A poster session will take place on the first day of the workshop, which gives you the opportunity to present your current research.
– 500€ for the 1st prize,
– 300€ for the 2nd prize,
– 200€ for the 3rd prize.
Please note that you do not need to have finalized results for this poster. You can also present the scientific question which you tackle, the approach you are using, intermediate results, and difficulties you might encounter. The prices will take into account the ability to communicate to a broad scientific audience, who does not necessarily know the details of your field.
Lucrezia Carboni – University of Grenoble
Title: Human Brain Functional Network Characterization
Functional brain connectivity networks are challenging data to be properly analyzed and characterized. While networks are a good model to represent the set of connections between brain regions, distinguishing pathological versus healthy states relying on the graph structure can be arduous. Indeed, even if many network descriptors and graph comparison distances exist, there is no clear evidence of the best metrics to be used in the discrimination of different brain states. Such metrics appear to be dataset dependent and to be used separately. Moreover, while many different approaches have been proposed with good accuracy results, their interpretation, a key point in the Neurosciences domain, remains difficult. For these reasons, we propose a way to combine different nodal statistics, i.e. any possible functions of the adjacency matrix defined on the nodes set of a graph. This allows characterizing graphs at both global and nodal levels. First, we define an equivalence relation on the set of nodes of a graph associated with a single nodal statistics. Next, we extend the definition to any collection of nodal statistics and define a measure of orthogonality among nodal statistics. We show our proposal’s usefulness both for determining which nodal statistics are less redundant depending on the underlying structure of the graph and which structural properties are the predominant ones in the graph. Finally, we propose a way to interpret brain regions’ connectivity at the nodal level. We apply our method to functional connectivity networks constructed with different brain atlases and different databases concerning different pathology. We show promising results that enlighten differences at the nodal level related to pathological states.
Huiyu Li – INRIA
Shakeel Ahmad Sheikh – Université de Lorraine, CNRS, INRIA, LORIA
Yingyu Yang – INRIA
Mulin Yu – INRIA
Othmane Marfoq – INRIA
Marina Costantini – EURECOM
Decentralized optimization algorithms allow multiple nodes in a network to collaboratively train a machine learning model using the data of all nodes, but keeping the data private. To achieve this, nodes communicate with their neighbors to exchange optimization values (parameters, gradients) instead of the data itself. By interleaving communication steps with computation steps, all nodes can converge to the optimal solution that a centralized algorithm would find if the data of all nodes was gathered at a single location.
In particular, gossip algorithms allow nodes to wake up at any time and contact one single neighbor to complete an iteration together. These algorithms have the attractive property of not needing a synchronization enforcer, and thus, they offer remarkable time and communication savings. Furthermore, they provide an extra degree of freedom to speed up convergence: the choice of the neighbor to contact when a node goes active.
Gossip algorithms were first proposed in the context of decentralized averaging, where all nodes in the network have a single scalar value and the task is to find the average of all the values in the network. In this setting, and when the neighbor choice is randomized, it is well-known how to choose the neighbor contacting probabilities to maximize convergence speed. However, in the context of decentralized optimization this choice is less clear, and recent work has reported that the probabilities that are optimal for decentralized averaging are not optimal anymore for some decentralized optimization settings.
In this poster we will explain the key differences between the tasks of averaging and optimization in the decentralized setting and will give insights on how to design fast algorithms for the latter. We will make special emphasis on two complementary and perhaps competing forces that drive the convergence speed: the network structure (graph-theoretic point of view), and the optimization landscape (mathematical optimization point of view).
Ziming Liu – INRIA
Recently, more and more healthcare and medical service robots are applied, such as hospital service, patient healthcare, and delivery. Visual localization is an important part of the perception module of autonomous robots. Recent advances in deep learning approaches have given rise to hybrid visual localization approaches that combine both deep networks and traditional pose estimation methods. One limitation of deep learning approaches is the availability of ground truth data needed to train the neural networks. For example, it is extremely difficult, if not impossible, to obtain a ground truth dense depth map of the environment to be used for stereo visual localization. Even if unsupervised training of networks has been investigated, supervised training remains more reliable and robust. In this paper, we propose a new hybrid dense stereo visual localization approach in which a dense depth map is obtained with a network that is supervised using ground truth poses that can be more easily obtained than ground truth depths maps. The depth map obtained from the neural network is used to warp the current image into the reference frame and the optimal pose is obtained by minimizing a cost function that encodes the similarity between the warped image and the reference image. The experimental results show that the proposed approach, not only improves state-of-the-art depth maps estimation networks on some of the standard benchmark datasets, but also outperforms the state-of-the-art visual localization methods.
Bernard Tamba Sandouno – INRIA
Evangelos-Marios Nikolados – University of Edinburgh
Yu Wang – LPMT
Marie Guyomard – I3S, CNRS
Alexandre Bonlarron – INRIA, UCA
Angelo Saadeh – Telecom Paris
Growing confidentiality concerns make it harder for organisations – like hospitals, banks and governmental institutions – and for departments within an institution to collaborate in order to federately train machine learning models on combined datasets.
We describe a solution for two to train a logistic regression on a vertically split dataset such that the privacy of the data used to train the models is protected not only from members of both the collaborating organisations but also from third party users of the models.
In other words, the data will be protected during the training process and after publishing the models’ parameters. Secure multi-party computation (MPC) and epsilon-differential privacy (DP) devise solutions to address the issues of protection from collaborating parties and from users of the models separately.
Can these be combined to form a unified solution? We propose, present, and evaluate a two-party epsilon-differentially private and fully secure logistic regression on a vertically partitioned dataset where the players need to jointly train a model.