Fetching the paper…
Reading the bibliography…
Deep neural networks have proved hugely successful, achieving human-like performance on a variety of tasks.
On-Device Neural Net Inference with Mobile GPUs
Juhyun Lee, Nikolay Chirkov, Ekaterina Ignasheva, Yury Pisarchyk, Mogan Shieh, Fabio Riccardi, Raman Sarokin, Andrei Kulik, and Matthias Grundmann. 2019 · 1907
Earlier work this paper cites.
Roy Schwartz, Jesse Dodge, Noah A. Smith, and Oren Etzioni. 2019 · 1907
Earlier work this paper cites.
Loss Aware Post-training Quantization
Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alexander M. Bronstein, and Avi Mendelson. 2019 · 1911
Earlier work this paper cites.
Dana Pessach and Erez Shmueli. 2020 · 2001
Earlier work this paper cites.
What is the State of Neural Network Pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag. 2020 · 2003
Earlier work this paper cites.
Recognizing facial expression: machine learning and application to spontaneous behavior. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) , Vol. 2. 568–573 vol. 2
M.S. Bartlett, G. Littlewort, M. Frank, C. Lainscsek, I. Fasel, and J. Movellan. 2005 · 2005
Earlier work this paper cites.
The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
I-Cheng Yeh and Che hui Lien. 2009 · 2007
Earlier work this paper cites.
Michela Paganini. 2020 · 2009
Earlier work this paper cites.
Characterising Bias in Compressed Models
Sara Hooker, Nyalleng Moorosi, Gregory Clark, Samy Bengio, and Emily Denton. 2020 · 2010
Earlier work this paper cites.
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning
Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, Geoff Pleiss, and John P. Cunningham. 2020 · 2011
Earlier work this paper cites.
Cross-dataset facial expression recognition. In 2011 IEEE International Conference on Robotics and Automation . 5985–5990
Haibin Yan, Marcelo H. Ang, and Aun Neow Poo. 2011 · 2011
Earlier work this paper cites.
Challenges in Representation Learning: A report on three machine learning contests
Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron Courville, Mehdi Mirza, Ben Hamner, Will 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 Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Zhang Chuang, and Yoshua Bengio. 2013 · 2013
Earlier work this paper cites.
Facial age affects emotional expression decoding
Mara Folster, Ursula Hess, and Katja Werheid. 2014 · 2014
Earlier work this paper cites.
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Deep Learning Face Attributes in the Wild. In Proceedings of International Conference on Computer Vision (ICCV)
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. 2015 · 2015
Earlier work this paper cites.
Deep Face Recognition. In Proceedings of the British Machine Vision Conference (BMVC) , Xianghua Xie, Mark W. Jones, and Gary K. L. Tam (Eds.). BMVA Press, Article 41, 12 pages
Omkar M. Parkhi, Andrea Vedaldi, and Andrew Zisserman. 2015 · 2015
Earlier work this paper cites.
Song Han, Huizi Mao, and William J. Dally. 2015 · 2016
Earlier work this paper cites.
A Survey of Model Compression and Acceleration for Deep Neural Networks
Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. 2017 · 2017
Earlier work this paper cites.
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew G. Howard, Hartwig Adam, and Dmitry Kalenichenko. 2017 · 2017
Cited alongside, same era.
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2584–2593
Shan Li, Weihong Deng, and JunPing Du. 2017 · 2017
Cited alongside, same era.
Fairness Beyond Disparate Treatment & Disparate Impact
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P. Gummadi. 2017 · 2017
Cited alongside, same era.
To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta. 2017 · 2017
Cited alongside, same era.
Introducing AI Fairness 360, A Step Towards Trusted AI - IBM Research
Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy
Abhinav Agrawal and Namita Mittal. 2020 · 2020
Later among the works it cites.
An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks
Maurizio Capra, Beatrice Bussolino, Alberto Marchisio, Muhammad Shafique, Guido Masera, and Maurizio Martina. 2020 · 2020
Later among the works it cites.
Real-time facial affective computing on mobile devices
Yuanyuan Guo, Yifan Xia, Jing Wang, Hui Yu, and Rung-Ching Chen. 2020 · 2020
Later among the works it cites.
Predictive policing algorithms are racist. They need to be dismantled
Will Douglas Heaven. 2020 · 2020
Later among the works it cites.
Algorithmic Justice League protests bias in voice AI and media coverage
Khari Johnson. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019 · 2018
Cited alongside, same era.
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In FAT
Joy Buolamwini and Timnit Gebru. 2018 · 2018
Cited alongside, same era.
