Zobrazeno 1 - 10
of 7 052
pro vyhledávání: '"Fidel, P."'
We introduce a novel strategy for multi-robot sorting of waste objects using Reinforcement Learning. Our focus lies on finding optimal picking strategies that facilitate an effective coordination of a multi-robot system, subject to maximizing the was
Externí odkaz:
http://arxiv.org/abs/2409.13511
The Scholarly Hybrid Question Answering over Linked Data (QALD) Challenge at International Semantic Web Conference (ISWC) 2024 focuses on Question Answering (QA) over diverse scholarly sources: DBLP, SemOpenAlex, and Wikipedia-based texts. This paper
Externí odkaz:
http://arxiv.org/abs/2409.18969
This work addresses stochastic optimal control problems where the unknown state evolves in continuous time while partial, noisy, and possibly controllable measurements are only available in discrete time. We develop a framework for controlling such s
Externí odkaz:
http://arxiv.org/abs/2407.18018
Autor:
Cazacu, Cristian, Fidel, Irina
When studying the weighted Hardy-Rellich inequality in $L^2$ with the full gradient replaced by the radial derivative the best constant becomes trivially larger or equal than in the first situation. Our contribution is to determine the new sharp cons
Externí odkaz:
http://arxiv.org/abs/2406.15792
Whisper is a state-of-the-art automatic speech recognition (ASR) model (Radford et al., 2022). Although Swiss German dialects are allegedly not part of Whisper's training data, preliminary experiments showed that Whisper can transcribe Swiss German q
Externí odkaz:
http://arxiv.org/abs/2404.19310
Autor:
Medeiros, Heitor Rapela, Aminbeidokhti, Masih, Pena, Fidel Guerrero, Latortue, David, Granger, Eric, Pedersoli, Marco
A common practice in deep learning involves training large neural networks on massive datasets to achieve high accuracy across various domains and tasks. While this approach works well in many application areas, it often fails drastically when proces
Externí odkaz:
http://arxiv.org/abs/2404.01492
Autor:
Blaauwbroek, Lasse, Olšák, Miroslav, Rute, Jason, Massolo, Fidel Ivan Schaposnik, Piepenbrock, Jelle, Pestun, Vasily
In proof assistants, the physical proximity between two formal mathematical concepts is a strong predictor of their mutual relevance. Furthermore, lemmas with close proximity regularly exhibit similar proof structures. We show that this locality prop
Externí odkaz:
http://arxiv.org/abs/2401.02949
Object detection models are commonly used for people counting (and localization) in many applications but require a dataset with costly bounding box annotations for training. Given the importance of privacy in people counting, these models rely more
Externí odkaz:
http://arxiv.org/abs/2311.11974
Autor:
Aminbeidokhti, Masih, Peña, Fidel A. Guerrero, Medeiros, Heitor Rapela, Dubail, Thomas, Granger, Eric, Pedersoli, Marco
Data augmentation is one of the most effective techniques for regularizing deep learning models and improving their recognition performance in a variety of tasks and domains. However, this holds for standard in-domain settings, in which the training
Externí odkaz:
http://arxiv.org/abs/2310.06670
Autor:
Medeiros, Heitor Rapela, Pena, Fidel A. Guerrero, Aminbeidokhti, Masih, Dubail, Thomas, Granger, Eric, Pedersoli, Marco
Publikováno v:
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision 2024
A powerful way to adapt a visual recognition model to a new domain is through image translation. However, common image translation approaches only focus on generating data from the same distribution as the target domain. Given a cross-modal applicati
Externí odkaz:
http://arxiv.org/abs/2310.04662