NVIDIA ADLR

NVIDIA ADLR

Archive Layout with Content

A variety of common markup showing how the theme styles them.

Header one

Header two

Header three

Header four

Header five
Header six

Blockquotes

Single line blockquote:

Quotes are cool.

Tables

EntryItem 
John Doe2016Description of the item in the list
Jane Doe2019Description of the item in the list
Doe Doe2022Description of the item in the list
Header1Header2Header3
cell1cell2cell3
cell4cell5cell6
cell1cell2cell3
cell4cell5cell6
Foot1Foot2Foot3

Definition Lists

Definition List Title
Definition list division.
Startup
A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model.
#dowork
Coined by Rob Dyrdek and his personal body guard Christopher “Big Black” Boykins, “Do Work” works as a self motivator, to motivating your friends.
Do It Live
I’ll let Bill O’Reilly explain this one.

Unordered Lists (Nested)

Ordered List (Nested)

  1. List item one
    1. List item one
      1. List item one
      2. List item two
      3. List item three
      4. List item four
    2. List item two
    3. List item three
    4. List item four
  2. List item two
  3. List item three
  4. List item four

Buttons

Make any link standout more when applying the .btn class.

Notices

Watch out! You can also add notices by appending {: .notice} to a paragraph.

HTML Tags

Address Tag

1 Infinite Loop
Cupertino, CA 95014
United States

This is an example of a link.

Abbreviation Tag

The abbreviation CSS stands for “Cascading Style Sheets”.

Cite Tag

“Code is poetry.” —Automattic

Code Tag

You will learn later on in these tests that word-wrap: break-word; will be your best friend.

Strike Tag

This tag will let you strikeout text.

Emphasize Tag

The emphasize tag should italicize text.

Insert Tag

This tag should denote inserted text.

Keyboard Tag

This scarcely known tag emulates keyboard text, which is usually styled like the <code> tag.

Preformatted Tag

This tag styles large blocks of code.

.post-title {
  margin: 0 0 5px;
  font-weight: bold;
  font-size: 38px;
  line-height: 1.2;
  and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}

Quote Tag

Developers, developers, developers… –Steve Ballmer

Strong Tag

This tag shows bold text.

Subscript Tag

Getting our science styling on with H2O, which should push the “2” down.

Superscript Tag

Still sticking with science and Isaac Newton’s E = MC2, which should lift the 2 up.

Variable Tag

This allows you to denote variables.

SDCNet: Video Prediction Using Spatially Displaced Convolution

Published in European Conference on Computer Vision (ECCV), 2018

SDCNet is a 3D convolutional neural network proposed for frame prediction. The model takes as input a sequence of past frames and their inter-frame optical flows and generates a per-pixel kernel and motion vector. A future frame is then synthesised by sampling past frames guided by the motion vectors and weighted by the learned kernels.

Recommended citation: Fitsum A. Reda, Guilin Liu, Kevin J. Shih, Robert Kirby, Jon Barker, David Tarjan, Andrew Tao, Bryan Catanzaro, SDCNet: Video Prediction Using Spatially Displaced Convolution. ECCV 2018. https://arxiv.org/abs/1811.00684

BigVGAN: A Universal Neural Vocoder with Large-Scale Training

Published:

we present BigVGAN, a universal neural vocoder. It’s trained only on speech data but shows extraordinary zero-shot generalization ability for non-speech vocalizations (laughter, applaud), singing voices, music, instrumental audio that are even recorded in varied noisy environment!

Speech Denoising in the Waveform Domain with Self-Attention

Published:

We present CleanUNet, a speech denoising model on the raw waveform. It is based on an encoder-decoder architecture combined with several self-attention blocks to refine its bottleneck representations, which is crucial to obtain good results. It outperforms the state-of-the-art models in terms of denoised speech quality from various objective and subjective evaluation metrics.

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Published in 2018 High Performance Machine Learning Workshop, 2018

This paper shows how to do large scale distributed, large batch, mixed precision training of language models with investigations into the successes and limitations of large batch training on publicly available language datasets.

Recommended citation: Raul Puri, Robert Kirby, Nikolai Yakovenko, Bryan Catanzaro, Large Scale Language Modeling: Converging on 40GB of Text in Four Hours. arXiv. 2018. https://arxiv.org/abs/1808.01371

Malware Detection by Eating a Whole EXE

Published in 2018 AAAI Workshop on AI for Cyber Security, 2018

This paper shows how to do whole binary classification for malware detection with a convolutional neural network. Done in collaboration with researchers at the University of Maryland.

Recommended citation: Edward Raff, Jon Barker, Jared Sylvester, Robert Brandon, Bryan Catanzaro, Charles Nicholas, Malware Detection by Eating a Whole EXE. arXiv. 2017. http://arxiv.org/abs/1710.09435

MegatronLM’s Supercharged V1.0

Published:

We release version 1.0 of Megatron which makes the training of large NLP models even faster and sustains 62.4 teraFLOPs in the end-to-end training that is 48% of the theoretical peak FLOPS for a single GPU in a DGX2-H server.

One TTS Alignment to Rule Them All

Published:

We present an unsupervised alignment learning framework that learns speech-text alignments online in text to speech models. We showcase this alignment learning framework can be applied to any TTS model removing the dependency of TTS systems on external aligners. It also enhances the speech quality as evaluated by human evaluators.

Improving Semantic Segmentation via Video Propagation and Label Relaxation

Published in arXiv, 2018

This paper shows how to scale up training sets for semantic segmentation by using video prediction-based data synthesis method. Our proposed joint propagation strategy and boundary relaxation technique can alleviate the label noise in the synthesized samples and lead to state-of-the-art performance on three benchmark datasets Cityscapes, CamVid and KITTI.

Recommended citation: Yi Zhu, Karan Sapra, Fitsum A. Reda, Kevin J. Shih, Shawn Newsam, Andrew Tao and Bryan Catanzaro, Improving Semantic Segmentation via Video Propagation and Label Relaxation, arXiv:1812.01593, 2018. https://arxiv.org/abs/1812.01593

Unsupervised Video Interpolation Using Cycle Consistency

Published in International Conference on Computer Vision (ICCV), 2019

We propose unsupervised techniques to synthesize high frame rate videos directly from low frame rate videos using cycle consistency. We also introduce a pseudo-supervised loss term that enforces the interpolated frames to be consistent with predictions of a pre-trained interpolation model. The pseudo-supervised loss term, used together with cycle consistency, can effectively adapt a pre-trained model to a new target domain. We show results that significantly reduce the domain gap problem in video frame interpolation.

Recommended citation: Fitsum A. Reda, Deqing Sun, Aysegul Dundar, Mohammad Shoeybi, Guilin Liu, Kevin J. Shih, Andrew Tao, Jan Kautz, Bryan Catanzaro, "Unsupervised Video Interpolation Using Cycle Consistency". In ICCV 2019. https://arxiv.org/abs/1906.05928

View Generalization for Single Image Textured 3D Models

Published in CVPR 2021, 2021

Recommended citation: Anand Bhattad, Aysegul Dundar, Guilin Liu, Andrew Tao, Bryan Catanzaro, View Generalization for Single Image Textured 3D Models, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR) 2021.