An open & responsible AI, with Mehdi Ounissi
The series "2 minutes of AI" is back! In this video, Mehdi Ounissi, a PhD student at Institut du cerveau et de la moell […]
PhD position available: Deep learning for modeling physical dynamics
Call for a fully funded PhD position in Artificial Intelligence - SCAI, PSUAD, TotalEnergies Topic: Deep learning for m […]
Daniel Brooks laureate of the THALES PhD Award 2021
Daniel Brooks is a PhD student at LIP6 (MLIA team) and has just defended his thesis on July 3rd. His work focuses on the […]
Patent: Detecting the risk of Torsade De Pointes using Deep Learning
A recent patent whose main authors are Edi Prifti (UMMISCO) and Prof. Joe-Elie Salem (SU/APHP) and describing a new meth […]
The CD - Book Artisticiel : Cyber Improvisations is released on the PhonoFaune label by Cristal Records.
Using the quintessence of the co-creativity and AI tools invented in the Ircam's Musical Representations team associated […]
Speed dating - 1st edition - Companies & Sorbonne University students
22 Oct 2021
Scai (Esclangon building)
Artificial intelligence needs the prefrontal cortex, Xiao-Jing Wang
25 Oct 2021
Amphi 25, Campus Pierre et Marie Curie
Do algorithms make the law? Aurélie Jean, Agence In Silico Veritas
28 Oct 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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