After more than 9 years of existence of DANTE as a team focused on dynamic networks at large, and in the context of a strengthening of the research activities in statistical machine learning and signal processing in DANTE (and more largely on the academic site of Lyon Saint-Etienne), it was decided to split the DANTE team into two new teams. All activities related to network communications (with a focus on wireless networks and performance evaluation) are now part of the new team HowNet and are not covered in this scientific report. The new scientific contours of DANTE are described below and this activity report focuses on the machine learning and signal processing activities of DANTE in 2021.

Building on a culture at the interface of signal modeling, mathematical optimization and statistical machine learning, the global objective of DANTE is to develop computationally efficient and mathematically founded methods and models to process high-dimensional data. Our ambition is to develop frugal signal processing and machine learning methods able to exploit structured models, intrinsically associated to resource-efficient implementations, and endowed with solid statistical guarantees.

The idea of frugal approaches means algorithms relying on a controlled use of computing resources, but also methods whose expressivity and flexibility provably relies on the versatile notion of sparsity. This is expected to avoid the current pitfalls of costly over-parameterizations and to robustify the approaches with respect to adversarial examples and overfitting. More specifically, it is essential to contribute to the understanding of methods based on neural networks, in order to improve their performance and most of all, their efficiency in resource-limited environments.

To make statistical machine learning both more frugal and more interpretable, it is important to develop techniques able to exploit not only high-dimensional data but also models in various forms when available. When some partial knowledge is available about some phenomena related to the processed data, e.g. under the form of a physical model such as a partial differential equation, or as a graph capturing local or non-local correlations, the goal is to use this knowledge as an inspiration to adapt machine learning algorithms. The main challenge is to flexibly articulate a priori knowledge and data-driven information, in order to achieve a controlled extrapolation of predicted phenomena much beyond the particular type of data on which they were observed, and even in applications where training data is scarce.

The notion of sparsity and its structured avatars –notably via graphs– is known to play a fundamental role in ensuring the identifiability of decompositions in latent spaces, for example for high-dimensional inverse problems in signal processing. The team's ambition is to deploy these ideas to ensure not only frugality but also some level of explainability of decisions and an interpretability of learned parameters, which is an important societal stake for the acceptability of “algorithmic decisions”. Learning in small-dimensional latent spaces is also a way to spare computing resources and, by limiting the public exposure of data, it is expected to enable tunable and quantifiable tradeoffs between the utility of the developed methods and their ability to preserve privacy.

This project is resolutely at the interface of signal modeling, mathematical optimization and statistical machine learning, and concentrates on scientific objectives that are both ambitious –as they are difficult and subject to a strong international competition– and realistic thanks to the richness and complementarity of skills they mobilize in the team.

Sparsity constitutes a backbone for this project, not only as a target to ensure resource-efficiency and privacy, but also as prior knowledge to be exploited to ensure the identifiability of parameters and the interpretability of results. Graphs are its necessary alter ego, to flexibly model and exploit relations between variables, signals, and phenomena, whether these relations are known a priori or to be inferred from data.
Lastly, advanced large-scale optimization is a key tool to handle in a statistically controlled and algorithmically efficient way the dynamic and incremental aspects of learning in varying environments.

The scientific activity of the project is articulated around the three axes described below. A common endeavor to these three axes consists in designing structured low-dimensional models, algorithms of bounded complexity to adjust these models to data through learning mechanisms, and a control of the performance of these algorithms to exploit these models on tasks ranging from low-level signal processing to the extraction of high-level information.

As now widely documented, the fact that a signal admits a sparse representation in some signal dictionary is an enabling factor not only to address a variety of inverse problems with high-dimensional signals and images, such as denoising, deconvolution, or declipping, but also to speedup or decrease the cost of the acquisition of analog signals in certain scenarios compatible with compressive sensing , . The flexibility of the models, which can incorporate learned dictionaries , as well as structured and/or low-rank variants of the now-classical sparse modeling paradigm , has been a key factor of the success of these approaches. Another important factor is the existence of algorithms of bounded complexity with provable performance, often associated to convex regularization and proximal strategies , , allowing to identify latent sparse signal representations from low-dimensional indirect observations.

