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Prediction APIs offered for a fee are a fast-growing industry and an important part of machine learning as a service.
Adaptive mixtures of local experts
Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, and Geoffrey E. Hinton · 1991
Earlier work this paper cites.
Hierarchical mixtures of experts and the EM algorithm
Michael I. Jordan and Robert A. Jacobs · 1994
Earlier work this paper cites.
Robust real-time object detection
Paul Viola and Michael Jones · 2001
Earlier work this paper cites.
Fast and robust classification using asymmetric adaboost and a detector cascade
Paul A. Viola and Michael J. Jones · 2001
Earlier work this paper cites.
A parallel mixture of SVMs for very large scale problems
Ronan Collobert, Samy Bengio, and Yoshua Bengio · 2002
Earlier work this paper cites.
Hierarchical mixture of classification experts uncovers interactions between brain regions
Bangpeng Yao, Dirk B. Walther, Diane M. Beck, and Fei-Fei Li · 2009
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
Earlier work this paper cites.
A cascade ranking model for efficient ranked retrieval
Lidan Wang, Jimmy J. Lin, and Donald Metzler · 2011
Earlier work this paper cites.
An efficient EM approach to parameter learning of the mixture of gaussian processes
Yan Yang and Jinwen Ma · 2011
Earlier work this paper cites.
Twenty years of mixture of experts
Seniha Esen Yuksel, Joseph N. Wilson, and Paul D. Gader · 2012
Earlier work this paper cites.
Deep convolutional network cascade for facial point detection
Yi Sun, Xiaogang Wang, and Xiaoou Tang · 2013
Earlier work this paper cites.
VADER: A parsimonious rule-based model for sentiment analysis of social media text
Clayton J. Hutto and Eric Gilbert · 2014
Earlier work this paper cites.
Classifier cascades and trees for minimizing feature evaluation cost
Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, and Olivier Chapelle · 2014
Earlier work this paper cites.
Learning complexity-aware cascades for deep pedestrian detection
Zhaowei Cai, Mohammad J. Saberian, and Nuno Vasconcelos · 2015
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Distributed gaussian processes
Marc Peter Deisenroth and Jun Wei Ng · 2015
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Challenges in representation learning: A report on three machine learning contests
Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron C. Courville, Mehdi Mirza, Benjamin Hamner, William Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Tudor Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Zhang Chuang, and Yoshua Bengio · 2015
Earlier work this paper cites.
A convolutional neural network cascade for face detection
Haoxiang Li, Zhe Lin, Xiaohui Shen, Jonathan Brandt, and Gang Hua · 2015
Cited alongside, same era.
Librispeech: an asr corpus based on public domain audio books
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur · 2015
Cited alongside, same era.
Learning social relation traits from face images
Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaoou Tang · 2015
Cited alongside, same era.
Deep speech 2 : End-to-end speech recognition in english and mandarin
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich Elsen, Jesse H. Engel, Linxi Fan, Christopher Fougner, Awni Y. Hannun, Billy Jun, Tony Han, Patrick LeGresley, Xiangang Li, Libby Lin, Sharan Narang, Andrew Y. Ng, Sherjil Ozair, Ryan Prenger, Sheng Qian, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Chong Wang, Yi Wang, Zhiqian Wang, Bo Xiao, Yan Xie, Dani Yogatama, Jun Zhan, and Zhenyao Zhu · 2016
Cited alongside, same era.
Training deep networks for facial expression recognition with crowd-sourced label distribution
Emad Barsoum, Cha Zhang, Cristian Canton Ferrer, and Zhengyou Zhang · 2016
Speech model pre-training for end-to-end spoken language understanding
Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, and Yoshua Bengio · 2019
Later among the works it cites.
Affectnet: A database for facial expression, valence, and arousal computing in the wild
Ali Mollahosseini, Behzad Hasani, and Mohammad H. Mahoor · 2019
Later among the works it cites.
Granger-causal attentive mixtures of experts: Learning important features with neural networks
Patrick Schwab, Djordje Miladinovic, and Walter Karlen · 2019
Later among the works it cites.
https://aws.amazon.com/comprehend
Amazon Comprehend API · 2020
Closest in time.
https://ai.baidu.com/
Baidu API · 2020
Closest in time.
https://github.com/bung87/bixin
Bixin, a Chinese Sentiment Analysis tool from GitHub · 2020
Closest in time.
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Cited alongside, same era.
Google’s cloud vision API is not robust to noise
Hossein Hosseini, Baicen Xiao, and Radha Poovendran · 2017
Cited alongside, same era.
Noscope: Optimizing deep cnn-based queries over video streams at scale
Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, and Matei Zaharia · 2017
Cited alongside, same era.
Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild
Shan Li, Weihong Deng, and JunPing Du · 2017
Cited alongside, same era.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, and Jeff Dean · 2017
Cited alongside, same era.
Complexity vs. performance: empirical analysis of machine learning as a service
Yuanshun Yao, Zhujun Xiao, Bolun Wang, Bimal Viswanath, Haitao Zheng, and Ben Y. Zhao · 2017
Cited alongside, same era.
Interpreting and explaining deep neural networks for classification of audio signals
Sören Becker, Marcel Ackermann, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek · 2018
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Cited alongside, same era.
https://www.faceplusplus.com/emotion-recognition/
Face++ Emotion API · 2020
Closest in time.
https://cloud.google.com/natural-language
Google NLP API · 2020
Closest in time.
https://cloud.google.com/speech-to-text
Google Speech API · 2020
Closest in time.
https://cloud.google.com/vision
Google Vision API · 2020
Closest in time.
https://cloud.ibm.com/apidocs/speech-to-text
IBM Speech API · 2020
Closest in time.
https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision
Microsoft computer vision API · 2020
Closest in time.
https://azure.microsoft.com/en-us/services/cognitive-services/speech-to-text
Microsoft speech API · 2020
Closest in time.
https://github.com/WuJie1010/Facial-Expression-Recognition.Pytorch
Pretrained facial emotion model from GitHub · 2020
Closest in time.
https://github.com/SeanNaren/deepspeech.pytorch
Pretrained speech to text model from GitHub · 2020
Closest in time.
https://github.com/cjhutto/vaderSentiment
Vader, an English Sentiment Analysis tool from GitHub · 2020
Closest in time.