# Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

## 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

## Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis

Published:

Flowtron is an autoregressive flow-based generative network for text-to-speech synthesis with direct control over speech variation and style transfer

## 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.

## MegatronLM: Training Billion+ Parameter Language Models Using GPU Model Parallelism

Published:

We train an 8.3 billion parameter transformer language model with 8-way model parallelism and 64-way data parallelism on 512 GPUs, making it the largest transformer based language model ever trained at 24x the size of BERT and 5.6x the size of GPT-2

## Mellotron: Multispeaker expressive voice synthesis by conditioning on rhythm, pitch and global style tokens

Published:

Mellotron is a multispeaker voice synthesis model that can make a voice emote and sing without emotive or singing training data

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## Image Inpainting for Irregular Holes Using Partial Convolutions

Published in ECCV 2018, 2018

Recommended citation: Guilin Liu, Fitsum A. Reda, Kevin J. Shih, Ting-Chun Wang, Andrew Tao, Bryan Catanzaro, Image Inpainting for Irregular Holes Using Partial Convolutions, Proceedings of the European Conference on Computer Vision (ECCV) 2018. https://arxiv.org/abs/1804.07723

Published in arXiv, 2018

Recommended citation: Guilin Liu, Kevin J. Shih, Ting-Chun Wang, Fitsum A. Reda, Karan Sapra, Zhiding Yu, Andrew Tao, Bryan Catanzaro, Partial Convolution based Padding, arXiv:1811.11718, 2018. https://arxiv.org/abs/1811.11718

## 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

## WaveGlow: a Flow-based Generative Network for Speech Synthesis

Published:

WaveGlow is an invertible neural network that can generate high quality speech efficiently from mel-spectrograms.

## Future Blog Post

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

## Blog Post number 4

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## Blog Post number 3

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## Blog Post number 2

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## Blog Post number 1

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## Malware Detection by Eating a Whole EXE

Published:

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

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

Published:

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

## SDCNet: Video Prediction Using Spatially Displaced Convolution

Published:

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

## WaveGlow: a Flow-based Generative Network for Speech Synthesis

Published:

WaveGlow is an invertible neural network that can generate high quality speech efficiently from mel-spectrograms.

## Improving Semantic Segmentation via Video Propagation and Label Relaxation

Published:

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

## Image Inpainting for Irregular Holes Using Partial Convolutions

Published:

Recommended citation: Guilin Liu, Fitsum A. Reda, Kevin J. Shih, Ting-Chun Wang, Andrew Tao, Bryan Catanzaro, Image Inpainting for Irregular Holes Using Partial Convolutions, Proceedings of the European Conference on Computer Vision (ECCV) 2018. https://arxiv.org/abs/1804.07723

Published:

Recommended citation: Guilin Liu, Kevin J. Shih, Ting-Chun Wang, Fitsum A. Reda, Karan Sapra, Zhiding Yu, Andrew Tao, Bryan Catanzaro, Partial Convolution based Padding, arXiv:1811.11718, 2018. https://arxiv.org/abs/1811.11718

## MegatronLM: Training Billion+ Parameter Language Models Using GPU Model Parallelism

Published:

We train an 8.3 billion parameter transformer language model with 8-way model parallelism and 64-way data parallelism on 512 GPUs, making it the largest transformer based language model ever trained at 24x the size of BERT and 5.6x the size of GPT-2

## Unsupervised Video Interpolation Using Cycle Consistency

Published:

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

## Mellotron: Multispeaker expressive voice synthesis by conditioning on rhythm, pitch and global style tokens

Published:

Mellotron is a multispeaker voice synthesis model that can make a voice emote and sing without emotive or singing training data

## Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis

Published:

Flowtron is an autoregressive flow-based generative network for text-to-speech synthesis with direct control over speech variation and style transfer

## 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.