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DTSTART;TZID=Europe/Paris:20241203T130000
DTEND;TZID=Europe/Paris:20241203T140000
DTSTAMP:20241115T093532Z
CREATED:20241115T093532Z
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UID:26921-1733230800-1733234400@www.loria.fr
SUMMARY:Department 2 Seminar: Thibault Gauthier.
DESCRIPTION:Le prochain séminaire du D2 aura lieu le 3 décembre à 13h00 en salle A008. \nOrateur : Thibault Gauthier \nTitre : The Automation of Mathematics in Practice \nAbstract:\nThis presentation provides an overview of automated systems actively\nused in proving mathematical theorems\, focusing on the practical\nsuccesses  achieved by SAT solvers\, first-order theorem provers\, and\ninteractive theorem provers.\nAdditionally\, we present our progress towards improving automation in\nareas that are usually considered challenging for proof automation\, such\nas conjecture generation and inductive reasoning. \nToutes les infos se trouvent aussi sur le site du département.
URL:https://www.loria.fr/event/department-2-seminar-thibault-gauthier/
LOCATION:A008
CATEGORIES:Séminaire
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BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20241206T133000
DTEND;TZID=Europe/Paris:20241206T150000
DTSTAMP:20241129T133542Z
CREATED:20241129T133542Z
LAST-MODIFIED:20241129T133542Z
UID:26976-1733491800-1733497200@www.loria.fr
SUMMARY:D5 Seminar: Deep Learning\, Optimal Control\, and Bio-Inspired Control for Dynamic Robots
DESCRIPTION:The next D5 Seminar\, « Deep Learning\, Optimal Control\, and Bio-Inspired Control for Dynamic Robots » will be held by Guillaume Bellegarda (EPFL) on Friday\, December 6 at 1:30 p.m. in room A008. \nAbstract:\nRecent advances in machine learning\, control\, and robotics show promising results towards integrating autonomous systems into society. Legged robots in particular suggest potential for the same dynamic capabilities as humans to adapt to everyday\, and even challenging\, environments. However\, when compared with humans and animals\, state-of-the-art robotic systems do not yet demonstrate the same agility nor intelligence to navigate the real world. While important contributions have been made to approach human levels in specific tasks\, the desired system generalizability to interact with and adapt to new environments remains challenging. Additionally\, for tasks learned with machine learning in which robotic systems do approach or surmount human-level skills\, the underlying neural network function approximation lacks interpretability and performance guarantees. This is true for both Artificial Neural Networks\, as well as their biological counterparts that exist in animals. In this talk\, we present several methods to maximize robotic system performance and explainability by leveraging ideas from machine learning\, model-based control\, and neuroscience. Example applications will be shown for highly dynamic motions on systems such as quadrupeds\, vehicles\, and wheel-legged robots.
URL:https://www.loria.fr/event/d5-seminar-deep-learning-optimal-control-and-bio-inspired-control-for-dynamic-robots/
CATEGORIES:Séminaire
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20241213T133000
DTEND;TZID=Europe/Paris:20241213T150000
DTSTAMP:20241209T151832Z
CREATED:20241209T151832Z
LAST-MODIFIED:20241209T151832Z
UID:26999-1734096600-1734102000@www.loria.fr
SUMMARY:D5 Seminar: Bio-inspired self-supervised learning of visual representations
DESCRIPTION:The next D5 Seminar\, « Bio-inspired self-supervised learning of visual representations » will be held by Arthur Aubret\, on Friday\, December 13 at 1:30 p.m. in room C005. \nAbstract:\nAbstract: Despite recent advances in self-supervised visual machine learning\, humans develop more robust representations with much fewer data. This may be explained by the fundamental differences between the development of their visual systems: while machine learning methods use massive amounts of i.i.d images\, humans actively move and interact with objects over time. In this talk\, I investigate how considering bio-inspired learning mechanisms can impact visual representations learning. I will provide evidence that modelling spatio-temporal regularities in egocentric visual sequences boosts the robustness of vision models. In addition\, I will explain how egocentric actions underpinning visual changes\, like eye saccades or object manipulations\, support object learning. Together\, these findings expose that the spatio-temporal structure and active nature of human visual experience may be key to develop strong semantic visual representations.
URL:https://www.loria.fr/event/d5-seminar-bio-inspired-self-supervised-learning-of-visual-representations/
CATEGORIES:Séminaire
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