Search Results for author:
Found 12 papers, 5 papers with code
Maintaining Adversarial Robustness in Continuous Learning
Adversarial robustness is essential for security and reliability of machine learning systems.
Interpretable System Identification and Long-term Prediction on Time-Series Data
Time-series prediction has drawn considerable attention during the past decades fueled by the emerging advances of deep learning methods.
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting.
Graph Neural Network
Multivariate Time Series Forecasting
+1
MFPP: Morphological Fragmental Perturbation Pyramid for Black-Box Model Explanations
Deep neural networks (DNNs) have recently been applied and used in many advanced and diverse tasks, such as medical diagnosis, automatic driving, etc.
Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis
With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent years.
PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-Spoofing
Face anti-spoofing has become an increasingly important and critical security feature for authentication systems, due to rampant and easily launchable presentation attacks.
Channel Pruning via Optimal Thresholding
By using OT, most negligible or unimportant channels are pruned to achieve high sparsity while minimizing performance degradation.
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers
In conjunction with the standard cross-entropy loss, we minimize the novel loss to train an ensemble of classifiers.
Segmenting Brain Tumors with Symmetry
We explore encoding brain symmetry into a neural network for a brain tumor segmentation task.
A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis
In this work, we examine a searchlight based shared response model to identify shared information in small contiguous regions (searchlights) across the whole brain.
A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation
We examine two ways to combine the ideas of a factor model and a searchlight based analysis to aggregate multi-subject fMRI data while preserving spatial locality.
Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets
The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted.
