activity
20182022
most citedEval all, trust a few, do wrong to none: Comparing sentence generation models

32 citations · 85 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2022★ 2 cited

Black-box language model explanation by context length probing

Ondřej Cífka, Antoine Liutkus

The increasingly widespread adoption of large language models has highlighted the need for improving their explainability. We present context length probing, a novel explanation te…

cs.LG2021★ 14 cited

Relative Positional Encoding for Transformers with Linear Complexity

Antoine Liutkus, Ondřej Cífka, Shih-Lun Wu +3

Recent advances in Transformer models allow for unprecedented sequence lengths, due to linear space and time complexity. In the meantime, relative positional encoding (RPE) was pro…

cs.SD2021★ 30 cited

Self-Supervised VQ-VAE for One-Shot Music Style Transfer

Ondřej Cífka, Alexey Ozerov, Umut Şimşekli +1

Neural style transfer, allowing to apply the artistic style of one image to another, has become one of the most widely showcased computer vision applications shortly after its intr…

cs.CL2018★ 7 cited

Are BLEU and Meaning Representation in Opposition?

Ondřej Cífka, Ondřej Bojar

One of possible ways of obtaining continuous-space sentence representations is by training neural machine translation (NMT) systems. The recent attention mechanism however removes…

cs.CL2018★ 32 cited

Eval all, trust a few, do wrong to none: Comparing sentence generation models

Ondřej Cífka, Aliaksei Severyn, Enrique Alfonseca +1

In this paper, we study recent neural generative models for text generation related to variational autoencoders. Previous works have employed various techniques to control the prio…