Machine Learning Journal Club - University of Turin

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  • MLJC project in Geometric DL accepted at NeurIPS 2022, New Orleans (USA)
    We are delighted to announce that a group from MLJC including Luca Bottero, Francesco Calisto and Valerio Pagliarino will present their work at the world-leading conference NeurIPS 2022 (Neural Information Processing Systems) in the Neur-Reps (https://www.neurreps.org/) (Symmetries and Geometry in Neural Representations) workshop. The conference will take place in New Orleans, Louisiana (USA) from November, 27th to December 3rd. The project is inserted in the landscape of Geometric Deep Learning (GDL), a […]
  • Presentation of MLJC Projects for A.Y. 2022/23 10th November, Physics Dept. of the University of Turin
    The Machine Learning Journal Club is happy to invite you to the event “MLJC – Presentation of projects and activities for the Academic Year 2022-23“. MLJC members will present the key projects and activites currently under development and they will interact with students, researchers and AI enthusiasts for extending our network and opportunities to everyone that is interested in collaborating with us. A large number of highly interactive short talks will allow […]
  • Activity Report 2021 published
    The Machine Learning Journal Club is pleased to publish the Annual Activity Report 2021 where the most relevant scientific and outreach activity of the past year is presented. This report is a great occasion to thank the several people and institutions that contributed to our activities with their expertise, resources and support.
  • MLJC meets the Minister of the State of North Rhine-Westphalia, dr Stephan Holthoff-Pförtner
    Questa mattina, 15 febbraio 2022, il Machine Learning Journal Club ha incontrato a Torino il ministro per gli Affari Federali, Europei e Internazionali della Renania settentrionale – Vestfalia Stephan dr Holthoff-Pförtner. All’incontro era presente anche il console generale a Milano dr Ingrid Jung. Il Ministro era a Torino per la firma di una Dichiarazione di Intenti per la collaborazione e lo sviluppo di relazioni amichevoli tra la Regione Piemonte e il Land […]
  • Merry Christmas and Happy New Year from the MLJC
  • Presentation at Toolbox Coworking
    Siamo lieti di comunicare che Giovedì 11 Novembre alle ore 18:00 presenteremo la nostra associazione ed i progetti di ricerca attivi in un evento che si terrà presso il Toolbox Coworking (Via Agostino da Montefeltro 2, Torino).Toolbox è uno spazio ibrido di coworking che favorisce la collaborazione tra associazioni, startup ed imprese: è quindi per noi una grande opportunità poterci esprimere in questo contesto.
  • BCI keynote at Intesa Sanpaolo Innovation Center
    La scorsa settimana siamo stati ospiti di Intesa Sanpaolo Innovation Center per parlare di #BCI (Brain Computer Interface).Se vi siete persi l’appuntamento potete rimediare ascoltando il nostro #podcast!“Brain Computer Interface: un nuovo futuro per le disabilità”, a cura di Gabriele Penna, Pio Raffaele Fina, Pietro Sillano, Machine Learning Journal Club – Spotify: https://lnkd.in/dGnqa5bK– Google Podcast: https://lnkd.in/dFSSawuv-Apple Podcast: https://lnkd.in/dvphwTpb– Sito di Intesa Sanpaolo: https://lnkd.in/dyBXpmT2Ancora grazie a Sonia D’Arcangelo e Marzio Alessi per averci invitati!

SHARPER – European Researchers’ Night 2021 – Turin

The Machine Learning Journal Club participated in the SHARPER – European Researchers’ Night in Turin on September 24th 2021, organized by the University of Turin and Politecnico of Turin, in the beautiful frame of the Castello del Valentino. The title of the stand was “MACHINE LEARNING E L’INTELLIGENZA ARTIFICIALE DEL FUTURO”. At our stand we presented projects spacing from Brain Computer Interfaces to Scientific Machine Learning.

Brain computer Interfaces

The MedicAI team showcased the new BCI hardware from OpenBCI that will allow the team to run custom experiments in order to acquire data for training, validation and testing of new advanced algorithms for EEG signal classification. These last are fundamental for critical applications, for example in allowing communication or the use of computers for people that cannot use their hands because of diseases. [Open the page]

Scientific Machine Learning

We presented an overview on our past Scientific Machine Learning projects that have the goal to create accurate and efficient models of natural phenomena by merging the knowledge coming from the physics-based approaches using differential equations and the data-driven approaches. In particular we showcased the work “Physics-Informed Machine Learning Simulator for Wildfire Propagation” and our contribution to “NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations” [Open the page]

Real Time Scientific Machine Learning

We presented an electronics based on a SoC with GPU acceleration , an FPGA and a large amount of inputs and outputs that allows to run machine learning algorithsm not only “offline” on dataset already saved on the hard drive, but also in real time on data coming from sensors. In this way it is possible to deploy machine learning applications where it is required by the model to trigger decisions with a very low latency (thanks to the FPGA-based neural network inference). In this project the final goal is the realization of adaptive control for dynamical systems that can merge knowledge coming from physics priors and sensor fusion. This project is still “work in progress”! [Open the page]

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