Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Bryan Catanzaro"'
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Autor:
Bryan Catanzaro
Publikováno v:
Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3.
Autor:
Guilin Liu, Aysegul Dundar, Kevin J. Shih, Ting-Chun Wang, Fitsum A. Reda, Karan Sapra, Zhiding Yu, Xiaodong Yang, Andrew Tao, Bryan Catanzaro
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Partial convolution weights convolutions with binary masks and renormalizes on valid pixels. It was originally proposed for image inpainting task because a corrupted image processed by a standard convolutional often leads to artifacts. Therefore, bin
Autor:
Steve Dai, Ben Keller, William J. Dally, Brucek Khailany, Rangharajan Venkatesan, Alicia Klinefelter, Robert M. Kirby, Saad Godil, Yanqing Zhang, Haoxing Ren, Bryan Catanzaro
Publikováno v:
IEEE Micro. 40:23-32
Recent advancements in machine learning provide an opportunity to transform chip design workflows. We review recent research applying techniques such as deep convolutional neural networks and graph-based neural networks in the areas of automatic desi
Autor:
Bryan Catanzaro, Stuart F. Oberman, Michael Siu, Robert M. Kirby, Rajarshi Roy, Saad Godil, Jonathan Raiman, Neel Kant, Ilyas Elkin
Publikováno v:
DAC
In this work, we present a reinforcement learning (RL) based approach to designing parallel prefix circuits such as adders or priority encoders that are fundamental to high-performance digital design. Unlike prior methods, our approach designs soluti
Autor:
Julie Bernauer, Prethvi Kashinkunti, Bryan Catanzaro, Matei Zaharia, Vijay Anand Korthikanti, Jared Casper, Amar Phanishayee, Dmitri Vainbrand, Patrick LeGresley, Mohammad Shoeybi, Deepak Narayanan, Mostofa Patwary
Publikováno v:
SC
Large language models have led to state-of-the-art accuracies across a range of tasks. However, training these models efficiently is challenging for two reasons: a) GPU memory capacity is limited, making it impossible to fit large models on even a mu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f27b8feb99fc20d65d879c34b6e517d
http://arxiv.org/abs/2104.04473
http://arxiv.org/abs/2104.04473
Autor:
Devendra Singh Sachan, Bryan Catanzaro, William L. Hamilton, Mostofa Patwary, Neel Kant, Mohammad Shoeybi, Wei Ping
Publikováno v:
ACL/IJCNLP (1)
Recent work on training neural retrievers for open-domain question answering (OpenQA) has employed both supervised and unsupervised approaches. However, it remains unclear how unsupervised and supervised methods can be used most effectively for neura
Publikováno v:
CVPR
Conditional image synthesis for generating photorealistic images serves various applications for content editing to content generation. Previous conditional image synthesis algorithms mostly rely on semantic maps, and often fail in complex environmen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6345d3c948e06e496d88be15e4a4eb55
http://arxiv.org/abs/2004.10289
http://arxiv.org/abs/2004.10289
Autor:
Mostofa Patwary, Bryan Catanzaro, Pascale Fung, Peng Xu, Mohammad Shoeybi, Raul Puri, Animashree Anandkumar
Publikováno v:
EMNLP (1)
Existing pre-trained large language models have shown unparalleled generative capabilities. However, they are not controllable. In this paper, we propose MEGATRON-CNTRL, a novel framework that uses large-scale language models and adds control to text