259 citations · 978 across the 60 of their papers we have counts for
7 papers · 1 filter
A Hierarchical Model for Data-to-Text Generation
Clément Rebuffel, Laure Soulier, Geoffrey Scoutheeten +1
Transcribing structured data into natural language descriptions has emerged as a challenging task, referred to as "data-to-text". These structures generally regroup multiple elemen…
Unsupervised Adversarial Image Inpainting
Arthur Pajot, Emmanuel de Bezenac, Patrick Gallinari
We consider inpainting in an unsupervised setting where there is neither access to paired nor unpaired training data. The only available information is provided by the uncomplete o…
Optimal Unsupervised Domain Translation
Emmanuel de Bézenac, Ibrahim Ayed, Patrick Gallinari
Domain Translation is the problem of finding a meaningful correspondence between two domains. Since in a majority of settings paired supervision is not available, much work focuses…
Benchmarking Regression Methods: A comparison with CGAN
Karan Aggarwal, Matthieu Kirchmeyer, Pranjul Yadav +2
In recent years, impressive progress has been made in the design of implicit probabilistic models via Generative Adversarial Networks (GAN) and its extension, the Conditional GAN (…
Context-Aware Zero-Shot Learning for Object Recognition
Eloi Zablocki, Patrick Bordes, Benjamin Piwowarski +2
Zero-Shot Learning (ZSL) aims at classifying unlabeled objects by leveraging auxiliary knowledge, such as semantic representations. A limitation of previous approaches is that only…
Copy mechanism and tailored training for character-based data-to-text generation
Marco Roberti, Giovanni Bonetta, Rossella Cancelliere +1
In the last few years, many different methods have been focusing on using deep recurrent neural networks for natural language generation. The most widely used sequence-to-sequence…
Learning Dynamical Systems from Partial Observations
Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot +2
We consider the problem of forecasting complex, nonlinear space-time processes when observations provide only partial information of on the system's state. We propose a natural dat…