15 citations · 20 across the 5 of their papers we have counts for
12 papers
Cascaded Text Generation with Markov Transformers
Yuntian Deng, Alexander M. Rush
The two dominant approaches to neural text generation are fully autoregressive models, using serial beam search decoding, and non-autoregressive models, using parallel decoding wit…
Residual Energy-Based Models for Text Generation
Yuntian Deng, Anton Bakhtin, Myle Ott +2
Text generation is ubiquitous in many NLP tasks, from summarization, to dialogue and machine translation. The dominant parametric approach is based on locally normalized models whi…
Residual Energy-Based Models for Text
Anton Bakhtin, Yuntian Deng, Sam Gross +3
Current large-scale auto-regressive language models display impressive fluency and can generate convincing text. In this work we start by asking the question: Can the generations o…
Neural Linguistic Steganography
Zachary M. Ziegler, Yuntian Deng, Alexander M. Rush
Whereas traditional cryptography encrypts a secret message into an unintelligible form, steganography conceals that communication is taking place by encoding a secret message into…
AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference
Thierry Tambe, En-Yu Yang, Zishen Wan +5
Conventional hardware-friendly quantization methods, such as fixed-point or integer, tend to perform poorly at very low word sizes as their shrinking dynamic ranges cannot adequate…
Real or Fake? Learning to Discriminate Machine from Human Generated Text
Anton Bakhtin, Sam Gross, Myle Ott +3
Energy-based models (EBMs), a.k.a. un-normalized models, have had recent successes in continuous spaces. However, they have not been successfully applied to model text sequences. W…