While being now well-mastered (and in the core field of expertise of the team), these tools are typically constrained to relatively rigid settings where the unknown is described either as a sparse vector or a low-rank matrix or tensor in high (but finite) dimension. Moreover, the algorithms hardly scale to the dimensions needed to handle inverse problems arising from the discretization of physical models (e.g., for 3D wavefield reconstruction). A major challenge is to establish a comprehensive algorithmic and theoretical toolset to handle continuous notions of sparsity , which have been identified as a way to potentially circumvent these bottlenecks. The other main challenge is to extend the sparse modeling paradigm to resource-efficient and interpretable statistical machine learning. The methodological and conceptual output of this axis provides tools for Axes 2 and 3, which in return fuel the questions investigated in this axis.

1.1 Versatile and efficient sparse modeling. The goal is to propose flexible and resource-efficient sparse models, possibly leveraging classical notions of dictionaries and structured factorization, but also the notion of sparsity in continuous domains (e.g. for sketched clustering, mixture model estimation, or image super-resolution), low-rank tensor representations, and neural networks with sparse connection patterns.

Besides the empirical validation of these models and of the related algorithms on a diversity of targeted applications, the challenge is to determine conditions under which their success can be mathematically controlled, and to determine the fundamental tradeoffs between the expressivity of these models and their complexity.

Graphs provide synthetic and sparse representations of the interactions between potentially high-dimensional data, whether in terms of proximity, statistical correlation, functional similarity, or simple affinities.
One central task in this domain is how to infer such discrete structures, from the observations, in a way that best accounts for the ties between data, without becoming too complex due to spurious relationships. The graphical lasso is among the most popular and successful algorithm to build a sparse representation of the relations between time series (observed at each node) and that unveils relevant patterns of the data. Recent works (e.g. ) strived to emphasize the clustered structure of the data by imposing spectral constraints to the Laplacian of the sought graphs, with the aim to improve the performance of spectral approaches to unsupervised classification. In this direction, several challenges remain, such as for instance the transposition of the framework to graph-based semi-supervised learning , where natural models are stochastic block models rather than strictly multi-component graphs (e.g. Gaussian mixtures models). As it is done in , the standard sketched graphical lasso. There exists other situations where the graph is known a priori and does not need to be inferred from the data. This is for instance the case when the data naturally lie on a graph (e.g. social networks or geographical graphs) and so, one has to combine this data structure with the attributes (or measures) carried by the nodes or the edges of these graphs. Graph signal processing (GSP) , which underwent methodological developments at a very rapid pace in recent years, is precisely an approach to jointly exploit algebraically these structures and attributes, either by filtering them, by re-organizing them, or by reducing them to principal components. However, as it tends to be more and more the case, data collection processes yield very large data sets with high dimensional graphs. In contrast to standard digital signal processing that relies on regular graph structures (cycle graph or cartesian grid) treating complex structured data in a global form is not an easily scalable task . Hence, the notion of distributed GSP , has naturally emerged. Yet, very little has been done on graph signals supported on dynamical graphs that undergo vertices/edges editions.

2.2 Distributed and adaptive learning on graphs.
The availability of a known graph structure underlying training data offers many opportunities to develop distributed approaches, open perspectives where graph signal processing and machine learning can mutually fertilize each other.

Some classifiers can be formalized as solutions of a constrained optimization problem, and an important objective is then to reduce their global complexity by developing distributed versions of these algorithms. Compared to costly centralized solutions, distributing the operations by restricting them to local node neighborhoods will enable solutions that are both more frugal and more privacy-friendly. In the case of dynamic graphs, the idea is to get inspiration from adaptive processing techniques to make the algorithms able to track the temporal evolution of data, either in terms of structural evolution or of temporal variations of the attributes. This aspect finds a natural continuation in the objectives of Axis 3.

With the resurgence of neural networks approaches in machine learning, training times of the order of days, weeks, or even months are common. Mainstream research in deep learning somehow applies it to an increasingly large class of problems and uses the general wisdom to improve the models prediction accuracy by “stacking more layers”, making the approach ever more resource-hungry. Underpinning theory on which resources are needed for a network architecture to achieve a given accuracy is still in its infancy. Efficient scaling of such techniques to massive sample sizes or dimensions in a resource-restricted environment remains a challenge and is a particularly active field of academic and industrial R&D, with recent interest in techniques such as sketching, dimension reduction, and approximate optimization.

