Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Zunino, Andrea"'
Autor:
Rollo, Federico, Zunino, Andrea, Raiola, Gennaro, Amadio, Fabio, Ajoudani, Arash, Tsagarakis, Nikolaos
Human-robot interaction (HRI) has become a crucial enabler in houses and industries for facilitating operational flexibility. When it comes to mobile collaborative robots, this flexibility can be further increased due to the autonomous mobility and n
Externí odkaz:
http://arxiv.org/abs/2311.12992
Autor:
Rollo, Federico, Zunino, Andrea, Tsagarakis, Nikolaos, Hoffman, Enrico Mingo, Ajoudani, Arash
In today's Human-Robot Interaction (HRI) scenarios, a prevailing tendency exists to assume that the robot shall cooperate with the closest individual or that the scene involves merely a singular human actor. However, in realistic scenarios, such as s
Externí odkaz:
http://arxiv.org/abs/2310.19413
Autor:
Fichtner, Andreas, Hofstede, Coen, Gebraad, Lars, Zunino, Andrea, Zigone, Dimitri, Eisen, Olaf
Ice streams are major contributors to ice sheet mass loss and sea level rise. Effects of their dynamic behaviour are imprinted into seismic properties, such as wave speeds and anisotropy. Here we present results from the first Distributed Acoustic Se
Externí odkaz:
http://arxiv.org/abs/2307.05976
Geometric navigation is nowadays a well-established field of robotics and the research focus is shifting towards higher-level scene understanding, such as Semantic Mapping. When a robot needs to interact with its environment, it must be able to compr
Externí odkaz:
http://arxiv.org/abs/2307.01121
Publikováno v:
Geophysical Journal International, Volume 235, Issue 3, December 2023, Pages 2979-2991
The use of the probabilistic approach to solve inverse problems is becoming more popular in the geophysical community, thanks to its ability to address nonlinear forward problems and to provide uncertainty quantification. However, such strategy is of
Externí odkaz:
http://arxiv.org/abs/2303.10047
An alternative approach to ultrasound computed tomography (USCT) for medical imaging is proposed, with the intent to (i) shorten acquisition time for devices with a large number of emitters, (ii) eliminate the calibration step, and (iii) suppress ins
Externí odkaz:
http://arxiv.org/abs/2201.09509
The concept of compressing deep Convolutional Neural Networks (CNNs) is essential to use limited computation, power, and memory resources on embedded devices. However, existing methods achieve this objective at the cost of a drop in inference accurac
Externí odkaz:
http://arxiv.org/abs/2104.09191
Any search or sampling algorithm for solution of inverse problems needs guidance to be efficient. Many algorithms collect and apply information about the problem on the fly, and much improvement has been made in this way. However, as a consequence of
Externí odkaz:
http://arxiv.org/abs/2005.14398
Autor:
Zunino, Andrea, Bargal, Sarah Adel, Volpi, Riccardo, Sameki, Mehrnoosh, Zhang, Jianming, Sclaroff, Stan, Murino, Vittorio, Saenko, Kate
Conventionally, AI models are thought to trade off explainability for lower accuracy. We develop a training strategy that not only leads to a more explainable AI system for object classification, but as a consequence, suffers no perceptible accuracy
Externí odkaz:
http://arxiv.org/abs/2003.06498
Autor:
Bargal, Sarah Adel, Zunino, Andrea, Petsiuk, Vitali, Zhang, Jianming, Saenko, Kate, Murino, Vittorio, Sclaroff, Stan
We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions. It does so by making sure the model has "the right reasons" for a prediction, defined as reasons that are coherent with those
Externí odkaz:
http://arxiv.org/abs/1812.02626