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SUMMARY:Training neural models using logic: results\, challenges\, and applications
DESCRIPTION:DATA&AI Team Seminar (Online) \nEfi Tsamoura\, Senior Researcher at Samsung AI \nTitle: Training neural models using logic: results\, challenges\, and applications\nAbstract: Neurosymbolic learning (NSL) vows to transform AI by combining the strong induction capabilities of neural models with the strong deduction capabilities of symbolic knowledge representation and reasoning techniques. This talk centers around an NSL problem that has received significant attention lately: training neural classifiers using supervision produced by logical theories. Empirical research has shown the advantages of this learning setting over end-to-end deep neural architectures in multiple aspects\, including accuracy and model complexity. Despite the extensive empirical research\, limited theoretical analysis has been dedicated to understanding if and under which conditions we can learn the underlying neural models. \nThis talk covers this gap by proposing necessary and sufficient conditions\, which ensure that we can learn the underlying models under rigorous guarantees. I will also discuss the relationship between this problem and other known problems in the machine learning literature. Furthermore\, I will present new challenges inherent to this NSL setting and propose solutions to overcome those challenges\, leading to models with substantially higher accuracy. I will conclude this talk with recent applied results and open challenges. \n  \nBio: Efi Tsamoura is a Senior Researcher at Samsung AI\, Cambridge\, UK. In 2016\, she was awarded a prestigious early career fellowship from the Alan Turing Institute\, UK\, for her work on logic and databases\, and before that\, she was a Postdoctoral Researcher in the Department of Computer Science of the University of Oxford. Her main research interests lie in the areas of logic\, knowledge representation and reasoning\, and neurosymbolic learning\, while her recent outcomes involve scaling symbolic reasoning to billions of triples\, as well as addressing open problems in neuro-symbolic learning. Her research has been published in top-tier machine learning\, AI\, and database venues (NeurIPS\, ICML\, SIGMOD\, VLDB\, PODS\, AAAI\, IJCAI\, etc.).
URL:https://www.etis-lab.fr/event/training-neural-models-using-logic-results-challenges-and-applications/
LOCATION:Zoom
CATEGORIES:Seminar
ORGANIZER;CN="Vassiis Christophides":MAILTO:vassilis.christophides@ensea.fr
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DTSTART;TZID=Europe/Paris:20250220T144500
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SUMMARY:ETIS Tandem Talk: Paul Gay & Guillaume Renton
DESCRIPTION:Tandem Talk by Paul Gay (UPPA) and Guillaume Renton (ETIS) on Sustainability and Machine Learning. \nPaul Gay | Interactions between sustainibility and machine learning research\nAI is a controversial topic and different visions of sustainibility co-exist in the IT communities. In this talk\, I will describe the state of the art from the GreenIT community to assess environmental impact of IT projects. Although the main ideas\, such as life cycle analysis\, description of embodied impacts and indirect effects have been explored for 10-20 years\, we are only beginning to see them applied to AI systems and projects. \nIn a second part\, I will present two of my current machine learning applications to sustainibility topics. The first one is the use of active learning to detect and classify controversial topic on renewable energies in social networks. The second one is to exploit the technique of early exit\, an interesting tool where the amount of compute depends on the data\, and which find applications in edge-cloud settings. \nGuillaume Renton | Reducing computation costs without jeopardizing precision of Entity Alignment tasks\nIn recent work\, we took interest in the computational cost of one of our entity alignment model\, HybEA. The idea was to provide an efficiency analysis on top of a performance analysis\, which is the main source of comparison between AI models. The efficiency analysis was conducted by using fvcore in order to compute the number of GFLOPS required to train the model. This has led to surprising results\, showing that the initial embedding sizes of the models were oversized. This allowed us to greatly reduce the computational cost of our model with a very small loss of accuracy. \n  \nThe talks will take place in the Curium at ENSEA as well as online:\nhttps://cnrs.zoom.us/j/92031778182?pwd=BcsbEGTQf2JwG8jIUkpaK35fZ1WXUK.1
URL:https://www.etis-lab.fr/event/etis-tandem-talk-paul-gay-guillaume-renton/
LOCATION:ENSEA\, Curium\, avenue du Ponceau\, Cergy\, 95014\, France
CATEGORIES:ETIS,Seminar
ORGANIZER;CN="Camille Simon Chane":MAILTO:camille.simon-chane@ensea.fr
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