|1. Amr Gomaa (DFKI)
Personalized multimodal fusion approach for referencing objects from moving vehicles
With the recent exponentially increasing capabilities of modern vehicles, novel approaches for interaction emerged the goes beyond traditional touch-based and voice commands approaches. In this work, we investigate a novel pointing and gaze multimodal fusion personalized approach for referencing surrounding objects while maintaining a long driving route in a simulated environment.
|2. Oliver Mey (Fraunhofer IIS)
Self-masking adversarial networks
Self-masking adversarial networks are a neural network architecture and technique for local interpretability that can be used to extract the classification-relevant parts of an input dataset. The working principle of the method is explained and results of the method are demonstrated on different sample datasets.
|3. Paweł Guzewicz (Inria & Ecole Polytechnique)
Expressive and efficient analytics for RDF graphs
Nowadays, public open data become steadily more available, frequently embracing more heterogeneous data formats, such as RDF, with numerous RDF graph datasets. Such data often contain interesting insights in the form of RDF graph aggregates that journalists can use as newspaper article ideas or leads in the process called Computational Lead Finding. In this work, we show how to efficiently and automatically find the top-k most interesting aggregates in an RDF graph.
|4. Marine Collery (Inria & IBM France)
Learning globally understandable models with rules
Global understanding of ML decision models is crucial in many environment. Rule systems are fully understandable, executable, transparent, and business user friendly. However, today’s rule learning algorithms produce rules with low expressivity…
|5. Isabel Rio-Torto de Oliveira (INESC TEC)
Explainable classification through natural language
Being able to justify the decisions of deep learning models is rapidly becoming a mandatory requirement for deploying these systems in the real-world, especially in medical and other high-stakes decision areas. Furthermore, the state-of-the-art focuses mainly on post-hoc visual interpretability. However, we argue that is it advantageous to leverage the complementarity of different explanation modalities, by exploring the generation of natural language explanations in an in-model fashion, integrating the generated text directly into the classification path.
|6. Claudio Lazo (TNO)
VISION: Working towards a European roadmap for AI excellence & trust
To make Europe the AI powerhouse as desired, we first need synergy and cooperation between the EU’s rich landscape of AI research & innovation communities. A great roadmap and common vision can make a huge difference, and VISION is the H2020-funded project that will achieve this in the following years. Learn about our approach, find out who are involved, and contribute to the European AI strategy!
|7. Rui Zhao (University of Edinburgh)
Dr.Aid: a formal framework to support data-use policy compliance for decentralized collaboration
Data sharing and data processing are a common practice across various domains, but the handling of data governance rules (aka. data-use policies) remains manual. Existing research on related topics fall short in supporting general obligations and/or multi-institutional multi-input-multi-output (MIMO) data processing graphs. We present a formal language and constructed the Dr.Aid framework addressing these issues.
|8. Pierre-Yves Lagrave (Thales Research and Technology)
Trusted AI with Lie-group based equivariant neural networks
Neural Networks are generically sensitive to geometrical transforms of their inputs, hence motivating the need for increasing their robustness with respect to the action of corresponding Lie groups. Building on the success of CNN, Group-Convolutional Neural Networks (G-CNN) have been introduced as an alternative to the data augmentation technique by leveraging on group-based equivariant convolution operators and are achieving state-of-the-art accuracies for a wide range of applications. G-CNN relying on compact groups are well covered by the literature and we will focus here on one challenging non-compact case by building SU(1,1) equivariant neural networks operating on the hyperbolic space, with an application to robust radar-Doppler signal classification.
|9. Murali Manohar Kondragunta (Gramener)
Controlling hate speech by tweaking neural networks
Identifying and suppressing different subnetworks that activate hate speech has many use-cases. Our work deals with probing different pre-trained models for such subnetworks. We also check if deactivating such subnetworks lead to any performance trade-offs.
|10. Frederic Jonske (IKIM)
Automated classification of external DICOM studies
External patients’ imaging studies often adhere to different naming and structural standards than the local one, making automated filing into the local database an error-prone process. The MOMO algorithm attempts to alleviate such difficulties using a prediction algorithm based on metadata and a Convolutional Neural Network.
|11. Mathieu de Langlard (Inria)
Characterization of the 3D human liver micro-architecture using image analysis
The human liver is divided into functional unit called lobules which are mainly composed of cells, blood and bile vessels networks. The aim of this presentation is to provide a complete image acquisition and analysis methodology to reconstruct a 3D human lobule micro-architecture and estimate its 3D morphological properties.
|12. Noémie Moreau (Université de Nantes & Keosys)
Comparison between threshold-based and deep-learning-based bone segmentation on whole body CT images
Bone segmentation can be used to evaluate metastatic tumor burden in breast cancer. This work compares the results of three bone segmentation methods : one threshold based and two deep learning based with a Cross Entropy/Dice loss and Hausdorff Distance/Dice loss. A dice score of 0.96, 0.99, 0.98 respectively for each method but with a better visual results for the Hausdorff/Dice method.
|13. Hassan Saber (Inria)
Routine bandits: Minimizing regret on recurring problems
We study a variant of the multi-armed bandit problem in which a learner faces every day one of many bandit instances, and call it a routine bandit. More specifically, at each period h in [1,H], the same bandit is considered during T > 1 consecutive time steps, but its identity is unknown to the learner.
|14. Tommaso Di Noto (University of Lausanne)
Anatomically-informed detection of cerebral aneurysms in TOF-MRA
The task of aneurysm detection is spatially constrained by the vascular anatomy of the brain. We leverage this information to build an anatomically-informed deep learning network. Specifically, we focus the attention of the model on the areas where aneurysm occurrence is most frequent.
|15. Asma Bensalah (Universitat Autònoma de Barcelona)
Towards stroke patients’ upper-limb automatic motor assessment using smartwatches
The rehabilitation stage is crucial for post stroke motor disabilities recovery. Hence, there is a need for continuous monitoring via non-invasive technology. In this work, smartwatches are used to provide patients’ monitoring, a shallow deep learning model and SVMs are used as a classification baseline.
|16. Matthis Maillard (Télécom Paris – Institut Polytechnique de Paris)
Knowledge distillation from multi-modal to mono-modal segmentation networks
The fusion of information from different modalities has demonstrated to improve the segmentation accuracy, with respect to mono-modal segmentations, in several applications. However, acquiring multiple modalities is usually not possible in a clinical setting due to a limited number of physicians and scanners, and to limit costs and scan time. Most of the time, only one modality is acquired. In this poster, we present a framework to transfer knowledge from a trained multi-modal network (teacher) to a mono-modal one (student).
|17. Xenia Klinge (DFKI)
Interactive Machine Learning pipeline powered by streamlit/Python (demo)
Getting started with Machine Learning today is easier than ever, thanks to an increasing amount of accessible tools. However, for non-experts as you would often meet in projects related to health and medicine, it remains a challenge to get to know the concepts, let alone run their own experiments. Our ML pipeline served in a browser through the Python libarry streamlit tries to make data exploration and prediction both manageable and understandable to our medical partners.