Zobrazeno 1 - 10
of 316
pro vyhledávání: '"Morucci, P."'
Autor:
Quertier, Tony, Marais, Benjamin, Barrué, Grégoire, Morucci, Stéphane, Azé, Sévan, Salladin, Sébastien
Malware is a fast-growing threat to the modern computing world and existing lines of defense are not efficient enough to address this issue. This is mainly due to the fact that many prevention solutions rely on signature-based detection methods that
Externí odkaz:
http://arxiv.org/abs/2408.02313
Autor:
Parikh, Harsh, Morucci, Marco, Orlandi, Vittorio, Roy, Sudeepa, Rudin, Cynthia, Volfovsky, Alexander
Experimental and observational studies often lack validity due to untestable assumptions. We propose a double machine learning approach to combine experimental and observational studies, allowing practitioners to test for assumption violations and es
Externí odkaz:
http://arxiv.org/abs/2307.01449
The successful analysis of argumentative techniques from user-generated text is central to many downstream tasks such as political and market analysis. Recent argument mining tools use state-of-the-art deep learning methods to extract and annotate ar
Externí odkaz:
http://arxiv.org/abs/2307.01401
We introduce Matched Machine Learning, a framework that combines the flexibility of machine learning black boxes with the interpretability of matching, a longstanding tool in observational causal inference. Interpretability is paramount in many high-
Externí odkaz:
http://arxiv.org/abs/2304.01316
Cybercrime is one of the major digital threats of this century. In particular, ransomware attacks have significantly increased, resulting in global damage costs of tens of billion dollars. In this paper, we train and test different Machine Learning a
Externí odkaz:
http://arxiv.org/abs/2207.02108
Publikováno v:
eLife, Vol 12 (2024)
Perceptual systems heavily rely on prior knowledge and predictions to make sense of the environment. Predictions can originate from multiple sources of information, including contextual short-term priors, based on isolated temporal situations, and co
Externí odkaz:
https://doaj.org/article/dbd94423aa39420690f8f1624a0c5175
In addition to signature-based and heuristics-based detection techniques, machine learning (ML) is widely used to generalize to new, never-before-seen malicious software (malware). However, it has been demonstrated that ML models can be fooled by tri
Externí odkaz:
http://arxiv.org/abs/2203.12980
Measurement bridges theory and empirics. Without measures that appropriately capture theoretical concepts, description will fail to represent reality and true causal inference will be impossible. Yet, the social sciences traffic in complex concepts a
Externí odkaz:
http://arxiv.org/abs/2111.11979
Autor:
Gupta, Neha R., Orlandi, Vittorio, Chang, Chia-Rui, Wang, Tianyu, Morucci, Marco, Dey, Pritam, Howell, Thomas J., Sun, Xian, Ghosal, Angikar, Roy, Sudeepa, Rudin, Cynthia, Volfovsky, Alexander
dame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates. This package implements the Dynamic Almost Matching Exactly (DAME) and Fast Large-Scale Almost Matching Exactly (FLA
Externí odkaz:
http://arxiv.org/abs/2101.01867
Autor:
Angelica Lenzi, Barbara Saccani, Marco Di Gregorio, Francesco Rossini, Alessio Sollima, Alice Mulè, Federica Morucci, Silvia Amadasi, Benedetta Fumarola, Paola Antonia Lanza, Silvia Lorenzotti, Evelyn Van Hauwermeiren, Elisa Cavalleri, Roberto Marzollo, Alberto Matteelli, Liana Signorini, Francesco Maria Risso
Publikováno v:
Antibiotics, Vol 13, Iss 7, p 667 (2024)
Central nervous system infections are among the most severe infectious conditions in the neonatal period and are still burdened by significant mortality, especially in preterm infants and those with a low birth weight or other comorbidities. In this
Externí odkaz:
https://doaj.org/article/bcb8ca0c976048cbb60496ec6da4ca2b