -7)
Session Chair:
Ruwanthi Abeysekara, University of Maryland - College Park
Presentation
Discussion
Resources
No resources available.
Session TP6b2
TP6b2.1: MRI Reconstruction with Side Information using Diffusion Models
Brett Levac, University of Texas, Austin, United States; Ajil Jalal, Kannan Ramchandran, University of California, Berkeley, United States; Jonathan Tamir, University of Texas, Austin, United States
TP6b2.2: Physics-driven Learned Deconvolution of Multi-spectral Cellular MRI with Radial Sampling
Jiawen Chen, Eric Ahrens, Piya Pal, University of California San Diego, United States
TP6b2.3: Flexible Multisubject Multiset FMRI Data Analysis Using Robust Discriminative Dictionary Learning
Rui Jin, Shuai Xu, Seung-Jun Kim, University of Maryland, Baltimore County, United States; Vince Calhoun, Tri-institutional Center for Translational Research in Neuroimaging & Data Science (TReNDS), United States
TP6b2.4: FlySeg: an Automated Volumetric Instance Segmentation Algorithm for Dense Cell Populations in Drosophila Melanogaster Nervous System
Andrea Vaccari, Middlebury College, United States; Mark Dombrovski, University of California, Los Angeles, United States
TP6b2.5: Information-Theoretic Limits on the Performance of Auditory Attention Decoders
Ruwanthi Abeysekara, University of Maryland, United States; Christopher Smalt, MIT Lincoln Laboratory, United States; I. M. Dushyanthi Karunathilake, Jonathan Simon, Behtash Babadi, University of Maryland, United States
Contacts