A central challenge is to develop novel approximate techniques with reduced computational and memory imprint. For certain unsupervised learning tasks such as PCA, unsupervised clustering, or parametric density estimation, random features (e.g. random Fourier features ) allow to compute aggregated sketches guaranteed to preserve the information needed to learn, and no more: this has led to the compressive learning framework, which is endowed with statistical learning guarantees as well as privacy preservation guarantees . A sketch can be seen as an embedding of the empirical probability distribution of the dataset with a particular form of kernel mean embedding .
Yet, designing random features given a learning task remains something of an art, and a major challenge is to design provably good end-to-end sketching pipelines with controlled complexity for supervised classification, structured matrix factorization, and deep learning.

Another crucial direction is the use of dynamical learning methods, capable of exploiting wisely multiple representations at different scales of the problem at hand. For instance, many low and mixed-precision variants of gradient-based methods have been recently proposed , , which are however based on a static reduced precision policy, while a dynamic approach can lead to much improved energy-efficiency. Also, despite their massive success, gradient-based training methods still possess many weaknesses (low convergence rate, dependence on the tuning of the learning parameters, vanishing and exploding gradients) and the use of dynamical information promises to allow for the development of alternative methods, such as second-order or multilevel methods, which are as scalable as first-order methods but with faster convergence guarantees , .

The overall objective in this axis is to adapt in a controlled manner the information that is extracted from datasets or data streams and to dynamically use such information in learning, in order to optimize the tradeoffs between statistical significance, resource-efficiency, privacy-preservation and integration of a priori knowledge.

The primary objectives of this project, which is rooted in Signal Processing and Machine Learning methodology, are to develop flexible methods, endowed with solid mathematical foundations and efficient algorithmic implementations, that can be adapted to numerous application domains. We are nevertheless convinced that such methods are best developed in strong and regular connection with concrete applications, which are not only necessary to validate the approaches but also to fuel the methodological investigations with relevant and fruitful ideas. The following application domains are primarily investigated in partnership with research groups with the relevant expertise.

There is a strong need to drastically compress signal processing and machine learning models (typically, but not only, deep neural networks) to fit them on embedded devices. For example, on autonomous vehicles, due to strong constraints (reliability, energy consumption, production costs), the memory and computing resources of dedicated high-end image-analysis hardware are two orders of magnitude more limited than what is typically required to run state-of-the-art deep network models in real-time. The research conducted in the DANTE project finds direct applications in these areas, including: compressing deep neural networks to obtain low-bandwidth video-codecs that can run on smartphones with limited memory resources; sketched learning and sparse networks for autonomous vehicles; or sketching algorithms tailored to exploit optical processing units for energy efficient large-scale learning.

Many problems in imaging involve the reconstruction of large scale data from limited and noise-corrupted measurements. In this context, the research conducted in DANTE pays a special attention to modeling domain knowledge such as physical constraints or prior medical knowledge. This finds applications from physics to medical imaging, including: multiphase flow image characterization; near infrared polarization imaging in circumstellar imaging; compressive sensing for joint segmentation and high-resolution 3D MRI imaging; or graph signal processing for radio astronomy imaging with the Square Kilometer Array (SKA).

Based on collaborations with the relevant experts the team also regularly investigates applications in computational social science. For example, modeling infection disease epidemics requires efficient methods to reduce the complexity of large networked datasets while preserving the ability to feed effective and realistic data-driven models of spreading phenomena. In another area, estimating the vote transfer matrices between two elections is an ill-posed problem that requires the design of adapted regularization schemes together with the associated optimization algorithms.

Robust prediction of the spatio-temporal evolution of the reproduction number $R\left(t\right)$ of the Covid-19 pandemic from open data (Santé-Publique-France and the European Center for Disease Prevention).

Following our work of last year , where an algorithm exploiting sparsity and convex optimization was developed, and dynamic maps were proposed, we identified robustness to outliers as a critical issue.

This is addressed in a paper submitted for publication to a journal, using convex regularization .

P. Gonçalves was nominated Deputy Scientific Director of the new research center of Inria in Lyon.

R. Gribonval was a keynote speaker at the international conference EUSIPCO 2021 and an invited speaker at the national conference CAp21.

A survey paper on sketching for large-scale learning, summarizing in tutorial style a series of works of the team, was published in the September 2021 issue of the IEEE Signal Processing Magazine and made its front cover .

In an effort towards reproducible research, the default policy of the team is to release open-source code (typically python or matlab) associated to research papers that report experiments. When applicable and possible, more engineered software is developed and maintained over several years to provide more robust and consistent implementations of selected results.

