Meta-learning aims to leverage the experience from previous tasks to solve new tasks using only little training data, train faster and/or get better performance. The proposed challenge focuses on “cross-domain meta-learning” for few-shot image classification using a novel “any-way” and “any-shot” setting. This challenge is part of TAILOR WP2 (see more information here). Goal The […]
A lot of TAILOR scientists submitted their paperworks to the 31st International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2022). The conference will be in Vienna, from 23rd to 29th of July, 2022. Here below the (not full) list of accepted papers presented by people involved in TAILOR: AISafety
Accepted papers by TAILOR scientists to IJCAI-ECAI 2022 Read More »
IJCAI-ECAI22 is coming up and TAILOR will be there! The conference will be in Vienna, from 23rd to 29th of July, 2022. Two keynotes will be given by TAILOR scientists: Gerhard Widmer (JKU Linz) will talk about “AI & Music: On the Role of AI in Studying a Human Art Form” on Monday 23rd, at 18.00
Keynotes from TAILOR scientists at IJCAI-ECAI 2022 in Vienna Read More »
The “Learning to run a power network challenge 2022” is concerned with AI for smart grids, and it has been built by RTE, the French Power Grid operator, and the TAU team, in collaboration with EPRI, CHA Learn, Google research, UCL and IQT labs.
Learning to Run a Power Network Challenge (L2RPN) Read More »
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
A TAILOR paper selected for oral presentation at CVPR 2022 Read More »
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
New projects funded by TAILOR Connectivity Fund Read More »
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
Modeling others for cooperation under imperfect information Read More »
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
You Only Write Thrice – a novel AI tool for scientific writing and publishing Read More »
Brian Logan Associate Professor at Utrecht University Many activities that are easy for humans, such as walking together with other humans, are hard to program as a set of rules for Artificial Intelligence (AI) robots. This is why machine learning is so popular in AI: correct behaviour can be learned by trial and error. However,
Logic-based multi-agent reinforcement learning Read More »
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
Conformal Inference for multivariate, complex, and heterogeneous data Read More »
