DataPerf: Benchmarks for Data-Centric AI Development

1 code implementation • NeurIPS 2023 • Mark Mazumder, Colby Banbury, Xiaozhe Yao, Bojan Karlaš, William Gaviria Rojas, Sudnya Diamos, Greg Diamos, Lynn He, Alicia Parrish, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Douwe Kiela, David Jurado, David Kanter, Rafael Mosquera, Juan Ciro, Lora Aroyo, Bilge Acun, Lingjiao Chen, Mehul Smriti Raje, Max Bartolo, Sabri Eyuboglu, Amirata Ghorbani, Emmett Goodman, Oana Inel, Tariq Kane, Christine R. Kirkpatrick, Tzu-Sheng Kuo, Jonas Mueller, Tristan Thrush, Joaquin Vanschoren, Margaret Warren, Adina Williams, Serena Yeung, Newsha Ardalani, Praveen Paritosh, Lilith Bat-Leah, Ce Zhang, James Zou, Carole-Jean Wu, Cody Coleman, Andrew Ng, Peter Mattson, Vijay Janapa Reddi

Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems.

The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage

no code implementations • 17 Nov 2021 • Daniel Galvez, Greg Diamos, Juan Ciro, Juan Felipe Cerón, Keith Achorn, Anjali Gopi, David Kanter, Maximilian Lam, Mark Mazumder, Vijay Janapa Reddi

The People's Speech is a free-to-download 30, 000-hour and growing supervised conversational English speech recognition dataset licensed for academic and commercial usage under CC-BY-SA (with a CC-BY subset).

speech-recognition Speech Recognition

Data Engineering for Everyone

no code implementations • 23 Feb 2021 • Vijay Janapa Reddi, Greg Diamos, Pete Warden, Peter Mattson, David Kanter

This article shows that open-source data sets are the rocket fuel for research and innovation at even some of the largest AI organizations.

BIG-bench Machine Learning

MLPerf Inference Benchmark

4 code implementations • 6 Nov 2019 • Vijay Janapa Reddi, Christine Cheng, David Kanter, Peter Mattson, Guenther Schmuelling, Carole-Jean Wu, Brian Anderson, Maximilien Breughe, Mark Charlebois, William Chou, Ramesh Chukka, Cody Coleman, Sam Davis, Pan Deng, Greg Diamos, Jared Duke, Dave Fick, J. Scott Gardner, Itay Hubara, Sachin Idgunji, Thomas B. Jablin, Jeff Jiao, Tom St. John, Pankaj Kanwar, David Lee, Jeffery Liao, Anton Lokhmotov, Francisco Massa, Peng Meng, Paulius Micikevicius, Colin Osborne, Gennady Pekhimenko, Arun Tejusve Raghunath Rajan, Dilip Sequeira, Ashish Sirasao, Fei Sun, Hanlin Tang, Michael Thomson, Frank Wei, Ephrem Wu, Lingjie Xu, Koichi Yamada, Bing Yu, George Yuan, Aaron Zhong, Peizhao Zhang, Yuchen Zhou

Machine-learning (ML) hardware and software system demand is burgeoning.

Benchmarking

MLPerf Training Benchmark

2 code implementations • 2 Oct 2019 • Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim Hazelwood, Andrew Hock, Xinyuan Huang, Atsushi Ike, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Guokai Ma, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Tsuguchika Tabaru, Carole-Jean Wu, Lingjie Xu, Masafumi Yamazaki, Cliff Young, Matei Zaharia

Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML.

Benchmarking BIG-bench Machine Learning

Beyond Human-Level Accuracy: Computational Challenges in Deep Learning

1 code implementation • 3 Sep 2019 • Joel Hestness, Newsha Ardalani, Greg Diamos

However, recent prior work shows that as dataset sizes grow, DL model accuracy and model size grow predictably.

EPNAS: Efficient Progressive Neural Architecture Search

no code implementations • 7 Jul 2019 • Yanqi Zhou, Peng Wang, Sercan Arik, Haonan Yu, Syed Zawad, Feng Yan, Greg Diamos

In this paper, we propose Efficient Progressive Neural Architecture Search (EPNAS), a neural architecture search (NAS) that efficiently handles large search space through a novel progressive search policy with performance prediction based on REINFORCE~\cite{Williams. 1992. PG}.

Neural Architecture Search

HybridNet: A Hybrid Neural Architecture to Speed-up Autoregressive Models

no code implementations • ICLR 2018 • Yanqi Zhou, Wei Ping, Sercan Arik, Kainan Peng, Greg Diamos

This paper introduces HybridNet, a hybrid neural network to speed-up autoregressive models for raw audio waveform generation.

Speech Synthesis

Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

36 code implementations • 8 Dec 2015 • Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich Elsen, Jesse Engel, Linxi Fan, Christopher Fougner, Tony Han, Awni Hannun, Billy Jun, Patrick LeGresley, Libby Lin, Sharan Narang, Andrew Ng, Sherjil Ozair, Ryan Prenger, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Yi Wang, Zhiqian Wang, Chong Wang, Bo Xiao, Dani Yogatama, Jun Zhan, Zhenyao Zhu

We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages.

Accented Speech Recognition Noisy Speech Recognition

Deep Speech: Scaling up end-to-end speech recognition

24 code implementations • 17 Dec 2014 • Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, Andrew Y. Ng

We present a state-of-the-art speech recognition system developed using end-to-end deep learning.

Accented Speech Recognition