1 citations · 1 across the 3 of their papers we have counts for
5 papers
Guided Diffusion for Trustworthy Synthetic Text Generation
Qi Pang, Hanshen Xiao, Andre Manoel +4
The rapid evolution of Large Language Models is fueled by massive datasets, yet this reliance poses severe privacy risks, as models can inadvertently memorize and even directly out…
NeMo Data Designer: An Extensible Framework for Multimodal Synthetic Data Generation
Johnny Greco, Nabin Mulepati, Andre Manoel +8
We present NeMo Data Designer (NDD), an open-source, general-purpose framework for multi-modal synthetic data generation (SDG). Designed to be intuitive to use, NDD provides a decl…
Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence
NVIDIA, :, Amala Sanjay Deshmukh +204
We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 N…
Synthetic Data Privacy Metrics
Amy Steier, Lipika Ramaswamy, Andre Manoel +1
Recent advancements in generative AI have made it possible to create synthetic datasets that can be as accurate as real-world data for training AI models, powering statistical insi…
Controllable Synthetic Clinical Note Generation with Privacy Guarantees
Tal Baumel, Andre Manoel, Daniel Jones +5
In the field of machine learning, domain-specific annotated data is an invaluable resource for training effective models. However, in the medical domain, this data often includes P…