Matrix recovery from quantized and corrupted measurements

By Andrew S. Lan, Christoph Studer, Richard Baraniuk. 2014

34 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Razvan Pascanu
2. Dan Alistarh
3. Antonio Polino

In Computational Mechanics

1. Razvan Pascanu
2. Dan Alistarh
3. Antonio Polino

In Computer Vision and Pattern Recognition

1. Dan Alistarh
2. Razvan Pascanu
3. Antonio Polino
4. Dacheng Tao
5. Luc Van Gool
6. Yoshua Bengio
7. Trevor Darrell
8. Gaurav Menghani

Citations in the corpus

• Model compression via distillation and quantization (2018, methodology): “…idea of optimizing the locations of quantization points during the learning process, which we use in differentiable quantization, has been used previously in Lan et al. (2014); Koren & Sill (2011); Zhang et al. (2017), although in the different context of matrix completion and recommender systems.”