Distributed Estimation of Gauss - Markov Random Fields With One-Bit Quantized Data

By Jun Fang, Hongbin Li. 2010

36 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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

1. Martin Jaggi
2. Sebastian U. Stich
3. Anastasia Koloskova
4. Peter Richtárik
5. Dorina Thanou
6. Pascal Frossard
7. Effrosini Kokiopoulou
8. Ye Pu

In Computer Networks and Communications

1. Sebastian U. Stich
2. Martin Jaggi
3. Anastasia Koloskova
4. Pascal Frossard
5. Effrosini Kokiopoulou
6. Ye Pu
7. Dorina Thanou
8. Peter Richtárik

Citations in the corpus

• Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication (2019, background): “Inorderto converge, adaptive schemes (with varying compression accuracy) have been proposed (Carli et al., 2010a; Fang & Li, 2010; Li et al., 2011; Thanou et al., 2013).”
• Distributed Average Consensus With Quantization Refinement (2013, methodology): “The saturation issue of the AdaptQ can be avoided by imposing a step-size cap that would prevent a too large step-size as proposed in [18].”