Representation Learning
By D.A. Forsyth. 2015
61 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Luke S. Zettlemoyer
- Quoc V. Le
- Mohit Bansal
- Pieter Abbeel
- Percy Liang
- Jianfeng Gao
- Yejin Choi
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Dacheng Tao
- Trevor Darrell
- Ming-Hsuan Yang
- Jitendra Malik
- Andrea Vedaldi
- Xiaogang Wang
- Andrew Zisserman
In Representation & Self-Supervised Learning
- Alex Nichol
- Alec Radford
- Marc-André Zöller
- Harrison Edwards
- Girish Sastry
- Ilya Sutskever
- Marco F. Huber
- Jared Kaplan
Citations in the corpus
- Evaluating Large Language Models Trained on Code (2021, background): “…(Mikolov et al., 2013; Sutskever et al., 2014; Dai & Le, 2015; Peters et al., 2018; Radford et al., 2018; Devlin et al., 2018), computer vision (Van Oord et al., 2016; Menick & Kalchbrenner, 2018; Chen et al., 2020; Bao et al., 2021), audio and speech processing (Oord et al., 2016; 2018; Dhariwal…”
- Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “Another approach for automatic feature generation is representation learning (Bengio et al., 2013; Goodfellow et al., 2016).”