7 citations · 8 across the 4 of their papers we have counts for
6 papers
Large Language Continuous Diffusion Models
Zhihan Yang, Wei Guo, Jean-Marie Lemercier +14
Despite the success of discrete diffusion language models (dLMs) for fast parallel decoding, their non-smooth, high-dimensional space hinders trajectory steering for reasoning and…
Accelerating Video Diffusion via Training-Free Trajectory Routing
Mustafa Munir, Huy Vu, Shreyas Misra +7
Video diffusion is computationally expensive, as it requires executing a large model across many denoising steps. Even with step-distillation, inference remains expensive because e…
Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding
Yonggan Fu, Lexington Whalen, Abhinav Garg +23
We introduce Nemotron-Labs-Diffusion, a tri-mode language model (LM) that unifies AR, diffusion, and self-speculation decoding within a single architecture. Trained with a joint AR…
DiMS: Distilling Multiple Steps of Iterative Non-Autoregressive Transformers for Machine Translation
Sajad Norouzi, Rasa Hosseinzadeh, Felipe Perez +1
The computational benefits of iterative non-autoregressive transformers decrease as the number of decoding steps increases. As a remedy, we introduce Distill Multiple Steps (DiMS),…
Code Generation from Natural Language with Less Prior and More Monolingual Data
Sajad Norouzi, Keyi Tang, Yanshuai Cao
Training datasets for semantic parsing are typically small due to the higher expertise required for annotation than most other NLP tasks. As a result, models for this application u…
Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation
Sajad Norouzi, David J. Fleet, Mohammad Norouzi
We introduce Exemplar VAEs, a family of generative models that bridge the gap between parametric and non-parametric, exemplar based generative models. Exemplar VAE is a variant of…