2 citations · 2 across the 5 of their papers we have counts for
6 papers
Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models
NVIDIA, :, Aaron Blakeman +198
As inference-time scaling becomes critical for enhanced reasoning capabilities, it is increasingly becoming important to build models that are efficient to infer. We introduce Nemo…
NVLM: Open Frontier-Class Multimodal LLMs
Wenliang Dai, Nayeon Lee, Boxin Wang +7
We introduce NVLM 1.0, a family of frontier-class multimodal large language models (LLMs) that achieve state-of-the-art results on vision-language tasks, rivaling the leading propr…
PHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation
Mike Ranzinger, Jon Barker, Greg Heinrich +3
Various visual foundation models have distinct strengths and weaknesses, both of which can be improved through heterogeneous multi-teacher knowledge distillation without labels, te…
Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement
Max Ehrlich, Jon Barker, Namitha Padmanabhan +4
Video compression is a central feature of the modern internet powering technologies from social media to video conferencing. While video compression continues to mature, for many c…
SDCNet: Video Prediction Using Spatially-Displaced Convolution
Fitsum A. Reda, Guilin Liu, Kevin J. Shih +5
We present an approach for high-resolution video frame prediction by conditioning on both past frames and past optical flows. Previous approaches rely on resampling past frames, gu…
Malware Detection by Eating a Whole EXE
Edward Raff, Jon Barker, Jared Sylvester +3
In this work we introduce malware detection from raw byte sequences as a fruitful research area to the larger machine learning community. Building a neural network for such a probl…