Zobrazeno 1 - 10
of 372
pro vyhledávání: '"Park, Daniel S"'
Autor:
Kelly, Stephen, Park, Daniel S., Song, Xingyou, McIntire, Mitchell, Nashikkar, Pranav, Guha, Ritam, Banzhaf, Wolfgang, Deb, Kalyanmoy, Boddeti, Vishnu Naresh, Tan, Jie, Real, Esteban
Autonomous robots deployed in the real world will need control policies that rapidly adapt to environmental changes. To this end, we propose AutoRobotics-Zero (ARZ), a method based on AutoML-Zero that discovers zero-shot adaptable policies from scrat
Externí odkaz:
http://arxiv.org/abs/2307.16890
Autor:
Zhang, Yu, Han, Wei, Qin, James, Wang, Yongqiang, Bapna, Ankur, Chen, Zhehuai, Chen, Nanxin, Li, Bo, Axelrod, Vera, Wang, Gary, Meng, Zhong, Hu, Ke, Rosenberg, Andrew, Prabhavalkar, Rohit, Park, Daniel S., Haghani, Parisa, Riesa, Jason, Perng, Ginger, Soltau, Hagen, Strohman, Trevor, Ramabhadran, Bhuvana, Sainath, Tara, Moreno, Pedro, Chiu, Chung-Cheng, Schalkwyk, Johan, Beaufays, Françoise, Wu, Yonghui
We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages. This is achieved by pre-training the encoder of the model on a large unlabeled multilingual dataset of 12 mill
Externí odkaz:
http://arxiv.org/abs/2303.01037
Autor:
Huang, Qingqing, Park, Daniel S., Wang, Tao, Denk, Timo I., Ly, Andy, Chen, Nanxin, Zhang, Zhengdong, Zhang, Zhishuai, Yu, Jiahui, Frank, Christian, Engel, Jesse, Le, Quoc V., Chan, William, Chen, Zhifeng, Han, Wei
We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts. Two types of diffusion models, a generator model, which generates an intermediate representation conditioned on
Externí odkaz:
http://arxiv.org/abs/2302.03917
Autor:
Wang, Gary, Cubuk, Ekin D., Rosenberg, Andrew, Cheng, Shuyang, Weiss, Ron J., Ramabhadran, Bhuvana, Moreno, Pedro J., Le, Quoc V., Park, Daniel S.
Data augmentation is a ubiquitous technique used to provide robustness to automatic speech recognition (ASR) training. However, even as so much of the ASR training process has become automated and more "end-to-end", the data augmentation policy (what
Externí odkaz:
http://arxiv.org/abs/2210.10879
Autor:
Zhang, Yu, Park, Daniel S., Han, Wei, Qin, James, Gulati, Anmol, Shor, Joel, Jansen, Aren, Xu, Yuanzhong, Huang, Yanping, Wang, Shibo, Zhou, Zongwei, Li, Bo, Ma, Min, Chan, William, Yu, Jiahui, Wang, Yongqiang, Cao, Liangliang, Sim, Khe Chai, Ramabhadran, Bhuvana, Sainath, Tara N., Beaufays, Françoise, Chen, Zhifeng, Le, Quoc V., Chiu, Chung-Cheng, Pang, Ruoming, Wu, Yonghui
We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a million hours of audio. We find that the combination of pre-training, s
Externí odkaz:
http://arxiv.org/abs/2109.13226
Autor:
Feng, Xiao, Rocha, Tainá, Thammavong, Hanna T., Tulaiha, Rima, Chen, Xin, Xie, Yingying, Park, Daniel S.
Publikováno v:
In Ecological Informatics March 2024 79
Autor:
Panchapagesan, Sankaran, Park, Daniel S., Chiu, Chung-Cheng, Shangguan, Yuan, Liang, Qiao, Gruenstein, Alexander
Knowledge Distillation is an effective method of transferring knowledge from a large model to a smaller model. Distillation can be viewed as a type of model compression, and has played an important role for on-device ASR applications. In this paper,
Externí odkaz:
http://arxiv.org/abs/2011.06110
The predictions of wide Bayesian neural networks are described by a Gaussian process, known as the Neural Network Gaussian Process (NNGP). Analytic forms for NNGP kernels are known for many models, but computing the exact kernel for convolutional arc
Externí odkaz:
http://arxiv.org/abs/2011.06006
Autor:
Zhang, Yu, Qin, James, Park, Daniel S., Han, Wei, Chiu, Chung-Cheng, Pang, Ruoming, Le, Quoc V., Wu, Yonghui
We employ a combination of recent developments in semi-supervised learning for automatic speech recognition to obtain state-of-the-art results on LibriSpeech utilizing the unlabeled audio of the Libri-Light dataset. More precisely, we carry out noisy
Externí odkaz:
http://arxiv.org/abs/2010.10504
Autor:
Park, Daniel S., Zhang, Yu, Jia, Ye, Han, Wei, Chiu, Chung-Cheng, Li, Bo, Wu, Yonghui, Le, Quoc V.
Publikováno v:
Proc. Interspeech 2020, 2817-2821
Recently, a semi-supervised learning method known as "noisy student training" has been shown to improve image classification performance of deep networks significantly. Noisy student training is an iterative self-training method that leverages augmen
Externí odkaz:
http://arxiv.org/abs/2005.09629