Collaborations with Cédric Vincent-Cuaz (PhD student, MAASAI, Université Côte d'Azur), Rémi Flamary (CMAP, Ecole Polytechnique), Marco Corneli (MAASAI, Université Côte d'Azur) and Nicolas Courty (IRISA, Université Bretagne Sud).

The Gromov-Wasserstein (GW) distance is derived from optimal transport (OT) theory. The interest of OT lies both in its ability to provide relationships, connections, between sets of points and distances between probability distributions. By modeling graphs as probability distributions GW has become an important tool in many ML tasks involving structured data. Based on GW as a fidelity term, we proposed in an efficient graph dictionary learning algorithm that allows to describe graphs as a simple composition of smaller graphs (atoms of the dictionary). We proposed a stochastic algorithm capable of learning a dictionary-like representation in the complex setting where the graphs in the dataset arrive progressively in time. We showed that these representations are particularly efficient for tasks such as change detection for structured data and clustering of graphs. We proposed an alternative approach in which goal is to learn a single graph of large size whose subgraphs will best match (according to the GW criterion) the graphs of the dataset.

In another line of works, in collaboration with Clément Bonet (PhD student, IRISA, Université Bretagne-Sud), Nicolas Courty, François Septier (LMBA, Université de Bretagne Sud) and Lucas Drumetz ( Lab-STICC OSE, IMT Atlantique), we proposed an extension of the GW framerwork for shape matching problems . It consists in finding an optimal plan between the measures projected on a wisely chosen subspace and then completing it in a nearly optimal transport plan on the whole space. The advantage is to lower the computational complexity of the GW distance.

In a series of recent articles, we proposed the Diffusion Wasserstein (DW) distance, a generalization of the standard Wasserstein to undirected and connected graphs where nodes are described by feature vectors. Using the heat diffusion equation constructed on the exponential kernel of the graph's Laplacian, we locally average the attributes of the nodes over a neighborhood that is controlled by the diffusion time. Like the fused Gromov-Wasserstein distance, this mixed distance allows to compute an optimal transport plan that captures both the structural and the feature information of the graphs. A big advantage of the Diffusion Distance though, is its computational cost that remains significantly inferior to that of the fused Gromov Wasserstein distance. Moreover, applied to different domain adaptation tasks, we experimentally showed that in many difficult situations, the DW distance was able to outperform the most recent concurrent methods.

To further reduce the computational cost of the diffusion Wasserstein distance, we proposed to use a Chebyshev approximation of the diffusion operator applied to the features vectors. In the course of this work, we were also able to tighten the theoretical approximation bounds, which in turn permits to significantly improve estimates of the polynomial order for a prescribed error .

Finally, to address the classical problem of tuning the diffusion time, the unique free parameter of DW distance, we devised a triplet loss based method that permits to find the best diffusion time in the context of domain adaptation tasks .

Our paper studying the expressivity of sparse deep neural networks from an approximation theoretic perspective and highlighting the role of depth to enable efficient approximation of functions with very limited smoothness was published this year . Motivated by the importance of quantizing networks besides pruning them to achieve sparsity, we started to investigate the approximation theoretic properties of quantized deep networks, with the objective of defining and comparing the corresponding approximation classes with the unquantized ones.

Neural networks with the ReLU activation function are described by weights and bias parameters, and realized a piecewise linear continuous function. Natural scalings and permutations operations on the parameters leave the realization unchanged, leading to equivalence classes of parameters that yield the same realization. These considerations in turn lead to the notion of identifiability – the ability to recover (the equivalence class of) parameters from the sole knowledge of the realization of the corresponding network. We studied this problem in depth throught the lens of a new embedding of ReLU neural network parameters of any depth. The proposed embedding is invariant to scalings and provides a locally linear parameterization of the realization of the network.Leveraging these two key properties, we derived some conditions under which a deep ReLU network is indeed locally identifiable from the knowledge of the realization on a finite set of samples. We studied the shallow case in more depth, establishing necessary and sufficient conditions for the network to be identifiable from a bounded subset .

An important challenge in deep learning is to promote sparsity during the learning phase using a regularizer. In the classical setting of linear inverse problems, it is well known that the

A particular challenge is to understand to what extent using an

From a more computational perspective, within the framework of the Ph.D. of Pierre Stock

, we proposed last year a technique to drastically compress neural networks using product quantization

, and this year an approach to learn networks that can be more efficiently quantized

. We also started to study efficient optimization algorithms to train quantized networks that leverage multiple quantization levels.

