Quasi-Newton Methods for Deep Learning: Forget the Past, Just Sample

By Albert S. Berahas, Majid Jahani, Martin Takác. 2019

47 citations recorded by the source corpus.

Subfields: Optimization & Training

How researchers see this paper

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

In Optimization & Training

1. Simon Lacoste-Julien
2. Issam Hadj Laradji
3. Nicolas Loizou
4. Sharan Vaswani
5. Peter Richtárik
6. Mark W. Schmidt
7. Majid Jahani
8. Albert S. Berahas

Citations in the corpus

• Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation (2019, methodology): “Next, we study stochastic Quasi-Newton meth-ods (Schraudolph et al., 2007; Sunehag et al., 2009; Zhou et al., 2017; Liu et al., 2018; Berahas et al., 2019; Gao and Goldfarb, 2019) in the interpolation setting (Section 5).”
• Scaling Up Quasi-newton Algorithms: Communication Efficient Distributed SR1 (2019, methodology): “2 As proposed in [4], at every iteration condition (3.”