6 papers
Learning to Query History: Nonstationary Classification via Learned Retrieval
Jimmy Gammell, Bishal Thapaliya, Yoon Jung +3
Nonstationarity is ubiquitous in practical classification settings, leading deployed models to perform poorly even when they generalize well to holdout sets available at training t…
SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning
Riyasat Ohib, Bishal Thapaliya, Gintare Karolina Dziugaite +3
In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subne…
Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity
Bishal Thapaliya, Esra Akbas, Ram Sapkota +3
Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the r…
Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data
Bishal Thapaliya, Esra Akbas, Jiayu Chen +5
Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for t…
ECGN: A Cluster-Aware Approach to Graph Neural Networks for Imbalanced Classification
Bishal Thapaliya, Anh Nguyen, Yao Lu +7
Classifying nodes in a graph is a common problem. The ideal classifier must adapt to any imbalances in the class distribution. It must also use information in the clustering struct…
DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks
Bishal Thapaliya, Robyn Miller, Jiayu Chen +8
Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs…