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Artificial Intelligence for All – HUMANIA
With the current rapid growth of AI research and applications, there are both unprecedented opportunities and legitimate worries about its potential misuses. In this context, we are committed to help making AI easier to access and use by a large population segment. Making AI more accessible to all s
DEEP-VISION – DEEP-VISION
The project is led by Frederic Jurie. Frederic Jurie has been full Professor (PRCE French grade) at the University of Caen Normandy, having been a researcher at CNRS from 1994 to 2004 at UMR LASMEA in Clermont-Ferrand, then from 2004 to 2007 at the INRIA Rhône-Alpes center affiliated with Cordelia S
Physics-Informed AI for Observation-driven Ocean AnalytiX – OceaniX
Covering more than 70% of earth’s surface, the oceans, especially the upper oceans (e.g., the first few hundred meters below the oceans’ surface), play key roles for the regulation of the earth climate (e.g., climate change) as well as for human societies (e.g., marine resources and maritime activit
AI for paedriatric neurorehabilitation – AI-4-CHILD
Artificial intelligence (AI), in its modern form, is profoundly changing many areas, including health. The development of AI for health opens up very promising prospects for improving the quality of care, reducing costs through more personalized care, but also better traceability and improved medica
Intelligent handling of imperfect data – INTENDED
The huge wealth of data available nowadays holds tremendous potential to improve our lives, whether it be by advancing scientific knowledge, improving patient care, or supporting more informed policymaking. However, obtaining relevant and reliable information from real-world data is difficult due bo
Propositional Reasoning for Large-Scale Optimization. Application to Clean Energy Mobility Issues – Massal'IA
The propositional satisfiability problem (SAT) is fundamental in many fields of Computer Science and particularly in Artificial Intelligence. Thanks to its high reasoning capabilities, SAT is a very competitive generic problem-solving approach that is often applied to solve decision problems in vari
Transfer Learning from Big data to Small Data: Leveraging Psychiatric Neuroimaging Biomarkers Discovery – Big2small
Unlike many other medical specialties, psychiatry lacks objective quantitative measures (such as blood dosage) to guide clinicians in choosing a therapeutic strategy. Brain anatomy is an imprint of the individual's genetic and environmental background. The identification of prognostic brain signatur
Algorithms, Approximations, Sparsity and Sketching for AI – AllegroAssai
Solid algorithmic and mathematical foundations are essential to endow AI systems with guaranteed utility, resource-efficiency and trustworthiness. Exploiting massive data streams requires controlling the tradeoffs between performance and computational footprint. For example, sensors for autonomous
Responsible AI – IA Responsable
The chair project will be incorporated into the framework of the European Union's policy on the ethics of artificial intelligence. The European Commission believes that the challenges for Europe in the field of AI are above all ethical and that guidelines should be set to gain the trust of citizens.
Deep learning for computational imaging with emerging image modalities – DeepCIM
Digital and computational imaging is a key technology to help understanding the world around us. This field is likely to know disruptive changes in the coming years due to the emergence of novel imaging modalities, e.g., omni-directional videos, light fields, and impressive advances in machine learn
EXPlainable artificial intelligence: a KnowlEdge CompilaTion FoundATION – EXPEKCTATION
The EXPEKCTATION project is about explainable and robust AI. It aims to devise global model-agnostic approaches for interpretable and robust machine learning using knowledge compilation: we seek for generic pre-processing techniques capable of extracting from any black-box predictor a corresponding
DeepCuriosity: Curiosity-driven exploration and curriculum learning in AI with applications to autonomous agents, automated discovery and educational technologies. – DeepCuriosity
The research vision and program of the DeepCuriosity project aim at developing the foundations of a new scientific approach to autonomous artificial intelligence and lifelong machine learning. While deep reinforcement learning has achieved impressive results recently (e.g. in complex board or video
Green Artificial Intelligence – GrAI
In recent years, artificial intelligence (AI) has become increasingly intertwined with our daily lives. However, AI such as that currently supported by most major players in the industry like GAFAM, is decentralized to servers. Since the electricity consumption of Internet infrastructures represents
Stattistics, computation and Artificial Intelligence – SCAI
The key factor for the recent boom in AI is the emergence of deep learning (DL). The successes of these methods - in particular, for supervised learning- are amazing. But the limits and some of the downsides of DL have been identified. It is well acknowledged that the current DL algorithms are “dat
TopAI: Topological Data Analysis for Machine Learning and AI – TopAI
TopAI is a project that aims at developing a world-leading research activity on topological and geometric approaches in Machine Learning (ML) and Artificial Intelligence (AI) going from mathematical foundations to industrial applications with high societal and economic impact in personalized medicin
BRIDinG thE gAp Between iterative proximaL methods and nEural networks – BRIDGEABLE
Proximal methods have enabled significant advances in large scale optimization in the last decade. At the same time, deep neural networks (NNs) have led to outstanding achievements in many application domains related to data science. However, the fundamental reasons for their excellent performance a
Bridging Statistical and Computational efficiency in Artificial Intelligence – BISCOTTE
With the enormous amounts of data involved in training a machine learning system, as well as the trend to push artificial intelligence to mobile devices and embedded systems, taking into account computation and memory constraints is of primary interest from the get go when designing machine learning
Advanced Machine/Deep learning for Heterogeneous Large scale data – AML-HELAS
Professor M. Vazirgiannis is involved in data science research since the 1990’s, worked in different research areas of this domain maintaining significant scientific impact. He established and leads the Data Science and Mining group at LIX/École Polytechnique (ÉP), being active in attracting signifi
HUman-MAchine Affective INteraction & Ethics – HUMAAINE
Abstract: The new uses of affective social robots, conversational agents, and the so-called “intelligent” systems, in fields as diverse as health, education or transport reflect a phase of significant change in human-machine relations which should receive great attention. How will Human co-learn, co
Deep Learning for Physical Processes with applications to Earth System Science – DL4CLIM
Motivations and Scientific Program: the project targets the development of Deep Learning (DL) methods for the modeling of physical processes. The application domain is environment and climate. It builds on the complementarity of two major scientific paradigms, Physics and Machine Learning (ML). The