Update with more acknowledgements, include Lanczos

Author: noahgolmant

README.md

@@ -1,6 +1,6 @@
 # pytorch-hessian-eigenthings
 
-The `hessian-eigenthings` module provides an efficient way to compute the eigendecomposition of the Hessian for an arbitrary PyTorch model. It uses PyTorch's Hessian-vector product and stochastic power iteration with deflation to compute the top eigenvalues and eigenvectors of the Hessian.
+The `hessian-eigenthings` module provides an efficient (and scalable!) way to compute the eigendecomposition of the Hessian for an arbitrary PyTorch model. It uses PyTorch's Hessian-vector product and your choice of (a) the Lanczos method or (b) stochastic power iteration with deflation in order to compute the top eigenvalues and eigenvectors of the Hessian.
 
 ## Installation
 
@@ -29,12 +29,23 @@ eigenvals, eigenvecs = compute_hessian_eigenthings(model, dataloader,
 
 This also includes a more general power iteration with deflation implementation in `power_iter.py`.
 
+## Citing this work
+If you find this repo useful and would like to cite it in a publication, here is a BibTeX entry:
+
+ @misc{hessian-eigenthings,
+  author = {Noah Golmant, Zhewei Yao, Amir Gholami, Michael Mahoney, Joseph Gonzalez},
+  title = {{pytorch-hessian-eigentings: efficient PyTorch Hessian eigendecomposition}},
+  month = oct,
+  year = 2018,
+  version = {1.0},
+  url = {https://github.com/noahgolmant/pytorch-hessian-eigenthings}
+  }
+
+
 ## Acknowledgements
 
-This code was written in collaboration with Zhewei Yao, Amir Gholami, and Michael Mahoney in UC Berkeley's [RISELab](https://rise.cs.berkeley.edu).
+This code was written in collaboration with Zhewei Yao, Amir Gholami, Michael Mahoney, and Joseph Gonzalez in UC Berkeley's [RISELab](https://rise.cs.berkeley.edu).
 
 The deflated power iteration routine is based on code in the [HessianFlow](https://github.com/amirgholami/HessianFlow) repository recently described in the following paper: Z. Yao, A. Gholami, Q. Lei, K. Keutzer, M. Mahoney. "Hessian-based Analysis of Large Batch Training and Robustness to Adversaries", *NIPS'18* ([arXiv:1802.08241](https://arxiv.org/abs/1802.08241))
 
 Stochastic power iteration with acceleration is based on the following paper: C. De Sa, B. He, I. Mitliagkas, C. Ré, P. Xu. "Accelerated Stochastic Power Iteration", *PMLR-21* ([arXiv:1707.02670](https://arxiv.org/abs/1707.02670))
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