The advent of increased computing capabilities, along with recent theoretical and numerical breakthroughs in the fields of signal processing, computational harmonic analysis, inverse problem solving, high-dimensional statistics and convex optimization, have boosted interactions between low-complexity data models (e.g., sparse or low-rank data models) and novel data sensing techniques.
In a nutshell, low-complexity data models aim at capturing, modeling and exploiting “just the information you need” in the ubiquitous data deluge characterizing any scientific or technological achievements. High dimensional objects can be thus reconstructed using little information. However, further developments and novel ideas are still required to meet new challenges, especially for efficiently dealing with complex data structures of “real life” applications and for interconnecting such models with other theoretical and applied fields.
The iTWIST workshop aims at fostering collaboration between international scientific teams for developing new theories, applications and generalizations of low-complexity models. For this edition, iTWIST was divided into two parts, a 2-day doctoral school, followed by the actual workshop for three days. All videos are available below.
Deep learning medical image analysis-1
Deep learning medical image analysis-2
Optimal transport-1
Optimal transport-2
Optimal transport-3
Global optimization branchandbound-1
Global optimization branchandbound-2
NonNegative low rank approximations-1
NonNegative low rank approximations-2
Modeling Low Dimensional Structure with Variational Autoencoders
AI for Sound : from independent component analysis and sparse
A function space view of overparameterized neural networks
Rank optimality for the Burer Monteiro factorization
Leveraging low dimensional models for human in the loop machine learning tasks
The BLASSO continuous dictionaries for sparser reconstructions
The landscape of dictionary learning
Primal dual splitting scheme with backtracking for handling with epigraphic constraint and sparse analysis regularization
SLS Single l1 Selection a new greedy algorithm with an l1 norm selection rule
MIP and Set Covering approaches for Sparse Approximation
A partially collapsed sampler for unsupervised nonnegative spike train restoration
Learning sparse structures for physics inspired compressed sensing
Exact recovery analysis of non negative orthogonal greedy algorithms
A mathematical approach to resilience
Learning to Sketch for Compressive Clustering
Gaussian Compression Stream Principle and Preliminary Results
Off the grid data driven optimization of sampling schemes in MRI
When compressive learning fails blame the decoder or the sketch
Keep the phase Signal recovery in phase only compressive sensing
One Bit to Rule Them All Binarizing the Reconstruction in 1 bit Compressive Sensing
Compressed Sensing with 1D Total Variation Breaking Sample Complexity Barriers via Non Uniform Recovery
Quantized Compressed Sensing by Rectified Linear Units
Unsupervised dictionary learning for anomaly detection
Lightweight U-Net for high resolution breast imaging
On variational auto encoders for fixed graph mesh learning
Curriculum learning to deal with noisy labels
Unmixing 2D HSQC NMR mixtures with NMF and sparsity
Successive Nonnegative Projection Algorithm for Linear Quadratic Mixtures
Analysis of short time orthogonal transform learning for NMF
Sparse Separable Nonnegative Matrix Factorization
An algorithm for non convex off the grid sparse spike estimation with a minimum separation constraint
Boosting the Sliding Frank Wolfe solver for 3D deconvolution
Continuous dictionaries meet low rank tensor approximations
Factorization over interpolation : A fast continuous orthogonal matching pursuit
Translation invariant interpolation of parametric dictionaries
Joint deconvolution and blind source separation on the sphere with an application to radio astronomy
Sparse and Smooth Spectral Clustering in the Dynamic SBM
Some detection tests for low complexity data models and unknown background distribution
Multi view Subspace Clustering for Hyperspectral Images
Blind Audio Source Separation with Minimum Volume Beta Divergence NMF
Phase retrieval with Bregm an divergences Application to audio signal recovery
Bandwidth extension of muscical audio signals using dilated convolutional neural networks
High resolution three dimensional crystalline microscopy
Cross modal registration using point clouds and graph matching in the context of correlative microscopies
Uniqueness of the random illumination microscopy variance equation
Local mean preserving post processing ste for non negativity enforcement in PET imaging application to 90Y PET
Leveraging RSF and PET images for prognosis of Multiple Myeloma at diagnosis
Morphological components analysis for circumstellar disks imaging
Interpretable Deep Multimodal Image Super Resolution