Matrix factorization with sparsity constraints plays an important role in many machine learning and signal processing problems such as dictionary learning, data visualization, dimension reduction.

Last year, from an algorithmic perspective, we analyzed and fixed a weakness of proximal algorithms in sparse matrix factorization. We also described a new tractable proximal operator called Generalized Hungarian Method, associated to so-called

From a theoretical perspective, we considered the hardness and uniqueness properties of sparse matrix factorization. First, even with only two factors and a fixed, known support, we showed that optimizing the coefficients of the sparse factors can be an NP-hard problem. Besides, we studied the landscape of the corresponding optimization problem and exhibited "easy" instances where the problem can be solved to global optimality with an algorithm demonstrated to be orders of magnitude faster than classical gradient based methods . In complement, we investigated the essential uniqueness of sparse matrix factorizations, both with two factors and in a multi-layer setting . We combined these results with a focus on so-called butterfly supports to achieve a multilayer sparse factorization algorithm able to learn fast transforms essentially at the cost of a single matrix-vector multiplication, with exact recovery guarantees . A first version of the corresponding algorithm was incorporated in the FA

The compressive learning framework proposes to deal with the large scale of datasets by compressing them into a single vector of generalized random moments, called a sketch, from which the learning task is then performed. Our papers establishing statistical guarantees on the generalization error of this procedure, first in a general abstract setting illustrated on PCA , then for the specific case of compressive

Theoretical guarantees in compressive learning fundamentally rely on comparing certain metrics between probability distributions. This year we established some conditions under which the Wasserstein distance can be controlled by Maximum Mean Discrepancy (MMD) norms, which are defined using reproducing kernel Hilbert spaces. Based on the relations between the MMD and the Wasserstein distance, we provide new guarantees for compressive statistical learning by introducing and studying the concept of Wasserstein learnability of the learning task .

Dimension reduction in compressive learning exploits the ability to approximate certain kernels by finite dimensional quadratures. We studied a quadrature, proposed by Ermakov and Zolotukhin in the sixties, through the lens of kernel methods. The nodes of this quadrature rule follow the distribution of a determinantal point process, while the weights are defined through a linear system, similarly to the optimal kernel quadrature. We showed how these two classes of quadrature are related, and we proved a tractable formula of the expected value of the squared worst-case integration error on the unit ball of an RKHS of the former quadrature. In particular, this formula involves the eigenvalues of the corresponding kernel and leads to improving on the existing theoretical guarantees of the optimal kernel quadrature with determinantal point processes .

From a more empirical perspective, we pursued our efforts to make sketching for compressive learning more versatile and efficient. This notably involved exploring how to adapt the sketching pipeline to exploit optical processing units (OPUs) for energy-efficient fast random projection, and investigating the ability to exploit sketching in large-scale deep self-supervised learning scenarios.

Finally, making the connection between graph learning and sketching methods, we have recently started to study the practical possibility and theoretical limitations of using a sketching technique to estimate the precision matrix involved in the Graphical Lasso algorithm.

In the context of the Ph.D. thesis of Antoine Chatalic (in the PANAMA team in Rennes, defended last year) we showed that a simple perturbation of the sketching mechanism with additive noise is sufficient to satisfy differential privacy, a well-established formalism for defining and quantifying the privacy of a random mechanism. We combined this with a feature subsampling mechanism, which reduces the computational cost without damaging privacy. The framework was applied to the tasks of Gaussian modeling, k-means clustering and principal component analysis (PCA), for which sharp privacy bounds were derived. Empirically, the quality (for subsequent learning) of the compressed representation produced by this mechanism is strongly related with the induced noise level, for which we gave analytical expressions.

This year we also addressed problem of differentially private estimation of multiple quantiles (MQ) of a dataset, a key building block in modern data analysis. We showed how to implement the non-smoothed Inverse Sensitivity (IS) mechanism for this specific problem and established that the resulting method is closely related to the recent JointExp algorithm, sharing in particular the same computational complexity and a similar efficiency. We also identified pitfalls of the two approaches on certain peaked distributions, and proposed a fix Numerical experiments showed that the empirical efficiency of the resulting algorithms is similar to the non-smoothed methods for non-degenerate datasets, but orders of magnitude better on real datasets with repeated values.

