Zobrazeno 1 - 10
of 3 629
pro vyhledávání: '"TUDISCO, A."'
Autor:
Carbone, D., Spatafora, A., Calvo, D., Guerra, F., Brischetto, G. A., Cappuzzello, F., Cavallaro, M., Ferrero, M., La Via, F., Tudisco, S.
Publikováno v:
Nuclear Instruments and Methods A 1069, 169960 (2024)
First prototypes of large area, p-n junction, silicon carbide (SiC) detectors have been produced as part of an ongoing programme to develop a new particle identification wall for the focal plane detector of the MAGNEX magnetic spectrometer, in prepar
Externí odkaz:
http://arxiv.org/abs/2411.03933
We analyze various formulations of the $\infty$-Laplacian eigenvalue problem on graphs, comparing their properties and highlighting their respective advantages and limitations. First, we investigate the graph $\infty$-eigenpairs arising as limits of
Externí odkaz:
http://arxiv.org/abs/2410.19666
Autor:
Schotthöfer, Steffen, Zangrando, Emanuele, Ceruti, Gianluca, Tudisco, Francesco, Kusch, Jonas
Low-Rank Adaptation (LoRA) has become a widely used method for parameter-efficient fine-tuning of large-scale, pre-trained neural networks. However, LoRA and its extensions face several challenges, including the need for rank adaptivity, robustness,
Externí odkaz:
http://arxiv.org/abs/2410.18720
Adversarial attacks on deep neural network models have seen rapid development and are extensively used to study the stability of these networks. Among various adversarial strategies, Projected Gradient Descent (PGD) is a widely adopted method in comp
Externí odkaz:
http://arxiv.org/abs/2410.12607
Autor:
Sikdar, Satyaki, Venturini, Sara, Charpignon, Marie-Laure, Kumar, Sagar, Rinaldi, Francesco, Tudisco, Francesco, Fortunato, Santo, Majumder, Maimuna S.
Publikováno v:
Nat. Hum. Behav. 8 (2024) 1631-1634
Authors of COVID-19 papers produced during the pandemic were overwhelmingly not subject matter experts. Such a massive inflow of scholars from different expertise areas is both an asset and a potential problem. Domain-informed scientific collaboratio
Externí odkaz:
http://arxiv.org/abs/2410.01838
Using drones to perform human-related tasks can play a key role in various fields, such as defense, disaster response, agriculture, healthcare, and many others. The drone delivery packing problem (DDPP) arises in the context of logistics in response
Externí odkaz:
http://arxiv.org/abs/2406.08430
Autor:
Sittoni, Pietro, Tudisco, Francesco
Implicit-depth neural networks have grown as powerful alternatives to traditional networks in various applications in recent years. However, these models often lack guarantees of existence and uniqueness, raising stability, performance, and reproduci
Externí odkaz:
http://arxiv.org/abs/2403.00720
We propose a novel methodology to solve a key eigenvalue optimization problem which arises in the contractivity analysis of neural ODEs. When looking at contractivity properties of a one layer weight-tied neural ODE $\dot{u}(t)=\sigma(Au(t)+b)$ (with
Externí odkaz:
http://arxiv.org/abs/2402.13092
Autor:
Zangrando, Emanuele, Deidda, Piero, Brugiapaglia, Simone, Guglielmi, Nicola, Tudisco, Francesco
Recent work in deep learning has shown strong empirical and theoretical evidence of an implicit low-rank bias: weight matrices in deep networks tend to be approximately low-rank and removing relatively small singular values during training or from av
Externí odkaz:
http://arxiv.org/abs/2402.03991
Techniques based on $k$-th order Hodge Laplacian operators $L_k$ are widely used to describe the topology as well as the governing dynamics of high-order systems modeled as simplicial complexes. In all of them, it is required to solve a number of lea
Externí odkaz:
http://arxiv.org/abs/2401.15492