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Nina Thigpen
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2020 – today
- 2021
- [j2]Ryan Burt, Nina N. Thigpen, Andreas Keil, José C. Príncipe
:
Unsupervised foveal vision neural architecture with top-down attention. Neural Networks 141: 145-159 (2021) - [c2]Garrett Honke, Irina Higgins, Nina Thigpen, Vladimir Miskovic, Katie Link, Sunny Duan, Pramod Gupta, Julia Klawohn, Greg Hajcak:
Representation learning for improved interpretability and classification accuracy of clinical factors from EEG. ICLR 2021 - 2020
- [i3]Ryan Burt, Nina N. Thigpen, Andreas Keil, José C. Príncipe:
Unsupervised Foveal Vision Neural Networks with Top-Down Attention. CoRR abs/2010.09103 (2020) - [i2]Garrett Honke, Irina Higgins, Nina Thigpen, Vladimir Miskovic, Katie Link, Pramod Gupta, Julia Klawohn, Greg Hajcak:
Representation learning for improved interpretability and classification accuracy of clinical factors from EEG. CoRR abs/2010.15274 (2020) - [i1]Ayse S. Cakmak, Nina Thigpen, Garrett Honke, Erick Andres Perez-Alday, Ali Bahrami Rad, Rebecca Adaimi, Chia-Jung Chang, Qiao Li, Pramod Gupta, Thomas Neylan, Samuel A. McLean, Gari D. Clifford:
Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data. CoRR abs/2011.07406 (2020)
2010 – 2019
- 2019
- [j1]Nathan M. Petro, Nina Thigpen, Steven Garcia, Maeve R. Boylan, Andreas Keil
Pre-target alpha power predicts the speed of cued target discrimination. NeuroImage 189: 878-885 (2019) - 2016
- [c1]Ryan Burt, Eder Santana, José C. Príncipe, Nina Thigpen, Andreas Keil
Predicting visual attention using gamma kernels. ICASSP 2016: 1606-1610
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