In the context of the Ph.D. work of Guillaume Lauga and the previous internships of Federico Grillini and Giovanni Seraghiti, this year we started to study the combination of proximal methods and multiresolution analysis in large-scale image denoising problems. The use of multiresolution schemes, such as wavelets transforms, is not new in imagining and is widely used to define regularization strategies. We studied the use of such techniques to a wider extent, as a solution to accelerate proximal algorithms usually used for their solution and make them usable for problems of very large dimensions. In the fashion of multilevel gradient methods

, popular techniques in smooth optimization, we designed multilevel versions of proximal algorithms employing wavelet transforms as transfer operators.

In the context of the internship of Hugo Gouttenegre, we also pursued the investigations in 3D MRI super-resolution using nonconvex optimization models. We provided a 3D extension of the Discrete Mumford-Shah, allowing to jointly perform a 3D super-resolution and a segmentation of the high-resolution volume. New phantom acquisitions were conducted, including a high-resolution groundtruth volume, to evaluate the quantitative performances of this approach. A numerical toolbox is under construction.

CIFRE contract with Facebook Artificial Intelligence Research, Paris on Deep neural networks for large scale learning

Partners: Facebook Artificial Intelligence Research, Paris; Inria-Grenoble

Funding: Facebook Artificial Intelligence Research, Paris; ANRT

CIFRE contract with Valeo AI, Paris on Frugal learning with applications to autonomous vehicles

Partners: Valeo AI, Paris; ENS de Lyon

Funding: Valeo AI, Paris; ANRT

Context: Chaire IA AllegroAssai

The overall objective of this thesis is to develop machine learning methods exploiting low-dimensional sketches and sparsity to address perception-based learning tasks in the context of autonomous vehicles.

Funding from Facebook Artificial Intelligence Research, Paris

Partners: Facebook Artificial Intelligence Research, Paris; ENS de Lyon

Funding: Facebook Artificial Intelligence Research, Paris

Context: Chaire IA AllegroAssai

This is supporting the research conducted in the framework of the Chaire IA AllegroAssai.

Duration of the project: 2020 - 2024.

AllegroAssai focuses on the design of machine learning techniques endowed both with statistical guarantees (to ensure their performance, fairness, privacy, etc.) and provable resource-efficiency (e.g. in terms of bytes and flops, which impact energy consumption and hardware costs), robustness in adversarial conditions for secure performance, and ability to leverage domain-specific models and expert knowledge. The vision of AllegroAssai is that the versatile notion of sparsity, together with sketching techniques using random features, are key in harnessing these fundamental tradeoffs. The first pillar of the project is to investigate sparsely connected deep networks, to understand the tradeoffs between the approximation capacity of a network architecture (ResNet, U-net, etc.) and its “trainability” with provably-good algorithms. A major endeavor is to design efficient regularizers promoting sparsely connected networks with provable robustness in adversarial settings. The second pillar revolves around the design and analysis of provably-good end-to-end sketching pipelines for versatile and resource-efficient large-scale learning, with controlled complexity driven by the structure of the data and that of the task rather than the dataset size.

Duration of the project: February 2020 - January 2024.

DataRedux puts forward an innovative framework to reduce networked data complexity while preserving its richness, by working at intermediate scales (“mesoscales”). Our objective is to reach a fundamental breakthrough in the theoretical understanding and representation of rich and complex networked datasets for use in predictive data-driven models. Our main novelty is to define network reduction techniques in relation with the dynamical processes occurring on the networks. To this aim, we will develop methods to go from data to information and knowledge at different scales in a human-accessible way by extracting structures from high-resolution, diverse and heterogeneous data. Our methodology will involve the identification of the most relevant subparts of time-resolved datasets while remapping the remaining parts of the system, the simultaneous structural-temporal representations of time-varying networks, the development of parsimonious data representations extracting meaningful structures at mesoscales (“mesostructures”), and the building of models of interactions that include mesostructures of various types. Our aim is to identify data aggregation methods at intermediate scales and new types of data representations in relation with dynamical processes, that carry the richness of information of the original data, while keeping their most relevant patterns for their manageable integration in data-driven numerical models for decision making and actionable insights.

Duration of the project: February 2020 - January 2024.

