Search Results for author:
Found 18 papers, 10 papers with code
Guidelines For The Choice Of The Baseline in XAI Attribution Methods
Given the broad adoption of artificial intelligence, it is essential to provide evidence that AI models are reliable, trustable, and fair.
Spectral Introspection Identifies Group Training Dynamics in Deep Neural Networks for Neuroimaging
Neural networks, whice have had a profound effect on how researchers study complex phenomena, do so through a complex, nonlinear mathematical structure which can be difficult for human researchers to interpret.
Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse
Our understanding of learning dynamics of deep neural networks (DNNs) remains incomplete.
Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey
Secondly, we discuss how multiple recent neuroimaging studies leveraged model interpretability to capture anatomical and functional brain alterations most relevant to model predictions.
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes
Coarse labels do not capture the long-tailed spectrum of brain disorder phenotypes, which leads to a loss of generalizability of the model that makes them less useful in diagnostic settings.
Algorithm-Agnostic Explainability for Unsupervised Clustering
The methods are (1) easy to implement and (2) broadly applicable across clustering algorithms, which could make them highly impactful.
Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data
We show that self-supervised models are not as robust as expected based on their results in natural imaging benchmarks and can be outperformed by supervised learning with dropout.
Peering Beyond the Gradient Veil with Distributed Auto Differentiation
We introduce an innovative, communication-friendly approach for training distributed DNNs, which capitalizes on the outer-product structure of the gradient as revealed by the mechanics of auto-differentiation.
Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease
Sensory input from multiple sources is crucial for robust and coherent human perception.
On self-supervised multi-modal representation learning: An application to Alzheimer's disease
In this paper, we introduce a way to exhaustively consider multimodal architectures for contrastive self-supervised fusion of fMRI and MRI of AD patients and controls.
Whole MILC: generalizing learned dynamics across tasks, datasets, and populations
In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC).
Multidataset Independent Subspace Analysis with Application to Multimodal Fusion
In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce.
Improved Differentially Private Decentralized Source Separation for fMRI Data
In this work, we propose a differentially private algorithm for performing ICA in a decentralized data setting.
Run, skeleton, run: skeletal model in a physics-based simulation
In this paper, we present our approach to solve a physics-based reinforcement learning challenge "Learning to Run" with objective to train physiologically-based human model to navigate a complex obstacle course as quickly as possible.
Spatio-temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks
We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI).
Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data
Independent component analysis (ICA), as an approach to the blind source-separation (BSS) problem, has become the de-facto standard in many medical imaging settings.
Deep learning for neuroimaging: a validation study
In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data.
Block Coordinate Descent for Sparse NMF
However, present algorithms designed for optimizing the mixed norm L$_1$/L$_2$ are slow and other formulations for sparse NMF have been proposed such as those based on L$_1$ and L$_0$ norms.
