Zobrazeno 1 - 10
of 189
pro vyhledávání: '"Varoquaux, Gael P."'
Autor:
Van Calster, Ben, Collins, Gary S., Vickers, Andrew J., Wynants, Laure, Kerr, Kathleen F., Barreñada, Lasai, Varoquaux, Gael, Singh, Karandeep, Moons, Karel G. M., Hernandez-boussard, Tina, Timmerman, Dirk, Mclernon, David J., Van Smeden, Maarten, Steyerberg, Ewout W.
A myriad of measures to illustrate performance of predictive artificial intelligence (AI) models have been proposed in the literature. Selecting appropriate performance measures is essential for predictive AI models that are developed to be used in m
Externí odkaz:
http://arxiv.org/abs/2412.10288
Autor:
Bengio, Yoshua, Mindermann, Sören, Privitera, Daniel, Besiroglu, Tamay, Bommasani, Rishi, Casper, Stephen, Choi, Yejin, Goldfarb, Danielle, Heidari, Hoda, Khalatbari, Leila, Longpre, Shayne, Mavroudis, Vasilios, Mazeika, Mantas, Ng, Kwan Yee, Okolo, Chinasa T., Raji, Deborah, Skeadas, Theodora, Tramèr, Florian, Adekanmbi, Bayo, Christiano, Paul, Dalrymple, David, Dietterich, Thomas G., Felten, Edward, Fung, Pascale, Gourinchas, Pierre-Olivier, Jennings, Nick, Krause, Andreas, Liang, Percy, Ludermir, Teresa, Marda, Vidushi, Margetts, Helen, McDermid, John A., Narayanan, Arvind, Nelson, Alondra, Oh, Alice, Ramchurn, Gopal, Russell, Stuart, Schaake, Marietje, Song, Dawn, Soto, Alvaro, Tiedrich, Lee, Varoquaux, Gaël, Yao, Andrew, Zhang, Ya-Qin
This is the interim publication of the first International Scientific Report on the Safety of Advanced AI. The report synthesises the scientific understanding of general-purpose AI -- AI that can perform a wide variety of tasks -- with a focus on und
Externí odkaz:
http://arxiv.org/abs/2412.05282
When dealing with right-censored data, where some outcomes are missing due to a limited observation period, survival analysis -- known as time-to-event analysis -- focuses on predicting the time until an event of interest occurs. Multiple classes of
Externí odkaz:
http://arxiv.org/abs/2410.16765
Autor:
Christodoulou, Evangelia, Reinke, Annika, Houhou, Rola, Kalinowski, Piotr, Erkan, Selen, Sudre, Carole H., Burgos, Ninon, Boutaj, Sofiène, Loizillon, Sophie, Solal, Maëlys, Rieke, Nicola, Cheplygina, Veronika, Antonelli, Michela, Mayer, Leon D., Tizabi, Minu D., Cardoso, M. Jorge, Simpson, Amber, Jäger, Paul F., Kopp-Schneider, Annette, Varoquaux, Gaël, Colliot, Olivier, Maier-Hein, Lena
Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently, broad conclusions are simply derived from mean performance values. I
Externí odkaz:
http://arxiv.org/abs/2409.17763
With the growing attention and investment in recent AI approaches such as large language models, the narrative that the larger the AI system the more valuable, powerful and interesting it is is increasingly seen as common sense. But what is this assu
Externí odkaz:
http://arxiv.org/abs/2409.14160
Autor:
Chen, Lihu, Varoquaux, Gaël
Large Language Models (LLMs) have made significant progress in advancing artificial general intelligence (AGI), leading to the development of increasingly large models such as GPT-4 and LLaMA-405B. However, scaling up model sizes results in exponenti
Externí odkaz:
http://arxiv.org/abs/2409.06857
Autor:
Morvan, Marine Le, Varoquaux, Gaël
Missing values are prevalent across various fields, posing challenges for training and deploying predictive models. In this context, imputation is a common practice, driven by the hope that accurate imputations will enhance predictions. However, rece
Externí odkaz:
http://arxiv.org/abs/2407.19804
When data are right-censored, i.e. some outcomes are missing due to a limited period of observation, survival analysis can compute the "time to event". Multiple classes of outcomes lead to a classification variant: predicting the most likely event, k
Externí odkaz:
http://arxiv.org/abs/2406.14085
Pretrained deep-learning models are the go-to solution for images or text. However, for tabular data the standard is still to train tree-based models. Indeed, transfer learning on tables hits the challenge of data integration: finding correspondences
Externí odkaz:
http://arxiv.org/abs/2402.16785
We present an in-depth analysis of data discovery in data lakes, focusing on table augmentation for given machine learning tasks. We analyze alternative methods used in the three main steps: retrieving joinable tables, merging information, and predic
Externí odkaz:
http://arxiv.org/abs/2402.06282