944 citations · 3.4k across the 10 of their papers we have counts for
11 papers
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
Jure Zbontar, Li Jing, Ishan Misra +2
Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large computer vision benchmarks. A successful approach to SSL is to learn embeddings which are…
Implicit Rank-Minimizing Autoencoder
Li Jing, Jure Zbontar, Yann LeCun
An important component of autoencoders is the method by which the information capacity of the latent representation is minimized or limited. In this work, the rank of the covarianc…
End-to-End Variational Networks for Accelerated MRI Reconstruction
Anuroop Sriram, Jure Zbontar, Tullie Murrell +5
The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (para…
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Florian Knoll, Tullie Murrell, Anuroop Sriram +8
Purpose: To advance research in the field of machine learning for MR image reconstruction with an open challenge. Methods: We provided participants with a dataset of raw k-space da…
GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction
Anuroop Sriram, Jure Zbontar, Tullie Murrell +3
Magnetic Resonance Image (MRI) acquisition is an inherently slow process which has spurred the development of two different acceleration methods: acquiring multiple correlated samp…
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Jure Zbontar, Florian Knoll, Anuroop Sriram +24
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applicati…
Simulating single-coil MRI from the responses of multiple coils
Mark Tygert, Jure Zbontar
We convert the information-rich measurements of parallel and phased-array MRI into noisier data that a corresponding single-coil scanner could have taken. Specifically, we replace…
Compressed sensing with a jackknife and a bootstrap
Mark Tygert, Rachel Ward, Jure Zbontar
Compressed sensing proposes to reconstruct more degrees of freedom in a signal than the number of values actually measured. Compressed sensing therefore risks introducing errors --…
Fast Incremental Learning for Off-Road Robot Navigation
Artem Provodin, Liila Torabi, Beat Flepp +5
A promising approach to autonomous driving is machine learning. In such systems, training datasets are created that capture the sensory input to a vehicle as well as the desired re…
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
Jure Žbontar, Yann LeCun
We present a method for extracting depth information from a rectified image pair. Our approach focuses on the first stage of many stereo algorithms: the matching cost computation.…
Computing the Stereo Matching Cost with a Convolutional Neural Network
Jure Žbontar, Yann LeCun
We present a method for extracting depth information from a rectified image pair. We train a convolutional neural network to predict how well two image patches match and use it to…