Zobrazeno 1 - 10
of 570
pro vyhledávání: '"Kung, David"'
Autor:
Cui, Xiaodong, Zhang, Wei, Kayi, Abdullah, Liu, Mingrui, Finkler, Ulrich, Kingsbury, Brian, Saon, George, Kung, David
Large-scale distributed training of deep acoustic models plays an important role in today's high-performance automatic speech recognition (ASR). In this paper we investigate a variety of asynchronous decentralized distributed training strategies base
Externí odkaz:
http://arxiv.org/abs/2110.11199
Autor:
Puri, Ruchir, Kung, David S., Janssen, Geert, Zhang, Wei, Domeniconi, Giacomo, Zolotov, Vladimir, Dolby, Julian, Chen, Jie, Choudhury, Mihir, Decker, Lindsey, Thost, Veronika, Buratti, Luca, Pujar, Saurabh, Ramji, Shyam, Finkler, Ulrich, Malaika, Susan, Reiss, Frederick
Over the last several decades, software has been woven into the fabric of every aspect of our society. As software development surges and code infrastructure of enterprise applications ages, it is now more critical than ever to increase software deve
Externí odkaz:
http://arxiv.org/abs/2105.12655
Autor:
Finkler, Ulrich, Merler, Michele, Panda, Rameswar, Jaiswal, Mayoore S., Wu, Hui, Ramakrishnan, Kandan, Chen, Chun-Fu, Cho, Minsik, Kung, David, Feris, Rogerio, Bhattacharjee, Bishwaranjan
Neural Architecture Search (NAS) is a powerful tool to automatically design deep neural networks for many tasks, including image classification. Due to the significant computational burden of the search phase, most NAS methods have focused so far on
Externí odkaz:
http://arxiv.org/abs/2011.10608
Autor:
Panda, Rameswar, Merler, Michele, Jaiswal, Mayoore, Wu, Hui, Ramakrishnan, Kandan, Finkler, Ulrich, Chen, Chun-Fu, Cho, Minsik, Kung, David, Feris, Rogerio, Bhattacharjee, Bishwaranjan
Neural Architecture Search (NAS) is an open and challenging problem in machine learning. While NAS offers great promise, the prohibitive computational demand of most of the existing NAS methods makes it difficult to directly search the architectures
Externí odkaz:
http://arxiv.org/abs/2006.13314
Autor:
Zhang, Rui, Albrecht, Conrad, Zhang, Wei, Cui, Xiaodong, Finkler, Ulrich, Kung, David, Lu, Siyuan
Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster response management etc.. In this paper we present progress in developing a
Externí odkaz:
http://arxiv.org/abs/2005.10053
The past decade has witnessed great progress in Automatic Speech Recognition (ASR) due to advances in deep learning. The improvements in performance can be attributed to both improved models and large-scale training data. Key to training such models
Externí odkaz:
http://arxiv.org/abs/2002.10502
Autor:
Zhang, Wei, Cui, Xiaodong, Kayi, Abdullah, Liu, Mingrui, Finkler, Ulrich, Kingsbury, Brian, Saon, George, Mroueh, Youssef, Buyuktosunoglu, Alper, Das, Payel, Kung, David, Picheny, Michael
Publikováno v:
45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP'2020) Oral
Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchronous Parallel SGD (AD-PSGD) is a family of distributed learning algorithms that have been demonstrated to perform well for large-scale deep learning tasks. One drawback of (A)D-
Externí odkaz:
http://arxiv.org/abs/2002.01119
Autor:
Zhang, Wei, Cui, Xiaodong, Finkler, Ulrich, Saon, George, Kayi, Abdullah, Buyuktosunoglu, Alper, Kingsbury, Brian, Kung, David, Picheny, Michael
Publikováno v:
INTERSPEECH 2019
Modern Automatic Speech Recognition (ASR) systems rely on distributed deep learning to for quick training completion. To enable efficient distributed training, it is imperative that the training algorithms can converge with a large mini-batch size. I
Externí odkaz:
http://arxiv.org/abs/1907.05701
Autor:
Zhang, Wei, Cui, Xiaodong, Finkler, Ulrich, Kingsbury, Brian, Saon, George, Kung, David, Picheny, Michael
In this paper, we propose and investigate a variety of distributed deep learning strategies for automatic speech recognition (ASR) and evaluate them with a state-of-the-art Long short-term memory (LSTM) acoustic model on the 2000-hour Switchboard (SW
Externí odkaz:
http://arxiv.org/abs/1904.04956
As deep neural networks become more complex and input datasets grow larger, it can take days or even weeks to train a deep neural network to the desired accuracy. Therefore, distributed Deep Learning at a massive scale is a critical capability, since
Externí odkaz:
http://arxiv.org/abs/1708.02188