Full Professor position in Artificial intelligence and Data Science for Biology
A full professor position is open at the Laboratory for Computational and Quantitative Biology (LCQB), Institut de Biolo […]
What is an "algorithmic bias"? with Thibault Grison
The series "2 minutes of AI" is back! In this video Thibault Grison, a PhD student at GRIPIC & CERES, raises the is […]
Why do we need Human-Artificial Intelligence Interaction as a research domain? with Oleksandra Vereschak
The series "2 minutes of AI" is back! In this video Oleksandra Vereschak, a PhD student at ISIR, approaches artificial […]
A successful speed dating
Last Friday, October 22, SCAI and datacraft organized a meeting between Sorbonne University Master 2 students looking fo […]
How can a robot learn to catch an object? with Johann Huber
The series "2 minutes of AI" is back! In this video, Johann Huber a PhD student at ISIR, explains the learning mechanis […]
jaii2021: Interactive access to information (AFIA-THL / ARIA / GDR TAL)
9 Dec 2021
Amphi 25 - Pierre et Marie Curie campus
The "autonomous vehicle": ethical issues
14 Dec 2021
Campus Pierre et Marie Curie, Centre de conférences Tower 44, 1st floor, Room 106
How AI can support the fight against cancer? Chloé Audigier, Siemens Healthineers
15 Dec 2021
Nested Sampling for Nuclear Quantum Effects
Although much heavier than electrons, light nuclei, mainly hydrogen, exhibit Nuclear Quantum Effects (NQE), such as tunnelling and zero-point energy, that can have a large impact on the structure and the dynamics of materials.
Explainable artificial intelligence/deep learning. Instantiation to human organoids
Interpretability in Artificial Intelligence (AI) tackles with what can be considered today as the Achilles heel of modern AI, with a particular modern flavor concerning the Deep Learning (DL): the lack of readability, traceability, explainability.
Explainable Sparse Models: a Marriage between Machine Learning and Decision Theory
The aim of this thesis is to propose new approaches based on non-additive integrals to construct explainable sparse models.
Learning to grasp
Learning to grasp is one of the most significant open problems in robotics, requiring complex interaction with previously unseen objects.
Memorization in Deep Learning
Deep Neural Networks obtain outstanding performances on many benchmarks, yet the key ingredient of their success remains unknown.
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