A TAILOR paper selected for oral presentation at CVPR 2022

A paper on learning from a limited data for human body/pose estimation from TAILOR researcher Hossein Rahmani, Lancaster University, has been accepted in the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR 2022) for oral presentation (acceptance rate is ~4%). This work has been done in collaboration with researchers from Singapore, US and […]

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New projects funded by TAILOR Connectivity Fund

On 15th of April, TAILOR Connectivity Fund has provided funds for new projects coming from PhD students and researchers from all over Europe. Check the list of all the projects funded by the Connectivity Fund by following this link https://tailor-network.eu/connectivity-fund/funded-projects/. The next deadline for applying to the Connectivity Fund and submitting your project is on

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Modeling others for cooperation under imperfect information

Nieves Montes PhD Student at Artificial Intelligence Research Institute (IIIA-CSIC) This research visit will focus on models for empathetic software agents. This means embedding autonomous agents with the ability to model their peers and understand the reasons behind their behaviour. This work is to enhance the performace of agents in cooperative tasks, where they need

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You Only Write Thrice – a novel AI tool for scientific writing and publishing

TAILOR researchers Kacper Sokol and Peter Flach have developed a workflow to automatically generate interactive documents, slides and computational notebooks from a collection of markdown source files. The framework offers a novel paradigm in authoring and publishing of scientific outputs. In academia it is quite common to produce multiple variants of the same content published

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Conformal Inference for multivariate, complex, and heterogeneous data

Marcos Matabuena University of Santiago de Compostela In this project, in collaboration with Gábor Lugosi (UPF), we propose new uncertainty quantification methods based on the design of new Conformal Inference strategies for complex data that arise in modern personalized medicine applications. The new uncertainty methods can examine the reliability and safety of results obtained with

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Private Continual Learning from a Stream of Pretrained Models

Antonio Carta Post-doc at Pisa University Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt and generalize continually, in an efficient, effective and scalable way appears to be fundamental for a more sustainable development of Artificial Intelligent systems. However, access

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Matheuristic Techniques for Timetabling Problems

Roberto Maria Rosati PhD Student in Information Engineering at University of Udine Recently, matheuristics have emerged as a promising research branch in combinatorial optimization. Thanks to this collaboration supported by TAILOR connectivity fund, we will design and apply novel matheuristic techniques to a variety of timetabling problems that are under investigation at University of Udine.

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