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Found 9 papers, 1 papers with code
Multi-view and Cross-view Brain Decoding
(2) Our extensive analysis across 9 broad regions, 11 language sub-regions and 16 visual sub-regions of the brain help us localize, for the first time, the parts of the brain involved in cross-view tasks like image captioning, image tagging, sentence formation and keyword extraction.
Enhancing Healthcare with EOG: A Novel Approach to Sleep Stage Classification
We introduce an innovative approach to automated sleep stage classification using EOG signals, addressing the discomfort and impracticality associated with EEG data acquisition.
Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)
In this survey, we will first discuss popular representations of language, vision and speech stimuli, and present a summary of neuroscience datasets.
Cross-view Brain Decoding
Also, the decoded representations are sufficiently detailed to enable high accuracy for cross-view-translation tasks with following pairwise accuracy: IC (78. 0), IT (83. 0), KE (83. 7) and SF (74. 5).
Visio-Linguistic Brain Encoding
In this paper, we systematically explore the efficacy of image Transformers (ViT, DEiT, and BEiT) and multi-modal Transformers (VisualBERT, LXMERT, and CLIP) for brain encoding.
A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategies
Sepsis is a leading cause of mortality and its treatment is very expensive.
Expert2Coder: Capturing Divergent Brain Regions Using Mixture of Regression Experts
In this paper, we achieve this by clustering similar regions together and for every cluster we learn a different linear regression model using a mixture of linear experts model.
Mixture of Regression Experts in fMRI Encoding
We argue that each expert learns a certain region of brain activations corresponding to its category of words, which solves the problem of identifying the regions with a simple encoding model.
Learning Photography Aesthetics with Deep CNNs
To obtain both accuracy and human interpretation of the score, we advocate learning the aesthetic attributes along with the prediction of the overall score.
