We define consistent evidence to be both compatible and sufficient with respect to model predictions.
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text.
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.
Ranked #1 on Image Inpainting on FFHQ 1024 x 1024
Estimating mutual information between continuous random variables is often intractable and extremely challenging for high-dimensional data.
Supervised machine learning models often associate irrelevant nuisance factors with the prediction target, which hurts generalization.
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.
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.
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.
We present two related methods for deriving connectivity-based brain atlases from individual connectomes.
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.
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.
There is no consensus on how to construct structural brain networks from diffusion MRI.
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.
One of the primary objectives of human brain mapping is the division of the cortical surface into functionally distinct regions, i. e. parcellation.
In the present work we demonstrate the use of a parcellation free connectivity model based on Poisson point processes.