Peer-reviewedLicence not statedThe Annals of Applied Statistics
Spectral-stimulus information for self-supervised stimulus encoding
- 1Department of Computer, Data, & Mathematical Sciences, Simmons University
- 2The U.S. Army Combat Capabilities Development Command Army Research Laboratory
- 3Department of Mathematics, University of Tennessee
Related
- Dual-domain self-supervised feature alignment via spectral–spatial representation learning for deepfake anomaly detection
- Human macrophages encode stimulus-specific information of prior exposures through trained immunity
- Event segmentation and encoding rely on information accumulated over different temporal scales
- PredIDR3: A new output-encoding scheme and abundant negative source provide more information for deep learning-based protein intrinsic disorder prediction
- Learning from Error Predictions Rather Than Prediction Errors: A Perspective on Self-Supervised Learning in the Brain
BibTeX
@article{deighton2026spectralstimulus,
title = {{Spectral-stimulus information for self-supervised stimulus encoding}},
author = {Jared Deighton and Wyatt Mackey and Ioannis D. Schizas and David L. Boothe and Vasileios Maroulas},
journal = {The Annals of Applied Statistics},
year = {2026},
month = jun,
doi = {10.1214/26-aoas2167},
url = {http://arxiv.org/abs/2407.06195},
}
RIS
TY - JOUR
TI - Spectral-stimulus information for self-supervised stimulus encoding
AU - Jared Deighton
AU - Wyatt Mackey
AU - Ioannis D. Schizas
AU - David L. Boothe
AU - Vasileios Maroulas
PY - 2026
DA - 2026/06/01
JO - The Annals of Applied Statistics
DO - 10.1214/26-aoas2167
UR - http://arxiv.org/abs/2407.06195
SN - 1932-6157
SN - 1941-7330
ER -