Zobrazeno 1 - 10
of 9 323
pro vyhledávání: '"Crabbe, A."'
Autor:
Beau, Nathanaël, Crabbé, Benoît
We introduce a novel dataset tailored for code generation, aimed at aiding developers in common tasks. Our dataset provides examples that include a clarified intent, code snippets associated, and an average of three related unit tests. It encompasses
Externí odkaz:
http://arxiv.org/abs/2409.16819
Tabular data is one of the most ubiquitous modalities, yet the literature on tabular generative foundation models is lagging far behind its text and vision counterparts. Creating such a model is hard, due to the heterogeneous feature spaces of differ
Externí odkaz:
http://arxiv.org/abs/2406.17673
Autor:
Huynh, Nicolas, Berrevoets, Jeroen, Seedat, Nabeel, Crabbé, Jonathan, Qian, Zhaozhi, van der Schaar, Mihaela
Identification and appropriate handling of inconsistencies in data at deployment time is crucial to reliably use machine learning models. While recent data-centric methods are able to identify such inconsistencies with respect to the training set, th
Externí odkaz:
http://arxiv.org/abs/2402.17599
Large Language Models (LLMs) have shown impressive abilities in data annotation, opening the way for new approaches to solve classic NLP problems. In this paper, we show how to use LLMs to create NuNER, a compact language representation model special
Externí odkaz:
http://arxiv.org/abs/2402.15343
Fourier analysis has been an instrumental tool in the development of signal processing. This leads us to wonder whether this framework could similarly benefit generative modelling. In this paper, we explore this question through the scope of time ser
Externí odkaz:
http://arxiv.org/abs/2402.05933
Autor:
Zeni, Claudio, Pinsler, Robert, Zügner, Daniel, Fowler, Andrew, Horton, Matthew, Fu, Xiang, Shysheya, Sasha, Crabbé, Jonathan, Sun, Lixin, Smith, Jake, Nguyen, Bichlien, Schulz, Hannes, Lewis, Sarah, Huang, Chin-Wei, Lu, Ziheng, Zhou, Yichi, Yang, Han, Hao, Hongxia, Li, Jielan, Tomioka, Ryota, Xie, Tian
The design of functional materials with desired properties is essential in driving technological advances in areas like energy storage, catalysis, and carbon capture. Generative models provide a new paradigm for materials design by directly generatin
Externí odkaz:
http://arxiv.org/abs/2312.03687
Data quality is crucial for robust machine learning algorithms, with the recent interest in data-centric AI emphasizing the importance of training data characterization. However, current data characterization methods are largely focused on classifica
Externí odkaz:
http://arxiv.org/abs/2310.18970
What distinguishes robust models from non-robust ones? While for ImageNet distribution shifts it has been shown that such differences in robustness can be traced back predominantly to differences in training data, so far it is not known what that tra
Externí odkaz:
http://arxiv.org/abs/2310.13040
Autor:
Kim Thys, Matthew J. Loza, Linghua Lynn, Katleen Callewaert, Lisa Varma, Marjolein Crabbe, Liesbeth Van Wesenbeeck, Erika Van Landuyt, Sandra De Meyer, Jeroen Aerssens, Inge Verbrugge
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract We examined candidate biomarkers for efficacy outcomes in hospitalized COVID-19 patients who were treated with sirukumab, an IL-6 neutralizing antibody, in a randomized, double-blind, placebo-controlled, phase 2 trial. Between May 2020 and M
Externí odkaz:
https://doaj.org/article/d81d7c2b56ac49389a4e6701a820f8ef
Interpretability methods are valuable only if their explanations faithfully describe the explained model. In this work, we consider neural networks whose predictions are invariant under a specific symmetry group. This includes popular architectures,
Externí odkaz:
http://arxiv.org/abs/2304.06715