Amazon scraps secret AI recruiting tool that showed bias against women
Jeffrey Dastin. 2018 · 2018
Cited alongside, same era.
Google’s New Machine Learning Curriculum Aims to Stop Bias Cold
Nate Swanner. 2018 · 2018
Cited alongside, same era.
Enabling Machine Learning on Resource Constrained Devices by Source Code Generation of the Learned Models. In Computational Science – ICCS 2018 , Yong Shi, Haohuan Fu, Yingjie Tian, Valeria V. Krzhizhanovskaya, Michael Harold Lees, Jack Dongarra, and Peter M. A. Sloot (Eds.). Springer International Publishing, Cham, 682–694
Tomasz Szydlo, Joanna Sendorek, and Robert Brzoza-Woch. 2018 · 2018
Cited alongside, same era.
Fairness Definitions Explained. In Proceedings of the International Workshop on Software Fairness (Gothenburg, Sweden) (FairWare ’18) . Association for Computing Machinery, New York, NY, USA, 1–7
Sahil Verma and Julia Rubin. 2018 · 2018
Cited alongside, same era.
Apple’s ’sexist’ credit card investigated by US regulator
2019 · 2019
Cited alongside, same era.
Training behavior of sparse neural network topologies. In 2019 IEEE High Performance Extreme Computing Conference (HPEC) . IEEE, 1–6
Simon Alford, Ryan Robinett, Lauren Milechin, and Jeremy Kepner. 2019 · 2019
Cited alongside, same era.
Fast and Scalable In-Memory Deep Multitask Learning via Neural Weight Virtualization. In Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services (Toronto, Ontario, Canada) (MobiSys ’20) . Association for Computing Machinery, New York, NY, USA, 175–190
Seulki Lee and Shahriar Nirjon. 2020 · 2020
Later among the works it cites.
Machine learning and credit ratings prediction in the age of fourth industrial revolution
Jing-Ping Li, Nawazish Mirza, Birjees Rahat, and Deping Xiong. 2020 · 2020
Later among the works it cites.
Fairness in Machine Learning
Luca Oneto and Silvia Chiappa. 2020 · 2020
Later among the works it cites.
Application of deep learning methods in biological networks
Jin Shuting, Xiangxiang Zeng, Feng Xia, Wei Huang, and Xiangrong Liu. 2020 · 2020
Later among the works it cites.
Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives
Matteo Spezialetti, Giuseppe Placidi, and Silvia Rossi. 2020 · 2020
Later among the works it cites.
AI in the 2020s Must Get Greener—and Here’s How
Ameet Talwalkar. 2020 · 2020
Later among the works it cites.
Suppressing Uncertainties for Large-Scale Facial Expression Recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Kai Wang, Xiaojiang Peng, Jianfei Yang, Shijian Lu, and Yu Qiao. 2020 · 2020
Later among the works it cites.
Investigating Bias and Fairness in Facial Expression Recognition. In Computer Vision - ECCV 2020 Workshops - Glasgow, UK, August 23-28, 2020, Proceedings, Part VI . 506–523
Tian Xu, Jennifer White, Sinan Kalkan, and Hatice Gunes. 2020a · 2020
Later among the works it cites.
Investigating Bias and Fairness in Facial Expression Recognition. In Computer Vision – ECCV 2020 Workshops , Adrien Bartoli and Andrea Fusiello (Eds.). Springer International Publishing, Cham, 506–523
Tian Xu, Jennifer White, Sinan Kalkan, and Hatice Gunes. 2020b · 2020
Later among the works it cites.
Mitigating Biases in Multimodal Personality Assessment. In Proceedings of the 2020 International Conference on Multimodal Interaction (Virtual Event, Netherlands) (ICMI ’20) . Association for Computing Machinery, New York, NY, USA, 361–369
Shen Yan, Di Huang, and Mohammad Soleymani. 2020 · 2020
Later among the works it cites.
The Hitchhiker’s Guide to Bias and Fairness in Facial Affective Signal Processing: Overview and techniques
Jiaee Cheong, Sinan Kalkan, and Hatice Gunes. 2021 · 2021
Later among the works it cites.
Designing deep learning studies in cancer diagnostics
Andreas Kleppe, Ole-Johan Skrede, Sepp De Raedt, Knut Liestøl, David J Kerr, and Håvard E Danielsen. 2021 · 2021
Later among the works it cites.
Pruning and Quantization for Deep Neural Network Acceleration: A Survey
Tailin Liang, John Glossner, Lei Wang, and Shaobo Shi. 2021 · 2021
Later among the works it cites.