research
∙
03/16/2023
The Intel Neuromorphic DNS Challenge
A critical enabler for progress in neuromorphic computing research is th...
share
research
∙
09/05/2022
Imaging with Equivariant Deep Learning
From early image processing to modern computational imaging, successful ...
research
∙
03/23/2022
Sampling Theorems for Unsupervised Learning in Linear Inverse Problems
Solving a linear inverse problem requires knowledge about the underlying...
research
∙
01/28/2022
Sampling Theorems for Learning from Incomplete Measurements
In many real-world settings, only incomplete measurement data are availa...
research
∙
11/05/2021
Efficient Neuromorphic Signal Processing with Loihi 2
The biologically inspired spiking neurons used in neuromorphic computing...
research
∙
06/09/2021
Vector Symbolic Architectures as a Computing Framework for Nanoscale Hardware
This article reviews recent progress in the development of the computing...
research
∙
08/17/2020
One Bit to Rule Them All : Binarizing the Reconstruction in 1-bit Compressive Sensing
This work focuses on the reconstruction of sparse signals from their 1-b...
research
∙
06/20/2020
A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse Problems
In this work we propose an efficient stochastic plug-and-play (PnP) algo...
research
∙
06/03/2020
CNN Denoisers As Non-Local Filters: The Neural Tangent Denoiser
Convolutional Neural Networks (CNNs) are now a well-established tool for...
research
∙
04/27/2020
Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springs
Neuromorphic computing applies insights from neuroscience to uncover inn...
research
∙
01/23/2020
Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders
We propose a dictionary-matching-free pipeline for multi-parametric quan...
research
∙
10/22/2019
The Practicality of Stochastic Optimization in Imaging Inverse Problems
In this work we investigate the practicality of stochastic gradient desc...
research
∙
05/23/2019
Some limitations of norm based generalization bounds in deep neural networks
Deep convolutional neural networks have been shown to be able to fit a l...
research
∙
05/21/2019
Revisiting hard thresholding for DNN pruning
The most common method for DNN pruning is hard thresholding of network w...
research
∙
10/03/2018
CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstruction
Current popular methods for Magnetic Resonance Fingerprint (MRF) recover...
research
∙
08/27/2018
Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net
We explore the use of deep learning for breast mass segmentation in mamm...
research
∙
03/12/2018
FeTa: A DCA Pruning Algorithm with Generalization Error Guarantees
Recent DNN pruning algorithms have succeeded in reducing the number of p...
research
∙
12/30/2017
PAC-Bayesian Margin Bounds for Convolutional Neural Networks - Technical Report
Recently the generalisation error of deep neural networks has been analy...
research
∙
05/15/2017
Sparse Coding by Spiking Neural Networks: Convergence Theory and Computational Results
In a spiking neural network (SNN), individual neurons operate autonomous...
research
∙
02/09/2014