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09/01/2022
Learning correspondences of cardiac motion from images using biomechanics-informed modeling
Learning spatial-temporal correspondences in cardiac motion from images ...
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03/15/2022
Surrogate Gap Minimization Improves Sharpness-Aware Training
The recently proposed Sharpness-Aware Minimization (SAM) improves genera...
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10/11/2021
Momentum Centering and Asynchronous Update for Adaptive Gradient Methods
We propose ACProp (Asynchronous-centering-Prop), an adaptive optimizer w...
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04/15/2021
Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity
Heterogeneous presentation of a neurological disorder suggests potential...
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02/09/2021
MALI: A memory efficient and reverse accurate integrator for Neural ODEs
Neural ordinary differential equations (Neural ODEs) are a new family of...
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10/15/2020
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
Most popular optimizers for deep learning can be broadly categorized as ...
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07/29/2020
Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis
Understanding how certain brain regions relate to a specific neurologica...
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06/03/2020
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Neural ordinary differential equations (NODEs) have recently attracted i...
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10/15/2019
Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI
Recurrent neural networks (RNNs) were designed for dealing with time-ser...
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09/30/2019
Decision Explanation and Feature Importance for Invertible Networks
Deep neural networks are vulnerable to adversarial attacks and hard to i...
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08/27/2019
Domain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation
Domain Adaptation (DA) has the potential to greatly help the generalizat...
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08/09/2019
Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection
Significant progress has been made using fMRI to characterize the brain ...
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07/23/2019
Invertible Network for Classification and Biomarker Selection for ASD
Determining biomarkers for autism spectrum disorder (ASD) is crucial to ...
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07/02/2019
Graph Neural Network for Interpreting Task-fMRI Biomarkers
Finding the biomarkers associated with ASD is helpful for understanding ...
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12/14/2018
Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery
Discovering imaging biomarkers for autism spectrum disorder (ASD) is cri...
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11/27/2018
ShelfNet for Real-time Semantic Segmentation
In this project, we present ShelfNet, a lightweight convolutional neural...
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10/28/2018
Prediction of severity and treatment outcome for ASD from fMRI
Autism spectrum disorder (ASD) is a complex neurodevelopmental syndrome....
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10/17/2018
LadderNet: Multi-path networks based on U-Net for medical image segmentation
U-Net has been providing state-of-the-art performance in many medical im...
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10/17/2018
Prediction of treatment outcome for autism from structure of the brain based on sure independence screening
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder,...
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08/23/2018
