Zobrazeno 1 - 10
of 2 267
pro vyhledávání: '"A. Varoquaux"'
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
Large Language Models (LLMs), including ChatGPT and LLaMA, are susceptible to generating hallucinated answers in a confident tone. While efforts to elicit and calibrate confidence scores have proven useful, recent findings show that controlling uncer
Externí odkaz:
http://arxiv.org/abs/2402.04957
Phrase representations play an important role in data science and natural language processing, benefiting various tasks like Entity Alignment, Record Linkage, Fuzzy Joins, and Paraphrase Classification. The current state-of-the-art method involves fi
Externí odkaz:
http://arxiv.org/abs/2401.10407
There are increasingly efficient data processing pipelines that work on vectors of numbers, for instance most machine learning models, or vector databases for fast similarity search. These require converting the data to numbers. While this conversion
Externí odkaz:
http://arxiv.org/abs/2312.09634