Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Giunchiglia, Eleonora"'
Deep Generative Models (DGMs) have found application in computer vision for generating adversarial examples to test the robustness of machine learning (ML) systems. Extending these adversarial techniques to tabular ML presents unique challenges due t
Externí odkaz:
http://arxiv.org/abs/2409.12642
The field of neuro-symbolic artificial intelligence (NeSy), which combines learning and reasoning, has recently experienced significant growth. There now are a wide variety of NeSy frameworks, each with its own specific language for expressing backgr
Externí odkaz:
http://arxiv.org/abs/2405.00532
Deep learning models have shown their strengths in various application domains, however, they often struggle to meet safety requirements for their outputs. In this paper, we introduce PiShield, the first package ever allowing for the integration of t
Externí odkaz:
http://arxiv.org/abs/2402.18285
Deep learning has been at the core of the autonomous driving field development, due to the neural networks' success in finding patterns in raw data and turning them into accurate predictions. Moreover, recent neuro-symbolic works have shown that inco
Externí odkaz:
http://arxiv.org/abs/2402.11362
Autor:
Stoian, Mihaela Cătălina, Dyrmishi, Salijona, Cordy, Maxime, Lukasiewicz, Thomas, Giunchiglia, Eleonora
Deep Generative Models (DGMs) have been shown to be powerful tools for generating tabular data, as they have been increasingly able to capture the complex distributions that characterize them. However, to generate realistic synthetic data, it is ofte
Externí odkaz:
http://arxiv.org/abs/2402.04823
In the recent years, machine learning has made great advancements that have been at the root of many breakthroughs in different application domains. However, it is still an open issue how make them applicable to high-stakes or safety-critical applica
Externí odkaz:
http://arxiv.org/abs/2304.03674
Autor:
Giunchiglia, Eleonora, Stoian, Mihaela Cătălina, Khan, Salman, Cuzzolin, Fabio, Lukasiewicz, Thomas
Neural networks have proven to be very powerful at computer vision tasks. However, they often exhibit unexpected behaviours, violating known requirements expressing background knowledge. This calls for models (i) able to learn from the requirements,
Externí odkaz:
http://arxiv.org/abs/2210.01597
In recent years, there has been an increasing interest in exploiting logically specified background knowledge in order to obtain neural models (i) with a better performance, (ii) able to learn from less data, and/or (iii) guaranteed to be compliant w
Externí odkaz:
http://arxiv.org/abs/2205.00523
Publikováno v:
In International Journal of Approximate Reasoning August 2024 171
Publikováno v:
J. Artif. Intell. Res. 72 (2021) 759--818
Multi-label classification (MC) is a standard machine learning problem in which a data point can be associated with a set of classes. A more challenging scenario is given by hierarchical multi-label classification (HMC) problems, in which every predi
Externí odkaz:
http://arxiv.org/abs/2103.13427