This project meets the compelling demand of developing a unified framework for distributed knowledge extraction and learning from graph data streaming using in-network adaptive processing, and adjoining powerful recent mathematical tools to analyze and improve performances. The project draws on three major parallel directions of research: network diffusion, signal processing on graphs, and random matrix theory which DARLING aims at unifying into a holistic dynamic network processing framework. Signal processing on graphs has recently provided a comprehensive set of basic instruments allowing for signal on graph filtering or sampling, but it is limited to static signal models. Network diffusion on the opposite inherently assumes models of time varying graphs and signals, and has pursued the path of proposing and understanding the performance of distributed dynamic inference on graphs. Both areas are however limited by their assuming either deterministic graph or signal models, thereby entailing often inflexible and difficult-to-grasp theoretical results. Random matrix theory for random graph inference has taken a parallel road in explicitly studying the performance, thereby drawing limitations and providing directions of improvement, of graph-based algorithms (e.g., spectral clustering methods). The ambition of DARLING lies in the development of network diffusion-type algorithms anchored in the graph signal processing lore, rather than heuristics, which shall systematically be analyzed and improved through random matrix analysis on elementary graph models. We believe that this original communion of as yet remote areas has the potential to path the pave to the emergence of the critically needed future field of dynamical network signal processing.

Duration of the project: September 2021 - September 2023.

This project focuses on large scale optimization problems in signal processing and imaging. A natural way to tackle them is to exploit their underlying structure, and to represent them at different resolution levels. The use of multiresolution schemes, such as wavelets transforms, is not new in imaging and is widely used to define regularization strategies. However, such techniques could be used to a wider extent, in order to accelerate the optimization algorithms used for their solution and to tackle large datasets. Techniques based on such ideas are usually called multilevel optimization methods and are well-known and widely used in the field of smooth optimization and especially in the solution of partial differential equations. Optimization problems arising in image reconstruction are however usually nonsmooth and thus solved by proximal methods. Such approaches are efficient for small-scale problems but still computationally demanding for problems with very high-dimensional data. The ambition of this project is thus to combine proximal methods and multiresolution analysis not only as a regularization, but as a solution to accelerate proximal algorithms.

Duration of the project: October 2021-December 2024.

Collaboration with Silviu-Ioan Filip and Olivier Sentieys (IRISA, Rennes), Anastasia Volkova (LS2N Nantes)

The LeanAI project aims at developing a comprehensive and flexible framework for mixed-precision optimization. The project is motivated by the increasing demand for intelligent edge devices capable of on-site learning, driven by the recent developments in deep learning. The realization of such systems is a massive challenge due to the limited resources available in an embedded context and the massive training costs for state-of-the-art deep neural networks. In this project we attack these problems at the arithmetic and algorithmic levels by exploring the design of new mixed numerical precision algorithms, energy-efficient and capable of offering increased performance in a resource-restricted environment. The ambition of the project is to develop more flexible and faster techniques than existing reduced-precision gradient algorithms, by determining the best numeric formats to be used in combination with this kind of methods, rules to dynamically adjust the precision and extension of such techniques to second-order and multilevel strategies.

Duration of the project: April 2019-December 2022.

Collaboration with Eric Van Reeth (Creatis, Lyon)

Magnetic Resonance Imaging (MRI) is an extremely important anatomical and functional imaging technique, widely used by physicists to establish medical diagnosis. Acquiring high resolution volumes is desirable in many clinical and pre-clinical applications to accurately adapt the treatment to the measurements, or simply obtain highly resolved images of small anatomical structures. However, directly acquiring high-resolution volumes implies: i) long scanning times, which are often not tolerated by patients and children, and ii) images with low signal-to-noise ratio. Therefore, it is of particular interest to quickly acquire low-resolution volumes, and enhance their resolution as a post-processing step.

This project aims at developing new techniques to build super-resolution images for 3D MRI, that can take into account more physical constraints, such as prior medical knowledge, and to derive efficient machine learning algorithms suited for large scale data, with theoretical guarantees. In particular, we explore specialized piecewise smooth reconstruction variational methods, like the Mumford-Shah (MS) and the Total Variation (TV) variants, and to adapt their fitting terms as well as their optimization algorithms. The main originality of this project is to combine resolution enhancement and segmentation in MRI (usually performed as two distinct post-processing steps), starting from the MS model, a seminal tool originally designed for image denoising and segmentation tasks. This approach will improve the quality of the reconstruction both in terms of sharpness and smoothness, and help the doctors with reaching a diagnosis.

All PhD students of the team are co-supervised by at least one team member. In addition, some team members are involved in the co-supervision of students hosted in other labs.

The following PhDs were defended in DANTE in 2021:

Members of the DANTE team participated to the following juries