Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Tami, Myriam"'
Estimating treatment effects over time holds significance in various domains, including precision medicine, epidemiology, economy, and marketing. This paper introduces a unique approach to counterfactual regression over time, emphasizing long-term pr
Externí odkaz:
http://arxiv.org/abs/2406.00535
Autor:
Colombo, Pierre, Pellegrain, Victor, Boudiaf, Malik, Storchan, Victor, Tami, Myriam, Ayed, Ismail Ben, Hudelot, Celine, Piantanida, Pablo
Proprietary and closed APIs are becoming increasingly common to process natural language, and are impacting the practical applications of natural language processing, including few-shot classification. Few-shot classification involves training a mode
Externí odkaz:
http://arxiv.org/abs/2310.13998
Estimating treatment effects over time is relevant in many real-world applications, such as precision medicine, epidemiology, economy, and marketing. Many state-of-the-art methods either assume the observations of all confounders or seek to infer the
Externí odkaz:
http://arxiv.org/abs/2310.10559
Autor:
Reisach, Alexander G., Tami, Myriam, Seiler, Christof, Chambaz, Antoine, Weichwald, Sebastian
Additive Noise Models (ANMs) are a common model class for causal discovery from observational data and are often used to generate synthetic data for causal discovery benchmarking. Specifying an ANM requires choosing all parameters, including those no
Externí odkaz:
http://arxiv.org/abs/2303.18211
Autor:
Boudiaf, Malik, Bennequin, Etienne, Tami, Myriam, Toubhans, Antoine, Piantanida, Pablo, Hudelot, Céline, Ayed, Ismail Ben
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have a few labeled samples, while simultaneously detecting instances that do not belong to any known class. We explore th
Externí odkaz:
http://arxiv.org/abs/2301.08390
Autor:
Boudiaf, Malik, Bennequin, Etienne, Tami, Myriam, Hudelot, Celine, Toubhans, Antoine, Piantanida, Pablo, Ayed, Ismail Ben
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have few labeled samples, while simultaneously detecting instances that do not belong to any known class. Departing from
Externí odkaz:
http://arxiv.org/abs/2206.09236
Every day, a new method is published to tackle Few-Shot Image Classification, showing better and better performances on academic benchmarks. Nevertheless, we observe that these current benchmarks do not accurately represent the real industrial use ca
Externí odkaz:
http://arxiv.org/abs/2205.05155
The increasing complexity of Industry 4.0 systems brings new challenges regarding predictive maintenance tasks such as fault detection and diagnosis. A corresponding and realistic setting includes multi-source data streams from different modalities,
Externí odkaz:
http://arxiv.org/abs/2110.08021
Few-Shot Learning (FSL) algorithms have made substantial progress in learning novel concepts with just a handful of labelled data. To classify query instances from novel classes encountered at test-time, they only require a support set composed of a
Externí odkaz:
http://arxiv.org/abs/2105.11804
In this paper, we explore contrastive learning for few-shot classification, in which we propose to use it as an additional auxiliary training objective acting as a data-dependent regularizer to promote more general and transferable features. In parti
Externí odkaz:
http://arxiv.org/abs/2012.13831