Search Results for author: Mike Davies

Found 21 papers, 4 papers with code

Sampling Theorems for Unsupervised Learning in Linear Inverse Problems

no code implementations23 Mar 2022 Julián Tachella, Dongdong Chen, Mike Davies

In this paper, we present necessary and sufficient sampling conditions for learning the signal model from partial measurements which only depend on the dimension of the model, and the number of operators or properties of the group action that the model is invariant to.

Dictionary Learning Matrix Completion

Sampling Theorems for Learning from Incomplete Measurements

no code implementations28 Jan 2022 Julián Tachella, Dongdong Chen, Mike Davies

In many real-world settings, only incomplete measurement data are available which can pose a problem for learning.

Efficient Neuromorphic Signal Processing with Loihi 2

no code implementations5 Nov 2021 Garrick Orchard, E. Paxon Frady, Daniel Ben Dayan Rubin, Sophia Sanborn, Sumit Bam Shrestha, Friedrich T. Sommer, Mike Davies

The biologically inspired spiking neurons used in neuromorphic computing are nonlinear filters with dynamic state variables -- very different from the stateless neuron models used in deep learning.

Audio Classification Optical Flow Estimation

A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse Problems

no code implementations20 Jun 2020 Junqi Tang, Mike Davies

In this work we propose an efficient stochastic plug-and-play (PnP) algorithm for imaging inverse problems.

The Neural Tangent Link Between CNN Denoisers and Non-Local Filters

no code implementations CVPR 2021 Julián Tachella, Junqi Tang, Mike Davies

While the NTK theory accurately predicts the filter associated with networks trained using standard gradient descent, our analysis shows that it falls short to explain the behaviour of networks trained using the popular Adam optimizer.

Image Denoising Image Restoration

SPRING: A fast stochastic proximal alternating method for non-smooth non-convex optimization

no code implementations27 Feb 2020 Derek Driggs, Junqi Tang, Jingwei Liang, Mike Davies, Carola-Bibiane Schönlieb

We introduce SPRING, a novel stochastic proximal alternating linearized minimization algorithm for solving a class of non-smooth and non-convex optimization problems.

Image Deconvolution Stochastic Optimization Optimization and Control 90C26

The Practicality of Stochastic Optimization in Imaging Inverse Problems

no code implementations22 Oct 2019 Junqi Tang, Karen Egiazarian, Mohammad Golbabaee, Mike Davies

We investigate this phenomenon and propose a theory-inspired mechanism for the practitioners to efficiently characterize whether it is beneficial for an inverse problem to be solved by stochastic optimization techniques or not.

Deblurring Image Deblurring +1

The role of invariance in spectral complexity-based generalization bounds

no code implementations23 May 2019 Konstantinos Pitas, Andreas Loukas, Mike Davies, Pierre Vandergheynst

Deep convolutional neural networks (CNNs) have been shown to be able to fit a random labeling over data while still being able to generalize well for normal labels.

Generalization Bounds

Revisiting hard thresholding for DNN pruning

no code implementations21 May 2019 Konstantinos Pitas, Mike Davies, Pierre Vandergheynst

Recently developed smart pruning algorithms use the DNN response over the training set for a variety of cost functions to determine redundant network weights, leading to less accuracy degradation and possibly less retraining time.

CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstruction

1 code implementation3 Oct 2018 Mohammad Golbabaee, Zhouye Chen, Yves Wiaux, Mike Davies

Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy computations of a matched-filtering step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications.

Dimensionality Reduction

FeTa: A DCA Pruning Algorithm with Generalization Error Guarantees

1 code implementation12 Mar 2018 Konstantinos Pitas, Mike Davies, Pierre Vandergheynst

Recent DNN pruning algorithms have succeeded in reducing the number of parameters in fully connected layers, often with little or no drop in classification accuracy.

General Classification

Cheap DNN Pruning with Performance Guarantees

no code implementations ICLR 2018 Konstantinos Pitas, Mike Davies, Pierre Vandergheynst

Recent DNN pruning algorithms have succeeded in reducing the number of parameters in fully connected layers often with little or no drop in classification accuracy.

Classification General Classification

PAC-Bayesian Margin Bounds for Convolutional Neural Networks

1 code implementation30 Dec 2017 Konstantinos Pitas, Mike Davies, Pierre Vandergheynst

Recently the generalization error of deep neural networks has been analyzed through the PAC-Bayesian framework, for the case of fully connected layers.

Sparse Coding by Spiking Neural Networks: Convergence Theory and Computational Results

no code implementations15 May 2017 Ping Tak Peter Tang, Tsung-Han Lin, Mike Davies

With a moderate but well-defined assumption, we prove that the SNN indeed solves sparse coding.

Dictionary learning for fast classification based on soft-thresholding

no code implementations9 Feb 2014 Alhussein Fawzi, Mike Davies, Pascal Frossard

The dictionary learning problem, which jointly learns the dictionary and linear classifier, is cast as a difference of convex (DC) program and solved efficiently with an iterative DC solver.

Classification Dictionary Learning +1

A Compressed Sensing Framework for Magnetic Resonance Fingerprinting

no code implementations9 Dec 2013 Mike Davies, Gilles Puy, Pierre Vandergheynst, Yves Wiaux

Inspired by the recently proposed Magnetic Resonance Fingerprinting (MRF) technique, we develop a principled compressed sensing framework for quantitative MRI.

Information Theory Information Theory

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