Search Results for author: Daniel Moyer

Found 21 papers, 11 papers with code

NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics

1 code implementation9 Jun 2023 Anwar Said, Roza G. Bayrak, Tyler Derr, Mudassir Shabbir, Daniel Moyer, Catie Chang, Xenofon Koutsoukos

We delve deeply into the dataset generation search space by crafting 35 datasets within both static and dynamic contexts, running in excess of 15 baseline methods for benchmarking.


SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI

1 code implementation22 Jun 2022 Junshen Xu, Daniel Moyer, P. Ellen Grant, Polina Golland, Juan Eugenio Iglesias, Elfar Adalsteinsson

Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion.

3D Reconstruction

Harmonization and the Worst Scanner Syndrome

no code implementations15 Jan 2021 Daniel Moyer, Polina Golland

We show that for a wide class of harmonization/domain-invariance schemes several undesirable properties are unavoidable.

Bayesian Image Reconstruction using Deep Generative Models

2 code implementations8 Dec 2020 Razvan V Marinescu, Daniel Moyer, Polina Golland

Our method, Bayesian Reconstruction through Generative Models (BRGM), uses a single pre-trained generator model to solve different image restoration tasks, i. e., super-resolution and in-painting, by combining it with different forward corruption models.

Image Denoising Image Inpainting +4

DEMI: Discriminative Estimator of Mutual Information

1 code implementation5 Oct 2020 Ruizhi Liao, Daniel Moyer, Polina Golland, William M. Wells

Estimating mutual information between continuous random variables is often intractable and extremely challenging for high-dimensional data.

Representation Learning

Invariant Representations through Adversarial Forgetting

no code implementations11 Nov 2019 Ayush Jaiswal, Daniel Moyer, Greg Ver Steeg, Wael Abd-Almageed, Premkumar Natarajan

We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechanism.

Efficient Covariance Estimation from Temporal Data

2 code implementations30 May 2019 Hrayr Harutyunyan, Daniel Moyer, Hrant Khachatrian, Greg Ver Steeg, Aram Galstyan

Estimating the covariance structure of multivariate time series is a fundamental problem with a wide-range of real-world applications -- from financial modeling to fMRI analysis.

Time Series Time Series Analysis

Exact Rate-Distortion in Autoencoders via Echo Noise

1 code implementation NeurIPS 2019 Rob Brekelmans, Daniel Moyer, Aram Galstyan, Greg Ver Steeg

The noise is constructed in a data-driven fashion that does not require restrictive distributional assumptions.

Representation Learning

Scanner Invariant Representations for Diffusion MRI Harmonization

no code implementations10 Apr 2019 Daniel Moyer, Greg Ver Steeg, Chantal M. W. Tax, Paul M. Thompson

Purpose: In the present work we describe the correction of diffusion-weighted MRI for site and scanner biases using a novel method based on invariant representation.

Fairness Image Reconstruction

Connectivity-Driven Brain Parcellation via Consensus Clustering

no code implementations10 Aug 2018 Anvar Kurmukov, Ayagoz Mussabayeva, Yulia Denisova, Daniel Moyer, Boris Gutman

We present two related methods for deriving connectivity-based brain atlases from individual connectomes.


Measures of Tractography Convergence

no code implementations12 Jun 2018 Daniel Moyer, Paul M. Thompson, Greg Ver Steeg

In the present work, we use information theory to understand the empirical convergence rate of tractography, a widely-used approach to reconstruct anatomical fiber pathways in the living brain.

Invariant Representations without Adversarial Training

1 code implementation NeurIPS 2018 Daniel Moyer, Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, Aram Galstyan

Representations of data that are invariant to changes in specified factors are useful for a wide range of problems: removing potential biases in prediction problems, controlling the effects of covariates, and disentangling meaningful factors of variation.

Representation Learning

Fast structure learning with modular regularization

3 code implementations NeurIPS 2019 Greg Ver Steeg, Hrayr Harutyunyan, Daniel Moyer, Aram Galstyan

We also use our approach for estimating covariance structure for a number of real-world datasets and show that it consistently outperforms state-of-the-art estimators at a fraction of the computational cost.

A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex

1 code implementation2 Mar 2017 Daniel Moyer, Boris A. Gutman, Neda Jahanshad, Paul M. Thompson

One of the primary objectives of human brain mapping is the division of the cortical surface into functionally distinct regions, i. e. parcellation.

An Empirical Study of Continuous Connectivity Degree Sequence Equivalents

no code implementations18 Nov 2016 Daniel Moyer, Boris A. Gutman, Joshua Faskowitz, Neda Jahanshad, Paul M. Thompson

In the present work we demonstrate the use of a parcellation free connectivity model based on Poisson point processes.

Point Processes

Cannot find the paper you are looking for? You can Submit a new open access paper.