9 citations · 19 across the 11 of their papers we have counts for
4 papers · 1 filter
Generating artificial texts as substitution or complement of training data
Vincent Claveau, Antoine Chaffin, Ewa Kijak
The quality of artificially generated texts has considerably improved with the advent of transformers. The question of using these models to generate learning data for supervised l…
PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding
Antoine Chaffin, Vincent Claveau, Ewa Kijak
Large language models (LM) based on Transformers allow to generate plausible long texts. In this paper, we explore how this generation can be further controlled at decoding time to…
It Takes Two to Tango: Mixup for Deep Metric Learning
Shashanka Venkataramanan, Bill Psomas, Ewa Kijak +3
Metric learning involves learning a discriminative representation such that embeddings of similar classes are encouraged to be close, while embeddings of dissimilar classes are pus…
AlignMixup: Improving Representations By Interpolating Aligned Features
Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg +1
Mixup is a powerful data augmentation method that interpolates between two or more examples in the input or feature space and between the corresponding target labels. Many recent m…