Zobrazeno 1 - 10
of 3 258
pro vyhledávání: '"Nozza"'
Autor:
Canetti D, Galli L, Lolatto R, Nozza S, Spagnuolo V, Muccini C, Trentacapilli B, Bruzzesi E, Ranzenigo M, Chiurlo M, Castagna A, Gianotti N
Publikováno v:
Drug Design, Development and Therapy, Vol Volume 17, Pp 3697-3706 (2023)
Diana Canetti,1 Laura Galli,1 Riccardo Lolatto,1 Silvia Nozza,2 Vincenzo Spagnuolo,1 Camilla Muccini,1 Benedetta Trentacapilli,2 Elena Bruzzesi,2 Martina Ranzenigo,2 Matteo Chiurlo,2 Antonella Castagna,1,2 Nicola Gianotti1 1Infectious Diseases Unit,
Externí odkaz:
https://doaj.org/article/c987d5c1ffe44d1c962211f6867d49d4
Autor:
Ranzenigo M, Gianotti N, Galli L, Poli A, Mastrangelo A, Bruzzesi E, Chiurlo M, Nozza S, Bossolasco S, Spagnuolo V, Mancusi D, Termini R, Carini E, Lazzarin A, Castagna A
Publikováno v:
Drug Design, Development and Therapy, Vol Volume 16, Pp 1975-1982 (2022)
Martina Ranzenigo,1,2 Nicola Gianotti,2 Laura Galli,2 Andrea Poli,2 Andrea Mastrangelo,1,2 Elena Bruzzesi,1,2 Matteo Chiurlo,1,2 Silvia Nozza,2 Simona Bossolasco,2 Vincenzo Spagnuolo,2 Daniela Mancusi,3 Roberta Termini,3 Elisabetta Carini,2 Adriano L
Externí odkaz:
https://doaj.org/article/5b17757785184543814fbef451ace17e
Publikováno v:
Journal of Pain Research, Vol Volume 13, Pp 3409-3413 (2020)
Jennifer Cogan,1 Maud André,2 Gabrielle Ariano-Lortie,2 Anna Nozza,3 Meggie Raymond,1 Antoine Rochon,1 Grisell Vargas-Shaffer4 1Department of Anesthesiology, Montreal Heart Institute, Université de Montréal, Montreal, QC H1T 1C8, Canada; 2Departme
Externí odkaz:
https://doaj.org/article/921717aa157743b3866b302fa047fd82
Autor:
Yu, Zehui, Sen, Indira, Assenmacher, Dennis, Samory, Mattia, Fröhling, Leon, Dahn, Christina, Nozza, Debora, Wagner, Claudia
Machine learning (ML)-based content moderation tools are essential to keep online spaces free from hateful communication. Yet, ML tools can only be as capable as the quality of the data they are trained on allows them. While there is increasing evide
Externí odkaz:
http://arxiv.org/abs/2405.08562
Autor:
Spagnuolo V, Galli L, Poli A, Bigoloni A, Fumagalli L, Gianotti N, Nozza S, Ferrari D, Locatelli M, Lazzarin A, Castagna A
Publikováno v:
Drug Design, Development and Therapy, Vol Volume 13, Pp 477-479 (2019)
Vincenzo Spagnuolo,1,2 Laura Galli,2 Andrea Poli,2 Alba Bigoloni,2 Luca Fumagalli,2 Nicola Gianotti,2 Silvia Nozza,2 Davide Ferrari,3 Massimo Locatelli,3 Adriano Lazzarin,2 Antonella Castagna1,2 1Vita-Salute San Raffaele University, Faculty of Medici
Externí odkaz:
https://doaj.org/article/884ef43661e243ecabd38c85c41e181d
Language Models (LMs) have been shown to inherit undesired biases that might hurt minorities and underrepresented groups if such systems were integrated into real-world applications without careful fairness auditing. This paper proposes FairBelief, a
Externí odkaz:
http://arxiv.org/abs/2402.17389
Autor:
Jürgen K Rockstroh, David Asmuth, Giuseppe Pantaleo, Bonaventura Clotet, Daniel Podzamczer, Jan van Lunzen, Keikawus Arastéh, Ronald Mitsuyasu, Barry Peters, Nozza Silvia, Darren Jolliffe, Mats Ökvist, Kim Krogsgaard, Maja A Sommerfelt
Publikováno v:
PLoS ONE, Vol 14, Iss 1, p e0210965 (2019)
BACKGROUND:Vacc-4x, a therapeutic HIV vaccine candidate has previously induced a significant reduction in viral load (VL) set-point compared to placebo upon interruption of combination anti-retroviral therapy (ART) (2007/1 study). This study, (2012/1
Externí odkaz:
https://doaj.org/article/0bf3f95c684d4befbb874ae8332339ff
Recent instruction fine-tuned models can solve multiple NLP tasks when prompted to do so, with machine translation (MT) being a prominent use case. However, current research often focuses on standard performance benchmarks, leaving compelling fairnes
Externí odkaz:
http://arxiv.org/abs/2310.12127
Recent computational approaches for combating online hate speech involve the automatic generation of counter narratives by adapting Pretrained Transformer-based Language Models (PLMs) with human-curated data. This process, however, can produce in-dom
Externí odkaz:
http://arxiv.org/abs/2309.02311
Large Language Models (LLMs) exhibit remarkable text classification capabilities, excelling in zero- and few-shot learning (ZSL and FSL) scenarios. However, since they are trained on different datasets, performance varies widely across tasks between
Externí odkaz:
http://arxiv.org/abs/2307.12973