Search Results for author:
Found 15 papers, 8 papers with code
Zero-shot Learning of Individualized Task Contrast Prediction from Resting-state Functional Connectomes
Given sufficient pairs of resting-state and task-evoked fMRI scans from subjects, it is possible to train ML models to predict subject-specific task-evoked activity using resting-state functional MRI (rsfMRI) scans.
GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Studies
We call our approach GLACIAL, which stands for "Granger and LeArning-based CausalIty Analysis for Longitudinal studies."
A Transformer-based Neural Language Model that Synthesizes Brain Activation Maps from Free-Form Text Queries
In this paper, we present Text2Brain, an easy to use tool for synthesizing brain activation maps from open-ended text queries.
Text2Brain: Synthesis of Brain Activation Maps from Free-form Text Query
In this work, we propose Text2Brain, a neural network approach for coordinate-based meta-analysis of neuroimaging studies to synthesize brain activation maps from open-ended text queries.
Neural encoding with visual attention
Using concurrent eye-tracking and functional Magnetic Resonance Imaging (fMRI) recordings from a large cohort of human subjects watching movies, we first demonstrate that leveraging gaze information, in the form of attentional masking, can significantly improve brain response prediction accuracy in a neural encoding model.
Domain-shift Conditioning using Adaptable Filtering via Hierarchical Embeddings for Robust Chinese Spell Check
Spell check is a useful application which processes noisy human-generated text.
From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts fromResting-state Functional Connectivity
Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals.
From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-state Functional Connectivity
Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals.
A shared neural encoding model for the prediction of subject-specific fMRI response
The increasing popularity of naturalistic paradigms in fMRI (such as movie watching) demands novel strategies for multi-subject data analysis, such as use of neural encoding models.
Hierarchical Character Embeddings: Learning Phonological and Semantic Representations in Languages of Logographic Origin using Recursive Neural Networks
Using recursive neural network imposes a prior on the mapping from logographs to embeddings since the network must read in the sub-units in logographs according to the order specified by the recursive structures.
Isolating the Effects of Modeling Recursive Structures: A Case Study in Pronunciation Prediction of Chinese Characters
Finding that explicitly modeling structures leads to better generalization, we consider the task of predicting Cantonese pronunciations of logographs (Chinese characters) using logographs{'} recursive structures.
Machine learning in resting-state fMRI analysis
Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI.
Phonology-Augmented Statistical Framework for Machine Transliteration using Limited Linguistic Resources
The problem is compounded by the limited linguistic resources available when converting foreign words to transliterated words in the target language.
Multimodal neural pronunciation modeling for spoken languages with logographic origin
Graphemes of most languages encode pronunciation, though some are more explicit than others.
Statistical Machine Transliteration Baselines for NEWS 2018
This paper reports the results of our trans-literation experiments conducted on NEWS 2018 Shared Task dataset.
